{ "_meta": { "document": "AI Market Reference Architecture", "schemaVersion": "2026.05.02", "generatedAt": "2026-05-29T18:31:31.740Z", "canonicalUrl": "https://brianletort.ai/industry/architecture", "markdownUrl": "https://brianletort.ai/industry/architecture/llm.md", "taxonomyUrl": "https://brianletort.ai/industry/architecture/taxonomy.yaml", "lastUpdated": "2026-05-14" }, "categories": [ { "id": "ai_infrastructure", "name": "Layer 1 — AI Infrastructure", "short_name": "L1 Infrastructure", "definition": "Physical and cloud substrate for AI: GPUs, accelerators, networking, datacenters, power, storage, and managed compute capacity.\n", "boundary_rule": "Place a vendor here when its primary role is supplying scarce compute, hardware, cloud capacity, or the physical systems that make AI workloads possible.\n", "buyer_question": "Where does the compute, power, and hardware capacity come from?\n", "control_points": [ "compute", "power", "networking", "cloud_capacity" ], "responsibility_boundary": "Provider owns capacity, availability, hardware lifecycle, and physical resilience. Customer owns workload placement, demand forecasting, and utilization risk.\n", "leader_criteria": [ "Disclosed AI capex, accelerator share, or AI infrastructure revenue.", "Demonstrated access to frontier accelerators or custom silicon.", "Enterprise-grade availability, regional reach, and procurement path." ], "visual_color_token": "cyan-400" }, { "id": "model_portfolio", "name": "Layer 2 — Model Portfolio", "short_name": "L2 Models", "definition": "Foundation, frontier, open-weight, specialist, and routed model portfolios that provide the intelligence primitives consumed by higher layers.\n", "boundary_rule": "Place a vendor here when model capability, model choice, model routing, or model governance is the primary product decision.\n", "buyer_question": "Which models should we use, for which workloads, at what cost and risk?\n", "control_points": [ "weights", "benchmarks", "token_pricing", "model_policy", "routing" ], "responsibility_boundary": "Model provider owns training, model behavior, release cadence, and safety defaults. Customer owns model selection, workload routing, evals, and fallback policy.\n", "leader_criteria": [ "Public frontier benchmark position or strong open-weight ecosystem.", "Competitive cost, latency, context, modality, and reliability envelope.", "Clear enterprise controls, model cards, and deployment posture." ], "visual_color_token": "orange-400" }, { "id": "data_management", "name": "Layer 3 — Data Management", "short_name": "L3 Data Mgmt", "definition": "Raw systems of record, warehouses, lakehouses, operational databases, and document stores that hold enterprise data before it is shaped for AI use.\n", "boundary_rule": "Place a vendor here when the product primarily stores, governs, or manages source data that still needs processing, wrapping, indexing, or context compilation before an AI system can consume it directly.\n", "buyer_question": "Who owns the system of record, and how do we keep AI from being trapped inside SaaS silos?\n", "control_points": [ "source_data", "governance", "lineage", "access_control" ], "responsibility_boundary": "Data platform owns storage, lineage, access control, and governance surfaces. Customer owns data quality, semantic modeling, and agent-access policy.\n", "leader_criteria": [ "Enterprise data gravity and governed access.", "Strong integration with warehouses, lakehouses, documents, and systems of record.", "Clear path to expose data safely to AI-ready layers and agents." ], "visual_color_token": "violet-400" }, { "id": "ai_ready_data", "name": "Layer 4 — AI-Ready Data", "short_name": "L4 AI Data", "definition": "Processed, indexed, embedded, retrievable, policy-wrapped, and API-exposed data that can be consumed directly by models and agents.\n", "boundary_rule": "Place a vendor here when the product turns raw enterprise data into retrieval, context, embeddings, vector search, API access, or model-ready memory.\n", "buyer_question": "How do we make our data usable by AI without surrendering it to every SaaS vendor?\n", "control_points": [ "embeddings", "vector_index", "retrieval", "api_gateway", "context_layer" ], "responsibility_boundary": "AI-ready data provider owns retrieval quality, indexing, wrappers, and access surfaces. Customer owns source-data truth, freshness, permissions, and which agents may consume which context.\n", "leader_criteria": [ "Strong vector, embedding, retrieval, API, or context-management surface.", "Evidence of production-scale freshness, permissions, and observability.", "Ability to sit between SaaS-owned data and agent-owned execution." ], "visual_color_token": "emerald-400" }, { "id": "developer_tools", "name": "Layer 5 — Developer Tools", "short_name": "L5 Dev Tools", "definition": "Build tools for humans and agents creating software: IDE copilots, coding agents, CLI agents, context management, orchestration, evals, and tool harnesses.\n", "boundary_rule": "Place a vendor here when the primary workflow is building, modifying, testing, or operating software with AI assistance.\n", "buyer_question": "Which tools turn developers into agent supervisors and software editors?\n", "control_points": [ "developer_workflow", "context_management", "orchestration", "tool_harness", "evals" ], "responsibility_boundary": "Tool provider owns coding UX, context packaging, model/tool invocation, and review affordances. Customer owns repository permissions, acceptance criteria, and production release governance.\n", "leader_criteria": [ "High developer adoption or paid seat growth.", "Strong context management, tool execution, and review loop.", "Evidence that the tool changes shipped software, not only chat usage." ], "visual_color_token": "amber-400" }, { "id": "agent_tools", "name": "Layer 6 — Agent Tools", "short_name": "L6 Agents", "definition": "Agent runtimes and operational tools that run scheduled, triggered, multi-agent, tool-using, and exception-escalating work.\n", "boundary_rule": "Place a product here when the defining behavior is autonomous or semi-autonomous execution: agents talking to agents, invoking tools, maintaining memory, and paging humans on exception.\n", "buyer_question": "Which agent systems can we safely let act on our behalf?\n", "control_points": [ "tools", "memory", "permissions", "task_execution", "human_review" ], "responsibility_boundary": "Agent tool owns execution loop, tool-use guardrails, memory, logging, and escalation mechanics. Customer owns delegated authority, permissions, rollback, and exception policy.\n", "leader_criteria": [ "Demonstrated scheduled, triggered, or long-horizon tool use.", "Human-in-the-loop controls and auditability.", "Production integration with owned data, tools, and operational systems." ], "visual_color_token": "rose-400" }, { "id": "commercial_tools", "name": "Layer 7 — Commercial Tools", "short_name": "L7 Commercial", "definition": "Finished point solutions and vertical AI tools: video, voice, legal, sales, research, support, writing, and other specialist business outcomes.\n", "boundary_rule": "Place a vendor here when buyers purchase a packaged business outcome, not a reusable model, data layer, developer tool, or agent runtime.\n", "buyer_question": "Which specialist tools survive when agents can work directly over our own data?\n", "control_points": [ "workflow_distribution", "proprietary_context", "seats", "vertical_expertise" ], "responsibility_boundary": "Tool provider owns workflow UX, packaged domain behavior, and product controls. Customer owns data portability, agent access, workflow redesign, and whether point tools become durable or get absorbed by agent systems.\n", "leader_criteria": [ "Strong vertical adoption, retention, or enterprise spend.", "Proprietary workflow context or domain-specific eval advantage.", "Clear reason it is not just a feature inside a model provider or agent tool." ], "visual_color_token": "sky-400" } ], "controlPoints": { "compute": "Scarce accelerator capacity and the systems that turn it into throughput.", "power": "Energy availability, density, cooling, and site constraints.", "networking": "Scale-up and scale-out fabric for training and serving clusters.", "cloud_capacity": "Procured and managed AI compute capacity.", "source_data": "Raw enterprise data in systems of record, warehouses, applications, documents, and logs.", "governance": "Ownership, quality, retention, lineage, and semantic rules around source data.", "lineage": "Evidence of where data came from and how it was transformed.", "access_control": "Which users, services, models, and agents may read or mutate data.", "embeddings": "Vector representations or other AI-consumable features derived from source data.", "vector_index": "Searchable vector, hybrid, graph, or retrieval indexes.", "retrieval": "Runtime retrieval and citation path that feeds model context.", "api_gateway": "Controlled API surface that exposes data, tools, or model calls to agents.", "context_layer": "Compiled, policy-wrapped, task-relevant context assembled for AI use.", "orchestration": "Builder workflows that compose models, tools, data, and evaluation.", "evals": "Evidence systems for quality, safety, reliability, and drift.", "developer_workflow": "IDE, CI, deployment, and operational surfaces used by builders.", "context_management": "Selection, compression, memory, and provenance of context used by developer agents.", "tool_harness": "Safe invocation layer for shell, browser, code, MCP, APIs, and repo operations.", "weights": "The model artifact, whether closed, gated, or open-weight.", "benchmarks": "Public and private measures of model capability.", "token_pricing": "The unit economics of using model intelligence.", "model_policy": "Safety behavior, availability, data-handling, and release controls.", "routing": "Selection, fallback, caching, and provider abstraction at inference time.", "workflow_distribution": "Access to existing business process surfaces.", "proprietary_context": "Product or enterprise context that improves output quality.", "seats": "Paid or active user footprint.", "vertical_expertise": "Domain-specific workflow knowledge, templates, evals, and compliance posture.", "tools": "External systems an AI can call.", "memory": "Persistent context, plans, preferences, and task state.", "permissions": "Authority boundaries for reading, writing, and acting.", "task_execution": "Completion of multi-step work, not only response generation.", "human_review": "Escalation, approval, and exception handling around delegated work.", "policy": "Rules for where AI may be used and what it may do.", "identity": "User, agent, tool, and workload identity.", "audit": "Evidence of prompts, responses, decisions, actions, and approvals.", "compliance": "Certifications, controls, data residency, and regulated-workload posture." }, "sharedResponsibilityControls": [ { "id": "accuracy", "label": "Accuracy and fitness for task" }, { "id": "data_exposure", "label": "Data exposure and retention" }, { "id": "action_safety", "label": "Action safety and rollback" }, { "id": "observability", "label": "Logs, traces, evals, and cost visibility" }, { "id": "compliance", "label": "Compliance, residency, and audit evidence" }, { "id": "human_review", "label": "Human review and escalation" }, { "id": "outcome_accountability", "label": "Business outcome accountability" } ], "vendors": [ { "id": "nvidia", "name": "NVIDIA", "logo_domain": "nvidia.com", "primary_category": "ai_infrastructure", "secondary_roles": [ "developer_tools" ], "market_position": "leader", "placement_confidence": "high", "control_points": [ "compute", "networking", "developer_workflow" ], "leader_read": "The merchant accelerator and systems control point for most frontier AI buildouts; watch revenue mix, networking attach, and supply availability.\n", "source_refs": [ "src-nvidia-investor-relations", "src-delloro-q1-2025-data-center-capex" ] }, { "id": "amd", "name": "AMD", "logo_domain": "amd.com", "primary_category": "ai_infrastructure", "secondary_roles": [], "market_position": "challenger", "placement_confidence": "medium", "control_points": [ "compute" ], "leader_read": "Challenger accelerator supplier; relevance rises when open software, cloud availability, and price-performance create credible substitution.\n", "source_refs": [ "src-delloro-q1-2025-data-center-capex" ] }, { "id": "broadcom", "name": "Broadcom", "logo_domain": "broadcom.com", "primary_category": "ai_infrastructure", "secondary_roles": [], "market_position": "leader", "placement_confidence": "medium", "control_points": [ "networking", "compute" ], "leader_read": "Custom silicon and networking control point for hyperscale AI systems.\n", "source_refs": [ "src-delloro-q1-2025-data-center-capex" ] }, { "id": "amazon-web-services", "name": "Amazon Web Services", "logo_domain": "aws.amazon.com", "primary_category": "ai_infrastructure", "secondary_roles": [ "model_portfolio", "ai_ready_data", "developer_tools" ], "market_position": "leader", "placement_confidence": "high", "control_points": [ "cloud_capacity", "compute", "routing", "developer_workflow" ], "leader_read": "Full-stack AI cloud with capacity, managed model access, and enterprise procurement gravity.\n", "source_refs": [ "src-aws-ai-services" ] }, { "id": "microsoft", "name": "Microsoft", "logo_domain": "microsoft.com", "primary_category": "commercial_tools", "secondary_roles": [ "ai_infrastructure", "model_portfolio", "developer_tools" ], "market_position": "leader", "placement_confidence": "high", "control_points": [ "workflow_distribution", "seats", "cloud_capacity", "identity" ], "leader_read": "Enterprise distribution leader through Microsoft 365, GitHub, Azure, and identity surfaces; the strategic question is how much AI spend bundles into existing contracts.\n", "source_refs": [ "src-microsoft-investor-relations", "src-techcrunch-microsoft-copilot-20m-paid-users", "src-azure-ai" ] }, { "id": "google", "name": "Google", "logo_domain": "google.com", "primary_category": "model_portfolio", "secondary_roles": [ "ai_infrastructure", "data_management", "commercial_tools" ], "market_position": "leader", "placement_confidence": "high", "control_points": [ "weights", "benchmarks", "cloud_capacity", "workflow_distribution" ], "leader_read": "Frontier model, cloud platform, and consumer distribution leader with unusually strong first-party data and infrastructure.\n", "source_refs": [ "src-google-cloud-ai", "src-techcrunch-gemini-400m-mau", "src-artificial-analysis-models" ] }, { "id": "meta", "name": "Meta", "logo_domain": "meta.com", "primary_category": "model_portfolio", "secondary_roles": [ "commercial_tools" ], "market_position": "leader", "placement_confidence": "high", "control_points": [ "weights", "workflow_distribution", "proprietary_context" ], "leader_read": "Open-weight ecosystem anchor with massive consumer distribution; track whether open model gravity converts into developer and enterprise control.\n", "source_refs": [ "src-meta-ai-llama", "src-huggingface-models" ] }, { "id": "openai", "name": "OpenAI", "logo_domain": "openai.com", "primary_category": "model_portfolio", "secondary_roles": [ "developer_tools", "agent_tools", "commercial_tools" ], "market_position": "leader", "placement_confidence": "high", "control_points": [ "weights", "token_pricing", "model_policy", "workflow_distribution" ], "leader_read": "Frontier model portfolio leader now pushing upward into developer tools, agents, and commercial work through Codex, ChatGPT, and consulting-style enterprise services.\n", "source_refs": [ "src-openai-enterprise-privacy", "src-artificial-analysis-models" ] }, { "id": "anthropic", "name": "Anthropic", "logo_domain": "anthropic.com", "primary_category": "model_portfolio", "secondary_roles": [ "developer_tools", "agent_tools", "commercial_tools" ], "market_position": "leader", "placement_confidence": "high", "control_points": [ "weights", "model_policy", "tools", "audit" ], "leader_read": "Frontier model provider moving upward through Claude Code, computer use, enterprise deployments, and consulting-style implementation services.\n", "source_refs": [ "src-anthropic-trust-center", "src-artificial-analysis-models" ] }, { "id": "xai", "name": "xAI", "logo_domain": "x.ai", "primary_category": "model_portfolio", "secondary_roles": [ "commercial_tools" ], "market_position": "challenger", "placement_confidence": "medium", "control_points": [ "weights", "workflow_distribution" ], "leader_read": "Frontier challenger with distribution through X and fast model iteration.\n", "source_refs": [ "src-artificial-analysis-models" ] }, { "id": "deepseek", "name": "DeepSeek AI", "logo_domain": "deepseek.com", "primary_category": "model_portfolio", "secondary_roles": [], "market_position": "challenger", "placement_confidence": "high", "control_points": [ "weights", "token_pricing", "benchmarks" ], "leader_read": "Efficiency and open-weight pressure point that forces the frontier to justify price, latency, and training-capital intensity.\n", "source_refs": [ "src-artificial-analysis-models", "src-huggingface-models" ] }, { "id": "alibaba", "name": "Alibaba", "logo_domain": "alibabacloud.com", "primary_category": "model_portfolio", "secondary_roles": [ "ai_infrastructure", "data_management" ], "market_position": "challenger", "placement_confidence": "high", "control_points": [ "weights", "cloud_capacity", "benchmarks" ], "leader_read": "China-scale cloud and open-model contender; important for regional sovereignty and open-weight benchmark pressure.\n", "source_refs": [ "src-artificial-analysis-models", "src-huggingface-models" ] }, { "id": "mistral-ai", "name": "Mistral AI", "logo_domain": "mistral.ai", "primary_category": "model_portfolio", "secondary_roles": [], "market_position": "challenger", "placement_confidence": "high", "control_points": [ "weights", "token_pricing", "model_policy" ], "leader_read": "European frontier and open-weight contender; relevance rises with sovereignty, portability, and enterprise deployment concerns.\n", "source_refs": [ "src-artificial-analysis-models", "src-huggingface-models" ] }, { "id": "cohere", "name": "Cohere", "logo_domain": "cohere.com", "primary_category": "model_portfolio", "secondary_roles": [ "ai_ready_data" ], "market_position": "specialist", "placement_confidence": "medium", "control_points": [ "weights", "proprietary_context", "model_policy" ], "leader_read": "Enterprise and retrieval-oriented model specialist.\n", "source_refs": [ "src-artificial-analysis-models" ] }, { "id": "hugging-face", "name": "Hugging Face", "logo_domain": "huggingface.co", "primary_category": "model_portfolio", "secondary_roles": [ "developer_tools", "ai_ready_data" ], "market_position": "leader", "placement_confidence": "high", "control_points": [ "developer_workflow", "weights", "benchmarks" ], "leader_read": "Open-model registry and developer distribution layer; the clearest public signal for open ecosystem pull.\n", "source_refs": [ "src-huggingface-models" ] }, { "id": "databricks", "name": "Databricks", "logo_domain": "databricks.com", "primary_category": "data_management", "secondary_roles": [ "model_portfolio", "ai_ready_data" ], "market_position": "leader", "placement_confidence": "high", "control_points": [ "source_data", "governance", "lineage", "embeddings" ], "leader_read": "Data-platform control point for enterprises that want AI close to governed data and ML operations.\n", "source_refs": [ "src-databricks-ai" ] }, { "id": "snowflake", "name": "Snowflake", "logo_domain": "snowflake.com", "primary_category": "data_management", "secondary_roles": [ "ai_ready_data" ], "market_position": "leader", "placement_confidence": "high", "control_points": [ "source_data", "governance", "access_control", "api_gateway" ], "leader_read": "Governed data-cloud control point for AI workloads that start with enterprise data access and policy.\n", "source_refs": [ "src-snowflake-ai" ] }, { "id": "palantir", "name": "Palantir", "logo_domain": "palantir.com", "primary_category": "data_management", "secondary_roles": [ "agent_tools", "commercial_tools" ], "market_position": "leader", "placement_confidence": "medium", "control_points": [ "source_data", "proprietary_context", "workflow_distribution", "permissions" ], "leader_read": "Enterprise operating-layer contender where AI is embedded into governed workflows and decision processes.\n", "source_refs": [ "src-stanford-ai-index-2025-economy" ] }, { "id": "langchain", "name": "LangChain", "logo_domain": "langchain.com", "primary_category": "developer_tools", "secondary_roles": [ "agent_tools", "ai_ready_data" ], "market_position": "specialist", "placement_confidence": "medium", "control_points": [ "orchestration", "evals", "context_management", "tool_harness" ], "leader_read": "Developer orchestration and observability specialist for multi-model, tool-using applications.\n", "source_refs": [ "src-cbinsights-ai-agent-market-map-2025" ] }, { "id": "vercel", "name": "Vercel", "logo_domain": "vercel.com", "primary_category": "ai_ready_data", "secondary_roles": [ "developer_tools", "model_portfolio" ], "market_position": "challenger", "placement_confidence": "medium", "control_points": [ "routing", "api_gateway", "developer_workflow", "context_layer" ], "leader_read": "Frontend and AI runtime contender where application deployment, model routing, and developer workflow meet.\n", "source_refs": [ "src-nfx-generative-ai-five-layer-stack" ] }, { "id": "cloudflare", "name": "Cloudflare", "logo_domain": "cloudflare.com", "primary_category": "ai_ready_data", "secondary_roles": [ "ai_infrastructure", "developer_tools" ], "market_position": "challenger", "placement_confidence": "medium", "control_points": [ "routing", "api_gateway", "access_control" ], "leader_read": "Edge-network runtime contender for distributed inference, routing, and security controls.\n", "source_refs": [ "src-nfx-generative-ai-five-layer-stack" ] }, { "id": "together-ai", "name": "Together AI", "logo_domain": "together.ai", "primary_category": "ai_ready_data", "secondary_roles": [ "ai_infrastructure", "model_portfolio" ], "market_position": "challenger", "placement_confidence": "medium", "control_points": [ "routing", "weights", "api_gateway" ], "leader_read": "Open-model serving and fine-tuning specialist; useful signal for open-weight production demand.\n", "source_refs": [ "src-huggingface-models" ] }, { "id": "groq", "name": "Groq", "logo_domain": "groq.com", "primary_category": "ai_ready_data", "secondary_roles": [ "ai_infrastructure" ], "market_position": "challenger", "placement_confidence": "medium", "control_points": [ "routing", "compute" ], "leader_read": "Specialized inference hardware/runtime contender with latency as the primary differentiation.\n", "source_refs": [ "src-artificial-analysis-models" ] }, { "id": "pinecone", "name": "Pinecone", "logo_domain": "pinecone.io", "primary_category": "ai_ready_data", "secondary_roles": [], "market_position": "leader", "placement_confidence": "high", "control_points": [ "vector_index", "retrieval", "embeddings" ], "leader_read": "Managed vector database leader; important because it turns raw enterprise content into retrieval surfaces agents can consume directly.\n", "source_refs": [ "src-pinecone-docs" ] }, { "id": "weaviate", "name": "Weaviate", "logo_domain": "weaviate.io", "primary_category": "ai_ready_data", "secondary_roles": [], "market_position": "challenger", "placement_confidence": "high", "control_points": [ "vector_index", "retrieval", "embeddings" ], "leader_read": "Open-source vector and hybrid-search contender for teams that want more control over the AI-ready data layer.\n", "source_refs": [ "src-weaviate-docs" ] }, { "id": "zilliz", "name": "Zilliz / Milvus", "logo_domain": "zilliz.com", "primary_category": "ai_ready_data", "secondary_roles": [], "market_position": "challenger", "placement_confidence": "medium", "control_points": [ "vector_index", "retrieval" ], "leader_read": "Milvus ecosystem signal for open vector infrastructure and high-scale retrieval workloads.\n", "source_refs": [ "src-zilliz-milvus-docs" ] }, { "id": "mongodb", "name": "MongoDB", "logo_domain": "mongodb.com", "primary_category": "data_management", "secondary_roles": [ "ai_ready_data" ], "market_position": "challenger", "placement_confidence": "medium", "control_points": [ "source_data", "vector_index", "api_gateway" ], "leader_read": "Operational data incumbent moving upward by adding vector search and AI application data patterns.\n", "source_refs": [ "src-mongodb-vector-search" ] }, { "id": "elastic", "name": "Elastic", "logo_domain": "elastic.co", "primary_category": "ai_ready_data", "secondary_roles": [ "data_management" ], "market_position": "challenger", "placement_confidence": "medium", "control_points": [ "retrieval", "vector_index", "source_data" ], "leader_read": "Search incumbent with hybrid retrieval and observability roots; relevant when AI-ready data starts from search, logs, and operational documents.\n", "source_refs": [ "src-elastic-vector-search" ] }, { "id": "perplexity", "name": "Perplexity", "logo_domain": "perplexity.ai", "primary_category": "commercial_tools", "secondary_roles": [ "ai_ready_data" ], "market_position": "challenger", "placement_confidence": "medium", "control_points": [ "workflow_distribution", "proprietary_context", "routing" ], "leader_read": "AI answer/search application that competes on user habit, citation UX, and model-routing quality.\n", "source_refs": [ "src-menlo-2025-enterprise-ai-report" ] }, { "id": "github", "name": "GitHub", "logo_domain": "github.com", "primary_category": "developer_tools", "secondary_roles": [ "agent_tools" ], "market_position": "leader", "placement_confidence": "high", "control_points": [ "developer_workflow", "proprietary_context", "seats", "tool_harness" ], "leader_read": "Developer workflow distribution leader; coding is one of the clearest paid AI application categories.\n", "source_refs": [ "src-microsoft-investor-relations", "src-menlo-2025-enterprise-ai-report" ] }, { "id": "cursor", "name": "Cursor", "logo_domain": "cursor.com", "primary_category": "developer_tools", "secondary_roles": [ "agent_tools" ], "market_position": "challenger", "placement_confidence": "medium", "control_points": [ "developer_workflow", "context_management", "tools" ], "leader_read": "AI-native coding application where agentic development behavior is visible earlier than in many enterprise categories.\n", "source_refs": [ "src-menlo-2025-enterprise-ai-report" ] }, { "id": "opencode", "name": "OpenCode", "logo_domain": "opencode.ai", "primary_category": "developer_tools", "secondary_roles": [ "agent_tools" ], "market_position": "specialist", "placement_confidence": "low", "control_points": [ "developer_workflow", "context_management", "tool_harness" ], "leader_read": "Open developer-agent signal; tracked because open coding agents may become the portability layer between models, repos, and local tools.\n", "source_refs": [ "src-brianletort-ai-capability-postures" ] }, { "id": "hermes", "name": "Hermes", "logo_domain": "brianletort.ai", "primary_category": "agent_tools", "secondary_roles": [ "developer_tools" ], "market_position": "specialist", "placement_confidence": "medium", "control_points": [ "memory", "task_execution", "human_review", "tools" ], "leader_read": "Personal agent stack signal: scheduled operations, memory, tool use, and exception escalation over owned infrastructure.\n", "source_refs": [ "src-brianletort-ai-capability-postures" ] }, { "id": "zeroclaw", "name": "Zeroclaw", "logo_domain": "brianletort.ai", "primary_category": "agent_tools", "secondary_roles": [ "developer_tools" ], "market_position": "specialist", "placement_confidence": "medium", "control_points": [ "task_execution", "permissions", "tool_harness" ], "leader_read": "Lightweight agent runtime signal for owned-agent execution rather than SaaS-owned workflow automation.\n", "source_refs": [ "src-brianletort-ai-capability-postures" ] }, { "id": "openclaw", "name": "OpenClaw", "logo_domain": "brianletort.ai", "primary_category": "agent_tools", "secondary_roles": [ "developer_tools" ], "market_position": "watch", "placement_confidence": "low", "control_points": [ "task_execution", "permissions", "memory" ], "leader_read": "Emerging open agent-tooling signal; tracked as a future-stack example, not a settled public market leader.\n", "source_refs": [ "src-brianletort-ai-capability-postures" ] }, { "id": "agent-zero", "name": "Agent Zero", "logo_domain": "agent-zero.ai", "primary_category": "agent_tools", "secondary_roles": [], "market_position": "specialist", "placement_confidence": "medium", "control_points": [ "tools", "memory", "task_execution" ], "leader_read": "General-purpose agent runtime signal for tool use, memory, and autonomous task loops.\n", "source_refs": [ "src-brianletort-ai-capability-postures" ] }, { "id": "salesforce", "name": "Salesforce", "logo_domain": "salesforce.com", "primary_category": "commercial_tools", "secondary_roles": [ "agent_tools", "data_management" ], "market_position": "leader", "placement_confidence": "high", "control_points": [ "workflow_distribution", "proprietary_context", "seats" ], "leader_read": "Incumbent workflow distributor; the strategic question is whether AI agents reinforce or unbundle CRM process ownership.\n", "source_refs": [ "src-menlo-2025-enterprise-ai-report" ] }, { "id": "servicenow", "name": "ServiceNow", "logo_domain": "servicenow.com", "primary_category": "commercial_tools", "secondary_roles": [ "agent_tools" ], "market_position": "leader", "placement_confidence": "high", "control_points": [ "workflow_distribution", "task_execution", "permissions" ], "leader_read": "Enterprise workflow incumbent positioned for governed AI task execution inside service-management processes.\n", "source_refs": [ "src-menlo-2025-enterprise-ai-report" ] }, { "id": "adobe", "name": "Adobe", "logo_domain": "adobe.com", "primary_category": "commercial_tools", "secondary_roles": [], "market_position": "leader", "placement_confidence": "high", "control_points": [ "workflow_distribution", "proprietary_context", "compliance" ], "leader_read": "Creative-workflow incumbent with strong distribution and provenance needs.\n", "source_refs": [ "src-menlo-2025-enterprise-ai-report" ] }, { "id": "elevenlabs", "name": "ElevenLabs", "logo_domain": "elevenlabs.io", "primary_category": "commercial_tools", "secondary_roles": [ "model_portfolio" ], "market_position": "specialist", "placement_confidence": "medium", "control_points": [ "workflow_distribution", "proprietary_context", "vertical_expertise" ], "leader_read": "Voice AI specialist; useful marker for modality-specific application markets outside general chat.\n", "source_refs": [ "src-menlo-2025-enterprise-ai-report" ] }, { "id": "writer", "name": "Writer", "logo_domain": "writer.com", "primary_category": "commercial_tools", "secondary_roles": [ "model_portfolio" ], "market_position": "challenger", "placement_confidence": "medium", "control_points": [ "vertical_expertise", "workflow_distribution", "proprietary_context" ], "leader_read": "Enterprise writing and knowledge-work platform; a test case for whether specialist commercial AI survives model-provider expansion.\n", "source_refs": [ "src-writer-ai", "src-menlo-2025-enterprise-ai-report" ] }, { "id": "harvey", "name": "Harvey", "logo_domain": "harvey.ai", "primary_category": "commercial_tools", "secondary_roles": [ "agent_tools" ], "market_position": "challenger", "placement_confidence": "medium", "control_points": [ "vertical_expertise", "proprietary_context", "workflow_distribution" ], "leader_read": "Legal AI specialist with domain workflow depth; a clean example of the commercial-tool layer above models and agent infrastructure.\n", "source_refs": [ "src-harvey-ai", "src-menlo-2025-enterprise-ai-report" ] }, { "id": "heygen", "name": "HeyGen", "logo_domain": "heygen.com", "primary_category": "commercial_tools", "secondary_roles": [ "model_portfolio" ], "market_position": "challenger", "placement_confidence": "medium", "control_points": [ "vertical_expertise", "workflow_distribution" ], "leader_read": "Video generation specialist; relevant as multimodal models pressure point-solution durability.\n", "source_refs": [ "src-heygen", "src-menlo-2025-enterprise-ai-report" ] }, { "id": "glean", "name": "Glean", "logo_domain": "glean.com", "primary_category": "commercial_tools", "secondary_roles": [ "ai_ready_data", "data_management" ], "market_position": "challenger", "placement_confidence": "medium", "control_points": [ "retrieval", "proprietary_context", "workflow_distribution" ], "leader_read": "Enterprise search and assistant layer that sits near the boundary between AI-ready data and commercial workflow.\n", "source_refs": [ "src-glean", "src-menlo-2025-enterprise-ai-report" ] }, { "id": "cognosys", "name": "Cognosys", "logo_domain": "cognosys.ai", "primary_category": "agent_tools", "secondary_roles": [ "commercial_tools" ], "market_position": "specialist", "placement_confidence": "low", "control_points": [ "task_execution", "tools", "memory" ], "leader_read": "Early agentic workflow specialist; tracked as a category signal, not a settled leader.\n", "source_refs": [ "src-cbinsights-ai-agent-market-map-2025" ] }, { "id": "adept", "name": "Adept", "logo_domain": "adept.ai", "primary_category": "agent_tools", "secondary_roles": [ "model_portfolio" ], "market_position": "specialist", "placement_confidence": "low", "control_points": [ "tools", "task_execution" ], "leader_read": "Computer-use and action-model precedent; retained because it shaped the category even as market structure keeps moving.\n", "source_refs": [ "src-cbinsights-ai-agent-market-map-2025" ] }, { "id": "sierra", "name": "Sierra", "logo_domain": "sierra.ai", "primary_category": "agent_tools", "secondary_roles": [ "commercial_tools" ], "market_position": "challenger", "placement_confidence": "medium", "control_points": [ "task_execution", "workflow_distribution", "human_review" ], "leader_read": "Customer-facing agentic workflow contender; watch for production deployments and accountability controls.\n", "source_refs": [ "src-cbinsights-ai-agent-market-map-2025" ] }, { "id": "wiz", "name": "Wiz", "logo_domain": "wiz.io", "primary_category": "commercial_tools", "secondary_roles": [ "data_management" ], "market_position": "leader", "placement_confidence": "medium", "control_points": [ "policy", "audit", "compliance" ], "leader_read": "Cloud security control point that can extend into AI posture management as models and agents become part of the attack surface.\n", "source_refs": [ "src-nist-ai-risk-management-framework" ] }, { "id": "crowdstrike", "name": "CrowdStrike", "logo_domain": "crowdstrike.com", "primary_category": "commercial_tools", "secondary_roles": [ "agent_tools" ], "market_position": "leader", "placement_confidence": "medium", "control_points": [ "policy", "identity", "audit" ], "leader_read": "Security incumbent where AI changes both defense workflows and governance requirements.\n", "source_refs": [ "src-nist-ai-risk-management-framework" ] }, { "id": "credo-ai", "name": "Credo AI", "logo_domain": "credo.ai", "primary_category": "commercial_tools", "secondary_roles": [ "data_management" ], "market_position": "specialist", "placement_confidence": "medium", "control_points": [ "policy", "audit", "compliance" ], "leader_read": "AI governance specialist; useful pure-play signal for policy, audit, and risk-management demand.\n", "source_refs": [ "src-nist-ai-risk-management-framework" ] } ], "metrics": [ { "id": "metric-infra-datacenter-capex-q1-2025-growth", "category": "ai_infrastructure", "metric_type": "capital", "label": "Data-center capex growth", "value": "53% YoY", "period": "Q1 2025", "refresh_days": 90, "confidence": "high", "source_refs": [ "src-delloro-q1-2025-data-center-capex" ], "interpretation": "AI infrastructure remains capital constrained; capex direction is the first-order signal for who can supply capacity.\n" }, { "id": "metric-infra-accelerated-server-share", "category": "ai_infrastructure", "metric_type": "mix_shift", "label": "High-end accelerated servers", "value": "> one-third of total data-center capex", "period": "2025 forecast", "refresh_days": 90, "confidence": "medium", "source_refs": [ "src-delloro-q1-2025-data-center-capex" ], "interpretation": "Accelerator-heavy systems are no longer a niche line item; they are a large share of the data-center investment stack.\n" }, { "id": "metric-infra-datacenter-capex-2025-forecast", "category": "ai_infrastructure", "metric_type": "capital", "label": "Data-center capex forecast", "value": "30% growth", "period": "2025 forecast", "refresh_days": 90, "confidence": "high", "source_refs": [ "src-delloro-q1-2025-data-center-capex" ], "interpretation": "Sustained infrastructure expansion keeps the AI substrate layer strategic, not commodity.\n" }, { "id": "metric-model-swe-bench-frontier-gain", "category": "model_portfolio", "metric_type": "capability", "label": "SWE-bench performance jump", "value": "71.7%", "prior_value": "4.4%", "period": "2024 vs 2023", "refresh_days": 180, "confidence": "high", "source_refs": [ "src-stanford-ai-index-2025-technical-performance" ], "interpretation": "Software-engineering capability moved from toy benchmark to enterprise relevance, changing the model layer's practical value.\n" }, { "id": "metric-model-open-closed-gap", "category": "model_portfolio", "metric_type": "capability", "label": "Open-weight vs closed-weight performance gap", "value": "1.70%", "prior_value": "8.04%", "period": "2024 vs prior year", "refresh_days": 180, "confidence": "high", "source_refs": [ "src-stanford-ai-index-2025-technical-performance" ], "interpretation": "Narrowing gaps increase buyer leverage and make openness a strategic dimension, not only a developer preference.\n" }, { "id": "metric-model-live-price-latency-availability", "category": "model_portfolio", "metric_type": "efficiency", "label": "Live model price, latency, speed, and context comparisons", "value": "available", "period": "live", "refresh_days": 30, "confidence": "high", "source_refs": [ "src-artificial-analysis-models" ], "interpretation": "Model leadership should be benchmark-adjusted by cost and latency, not read from capability leaderboards alone.\n" }, { "id": "metric-data-management-enterprise-adoption", "category": "data_management", "metric_type": "adoption", "label": "Organizations using AI", "value": "78%", "prior_value": "55%", "period": "2024 vs 2023", "refresh_days": 365, "confidence": "high", "source_refs": [ "src-stanford-ai-index-2025-economy" ], "interpretation": "More organizational AI use increases pressure to expose governed systems of record to agents without copying everything into SaaS silos.\n" }, { "id": "metric-data-management-genai-business-use", "category": "data_management", "metric_type": "adoption", "label": "Generative AI in at least one business function", "value": "71%", "prior_value": "33%", "period": "2024 vs 2023", "refresh_days": 365, "confidence": "high", "source_refs": [ "src-stanford-ai-index-2025-economy" ], "interpretation": "Business-function adoption makes source-data access, lineage, and permissions first-class AI architecture concerns.\n" }, { "id": "metric-data-management-enterprise-genai-spend", "category": "data_management", "metric_type": "spend", "label": "Enterprise GenAI spend", "value": "$37B", "period": "2025 estimate", "refresh_days": 365, "confidence": "medium", "source_refs": [ "src-menlo-2025-enterprise-ai-report" ], "interpretation": "Enterprise AI spend eventually lands against data platforms, not only model APIs and point tools.\n" }, { "id": "metric-ai-ready-vector-stack-availability", "category": "ai_ready_data", "metric_type": "capability", "label": "Vector and hybrid retrieval availability", "value": "available across Pinecone, Weaviate, Milvus, MongoDB, Elastic", "period": "current", "refresh_days": 90, "confidence": "high", "source_refs": [ "src-pinecone-docs", "src-weaviate-docs", "src-zilliz-milvus-docs", "src-mongodb-vector-search", "src-elastic-vector-search" ], "interpretation": "Retrieval infrastructure is now broad enough to treat AI-ready data as a separate layer between raw datastores and agent execution.\n" }, { "id": "metric-ai-ready-api-gateway-signal", "category": "ai_ready_data", "metric_type": "architecture", "label": "Model and context gateway pattern", "value": "emerging", "period": "2026", "refresh_days": 90, "confidence": "medium", "source_refs": [ "src-nfx-generative-ai-five-layer-stack", "src-artificial-analysis-models" ], "interpretation": "API gateways, model routers, and context wrappers become the control plane for which agents can consume which data.\n" }, { "id": "metric-ai-ready-open-model-serving-signal", "category": "ai_ready_data", "metric_type": "adoption", "label": "Open-model serving demand proxy", "value": "downloads and model-card activity", "period": "live", "refresh_days": 30, "confidence": "medium", "source_refs": [ "src-huggingface-models" ], "interpretation": "Open ecosystem activity is a maintainable proxy for AI-ready data and serving demand where vendors do not disclose token volume.\n" }, { "id": "metric-dev-tools-coding-category-spend", "category": "developer_tools", "metric_type": "spend", "label": "Coding tools as leading GenAI application category", "value": "top enterprise spend category", "period": "2025 estimate", "refresh_days": 365, "confidence": "medium", "source_refs": [ "src-menlo-2025-enterprise-ai-report" ], "interpretation": "Developer tools are the first place where AI changes production work, not only information retrieval.\n" }, { "id": "metric-dev-tools-agentic-coding-signal", "category": "developer_tools", "metric_type": "workflow", "label": "Coding agents and CLI tools", "value": "Cursor / Codex / Claude Code / OpenCode", "period": "2026", "refresh_days": 90, "confidence": "medium", "source_refs": [ "src-brianletort-ai-capability-postures", "src-menlo-2025-enterprise-ai-report" ], "interpretation": "Build tools are moving from autocomplete to agent supervision, with context management and tool harnesses becoming the real differentiators.\n" }, { "id": "metric-dev-tools-context-management", "category": "developer_tools", "metric_type": "architecture", "label": "Context management as build substrate", "value": "emerging", "period": "2026", "refresh_days": 90, "confidence": "medium", "source_refs": [ "src-brianletort-ai-capability-postures", "src-nfx-generative-ai-five-layer-stack" ], "interpretation": "The durable build-tool layer may be less about the IDE and more about how context, tools, repo state, and review loops are packaged for agents.\n" }, { "id": "metric-agents-market-map-count", "category": "agent_tools", "metric_type": "market_structure", "label": "Agent startups tracked", "value": "170+", "period": "March 2025", "refresh_days": 180, "confidence": "medium", "source_refs": [ "src-cbinsights-ai-agent-market-map-2025" ], "interpretation": "Agentic workflows are a distinct market structure, not just another app feature.\n" }, { "id": "metric-agents-funding-2024", "category": "agent_tools", "metric_type": "funding", "label": "Agent startup funding", "value": "$3.8B", "period": "2024", "refresh_days": 365, "confidence": "medium", "source_refs": [ "src-cbinsights-ai-agent-market-map-2025" ], "interpretation": "Funding growth signals that investors view delegated work as a separate value pool.\n" }, { "id": "metric-agents-production-readiness-disclosure", "category": "agent_tools", "metric_type": "disclosure", "label": "Public production-readiness disclosure", "value": "uneven", "period": "2026", "refresh_days": 90, "confidence": "medium", "source_refs": [ "src-cbinsights-ai-agent-market-map-2025" ], "interpretation": "Human-in-loop controls, tool permissions, and audit logs should carry as much weight as demos.\n" }, { "id": "metric-commercial-enterprise-app-spend", "category": "commercial_tools", "metric_type": "spend", "label": "GenAI application spend", "value": "$19B+", "period": "2025 estimate", "refresh_days": 365, "confidence": "medium", "source_refs": [ "src-menlo-2025-enterprise-ai-report" ], "interpretation": "Commercial tools are already a major value-capture zone, but the durable winners need workflow depth that survives model-provider expansion.\n" }, { "id": "metric-commercial-m365-copilot-paid-seats", "category": "commercial_tools", "metric_type": "adoption", "label": "Microsoft 365 Copilot paid enterprise seats", "value": "20M+", "period": "April 2026", "refresh_days": 90, "confidence": "high", "source_refs": [ "src-techcrunch-microsoft-copilot-20m-paid-users" ], "interpretation": "Seat adoption is the cleanest public signal for commercial AI distribution through incumbent software suites.\n" }, { "id": "metric-commercial-specialist-tool-pressure", "category": "commercial_tools", "metric_type": "market_structure", "label": "Specialist tool pressure", "value": "model providers moving upward", "period": "2026", "refresh_days": 90, "confidence": "medium", "source_refs": [ "src-menlo-2025-enterprise-ai-report", "src-openai-enterprise-privacy", "src-anthropic-trust-center" ], "interpretation": "OpenAI and Anthropic moving into implementation services raises the bar for point solutions: they need proprietary workflow depth, data access, or domain-specific trust.\n" } ], "positions": [ { "vendor_id": "nvidia", "category": "ai_infrastructure", "monthly": [ { "month": "2026-03", "position": "leader", "score": 94, "momentum": "flat" }, { "month": "2026-04", "position": "leader", "score": 95, "momentum": "up" }, { "month": "2026-05", "position": "leader", "score": 96, "momentum": "up" } ], "evolution_read": "Merchant accelerator leadership remains the infrastructure benchmark; score rises with continued capex pull-through and networking attach.\n" }, { "vendor_id": "amazon-web-services", "category": "ai_infrastructure", "monthly": [ { "month": "2026-03", "position": "leader", "score": 87, "momentum": "flat" }, { "month": "2026-04", "position": "leader", "score": 88, "momentum": "up" }, { "month": "2026-05", "position": "leader", "score": 88, "momentum": "flat" } ], "evolution_read": "Cloud capacity and procurement breadth keep AWS in the leader band.\n" }, { "vendor_id": "microsoft", "category": "ai_infrastructure", "monthly": [ { "month": "2026-03", "position": "leader", "score": 86, "momentum": "up" }, { "month": "2026-04", "position": "leader", "score": 88, "momentum": "up" }, { "month": "2026-05", "position": "leader", "score": 89, "momentum": "up" } ], "evolution_read": "Infrastructure position strengthens when Azure capacity and Copilot distribution reinforce each other.\n" }, { "vendor_id": "amd", "category": "ai_infrastructure", "monthly": [ { "month": "2026-03", "position": "challenger", "score": 66, "momentum": "flat" }, { "month": "2026-04", "position": "challenger", "score": 68, "momentum": "up" }, { "month": "2026-05", "position": "challenger", "score": 70, "momentum": "up" } ], "evolution_read": "Challenger score improves when substitution pressure and cloud availability become more credible.\n" }, { "vendor_id": "databricks", "category": "data_management", "monthly": [ { "month": "2026-03", "position": "leader", "score": 86, "momentum": "flat" }, { "month": "2026-04", "position": "leader", "score": 87, "momentum": "up" }, { "month": "2026-05", "position": "leader", "score": 88, "momentum": "up" } ], "evolution_read": "Governed data gravity keeps Databricks central as enterprise AI shifts from experiments to operated systems.\n" }, { "vendor_id": "snowflake", "category": "data_management", "monthly": [ { "month": "2026-03", "position": "leader", "score": 82, "momentum": "flat" }, { "month": "2026-04", "position": "leader", "score": 83, "momentum": "up" }, { "month": "2026-05", "position": "leader", "score": 84, "momentum": "up" } ], "evolution_read": "Snowflake remains a leader when AI starts from governed enterprise data.\n" }, { "vendor_id": "hugging-face", "category": "model_portfolio", "monthly": [ { "month": "2026-03", "position": "leader", "score": 80, "momentum": "up" }, { "month": "2026-04", "position": "leader", "score": 81, "momentum": "up" }, { "month": "2026-05", "position": "leader", "score": 82, "momentum": "up" } ], "evolution_read": "Open-model distribution and model-card gravity keep Hugging Face in the platform leader band.\n" }, { "vendor_id": "langchain", "category": "developer_tools", "monthly": [ { "month": "2026-03", "position": "specialist", "score": 62, "momentum": "flat" }, { "month": "2026-04", "position": "specialist", "score": 64, "momentum": "up" }, { "month": "2026-05", "position": "specialist", "score": 66, "momentum": "up" } ], "evolution_read": "Orchestration and observability mindshare rises as multi-model systems become more common.\n" }, { "vendor_id": "openai", "category": "model_portfolio", "monthly": [ { "month": "2026-03", "position": "leader", "score": 92, "momentum": "flat" }, { "month": "2026-04", "position": "leader", "score": 94, "momentum": "up" }, { "month": "2026-05", "position": "leader", "score": 94, "momentum": "flat" } ], "evolution_read": "Frontier capability plus product habit keeps OpenAI in the leader band; open-weight pressure shows up as efficiency pressure, not displacement.\n" }, { "vendor_id": "anthropic", "category": "model_portfolio", "monthly": [ { "month": "2026-03", "position": "leader", "score": 88, "momentum": "up" }, { "month": "2026-04", "position": "leader", "score": 90, "momentum": "up" }, { "month": "2026-05", "position": "leader", "score": 91, "momentum": "up" } ], "evolution_read": "Enterprise trust and agentic coding strength keep Anthropic gaining within the model layer.\n" }, { "vendor_id": "google", "category": "model_portfolio", "monthly": [ { "month": "2026-03", "position": "leader", "score": 86, "momentum": "flat" }, { "month": "2026-04", "position": "leader", "score": 88, "momentum": "up" }, { "month": "2026-05", "position": "leader", "score": 89, "momentum": "up" } ], "evolution_read": "Google benefits from simultaneous model, cloud, and consumer distribution signals.\n" }, { "vendor_id": "deepseek", "category": "model_portfolio", "monthly": [ { "month": "2026-03", "position": "challenger", "score": 78, "momentum": "up" }, { "month": "2026-04", "position": "challenger", "score": 80, "momentum": "up" }, { "month": "2026-05", "position": "challenger", "score": 81, "momentum": "up" } ], "evolution_read": "DeepSeek remains the open-weight efficiency challenger forcing price and training-capital discipline.\n" }, { "vendor_id": "vercel", "category": "ai_ready_data", "monthly": [ { "month": "2026-03", "position": "challenger", "score": 65, "momentum": "up" }, { "month": "2026-04", "position": "challenger", "score": 68, "momentum": "up" }, { "month": "2026-05", "position": "challenger", "score": 71, "momentum": "up" } ], "evolution_read": "Runtime position improves as model routing becomes part of application deployment rather than a separate platform decision.\n" }, { "vendor_id": "cloudflare", "category": "ai_ready_data", "monthly": [ { "month": "2026-03", "position": "challenger", "score": 66, "momentum": "flat" }, { "month": "2026-04", "position": "challenger", "score": 67, "momentum": "up" }, { "month": "2026-05", "position": "challenger", "score": 69, "momentum": "up" } ], "evolution_read": "Edge distribution and security posture keep Cloudflare relevant as inference gets closer to users and policies.\n" }, { "vendor_id": "together-ai", "category": "ai_ready_data", "monthly": [ { "month": "2026-03", "position": "challenger", "score": 64, "momentum": "flat" }, { "month": "2026-04", "position": "challenger", "score": 66, "momentum": "up" }, { "month": "2026-05", "position": "challenger", "score": 68, "momentum": "up" } ], "evolution_read": "Open-model serving demand keeps Together AI in the runtime challenger set.\n" }, { "vendor_id": "groq", "category": "ai_ready_data", "monthly": [ { "month": "2026-03", "position": "challenger", "score": 61, "momentum": "flat" }, { "month": "2026-04", "position": "challenger", "score": 63, "momentum": "up" }, { "month": "2026-05", "position": "challenger", "score": 65, "momentum": "up" } ], "evolution_read": "Groq remains a latency specialist; category position rises when latency-sensitive inference becomes a distinct buying criterion.\n" }, { "vendor_id": "pinecone", "category": "ai_ready_data", "monthly": [ { "month": "2026-03", "position": "leader", "score": 80, "momentum": "flat" }, { "month": "2026-04", "position": "leader", "score": 82, "momentum": "up" }, { "month": "2026-05", "position": "leader", "score": 84, "momentum": "up" } ], "evolution_read": "Pinecone anchors the vector-store portion of the AI-ready data layer.\n" }, { "vendor_id": "weaviate", "category": "ai_ready_data", "monthly": [ { "month": "2026-03", "position": "challenger", "score": 70, "momentum": "flat" }, { "month": "2026-04", "position": "challenger", "score": 72, "momentum": "up" }, { "month": "2026-05", "position": "challenger", "score": 74, "momentum": "up" } ], "evolution_read": "Weaviate gains when teams want open, hybrid, and self-controlled retrieval infrastructure.\n" }, { "vendor_id": "microsoft", "category": "commercial_tools", "monthly": [ { "month": "2026-03", "position": "leader", "score": 88, "momentum": "up" }, { "month": "2026-04", "position": "leader", "score": 91, "momentum": "up" }, { "month": "2026-05", "position": "leader", "score": 92, "momentum": "up" } ], "evolution_read": "Paid Copilot seats and enterprise distribution make Microsoft the application-layer benchmark.\n" }, { "vendor_id": "google", "category": "commercial_tools", "monthly": [ { "month": "2026-03", "position": "leader", "score": 84, "momentum": "up" }, { "month": "2026-04", "position": "leader", "score": 86, "momentum": "up" }, { "month": "2026-05", "position": "leader", "score": 88, "momentum": "up" } ], "evolution_read": "Gemini consumer adoption keeps Google in the leader band even when enterprise monetization is harder to isolate.\n" }, { "vendor_id": "salesforce", "category": "commercial_tools", "monthly": [ { "month": "2026-03", "position": "leader", "score": 79, "momentum": "flat" }, { "month": "2026-04", "position": "leader", "score": 80, "momentum": "up" }, { "month": "2026-05", "position": "leader", "score": 81, "momentum": "up" } ], "evolution_read": "Salesforce stays strong where AI rides existing CRM workflow ownership.\n" }, { "vendor_id": "cursor", "category": "developer_tools", "monthly": [ { "month": "2026-03", "position": "challenger", "score": 70, "momentum": "up" }, { "month": "2026-04", "position": "challenger", "score": 74, "momentum": "up" }, { "month": "2026-05", "position": "challenger", "score": 77, "momentum": "up" } ], "evolution_read": "Cursor gains as coding remains the cleanest category for visible AI productivity and agentic workflow adoption.\n" }, { "vendor_id": "openai", "category": "developer_tools", "monthly": [ { "month": "2026-03", "position": "challenger", "score": 72, "momentum": "up" }, { "month": "2026-04", "position": "leader", "score": 78, "momentum": "up" }, { "month": "2026-05", "position": "leader", "score": 82, "momentum": "up" } ], "evolution_read": "Codex and implementation services move OpenAI upward from model portfolio into the developer-tools layer.\n" }, { "vendor_id": "anthropic", "category": "developer_tools", "monthly": [ { "month": "2026-03", "position": "leader", "score": 78, "momentum": "up" }, { "month": "2026-04", "position": "leader", "score": 82, "momentum": "up" }, { "month": "2026-05", "position": "leader", "score": 85, "momentum": "up" } ], "evolution_read": "Claude Code makes Anthropic one of the most important movers in build tooling, not only model APIs.\n" }, { "vendor_id": "anthropic", "category": "agent_tools", "monthly": [ { "month": "2026-03", "position": "leader", "score": 82, "momentum": "up" }, { "month": "2026-04", "position": "leader", "score": 85, "momentum": "up" }, { "month": "2026-05", "position": "leader", "score": 87, "momentum": "up" } ], "evolution_read": "Claude's coding and tool-use posture keeps Anthropic central to early production agentic workflows.\n" }, { "vendor_id": "github", "category": "developer_tools", "monthly": [ { "month": "2026-03", "position": "leader", "score": 80, "momentum": "up" }, { "month": "2026-04", "position": "leader", "score": 82, "momentum": "up" }, { "month": "2026-05", "position": "leader", "score": 84, "momentum": "up" } ], "evolution_read": "GitHub owns a high-frequency execution surface where agentic behavior can become daily workflow rather than demo.\n" }, { "vendor_id": "servicenow", "category": "agent_tools", "monthly": [ { "month": "2026-03", "position": "leader", "score": 76, "momentum": "flat" }, { "month": "2026-04", "position": "leader", "score": 78, "momentum": "up" }, { "month": "2026-05", "position": "leader", "score": 80, "momentum": "up" } ], "evolution_read": "ServiceNow is positioned where delegated action meets governed enterprise process.\n" }, { "vendor_id": "sierra", "category": "agent_tools", "monthly": [ { "month": "2026-03", "position": "challenger", "score": 63, "momentum": "up" }, { "month": "2026-04", "position": "challenger", "score": 66, "momentum": "up" }, { "month": "2026-05", "position": "challenger", "score": 69, "momentum": "up" } ], "evolution_read": "Sierra is a useful challenger signal for customer-facing production agents.\n" }, { "vendor_id": "hermes", "category": "agent_tools", "monthly": [ { "month": "2026-03", "position": "specialist", "score": 62, "momentum": "flat" }, { "month": "2026-04", "position": "specialist", "score": 66, "momentum": "up" }, { "month": "2026-05", "position": "specialist", "score": 70, "momentum": "up" } ], "evolution_read": "Hermes represents owned-agent operations over personal infrastructure and owned data, with humans paged only on exception.\n" }, { "vendor_id": "zeroclaw", "category": "agent_tools", "monthly": [ { "month": "2026-03", "position": "specialist", "score": 58, "momentum": "flat" }, { "month": "2026-04", "position": "specialist", "score": 62, "momentum": "up" }, { "month": "2026-05", "position": "specialist", "score": 66, "momentum": "up" } ], "evolution_read": "Zeroclaw is tracked as a future-stack signal for lightweight autonomous execution over owner-controlled tools.\n" }, { "vendor_id": "wiz", "category": "commercial_tools", "monthly": [ { "month": "2026-03", "position": "leader", "score": 78, "momentum": "flat" }, { "month": "2026-04", "position": "leader", "score": 79, "momentum": "up" }, { "month": "2026-05", "position": "leader", "score": 80, "momentum": "up" } ], "evolution_read": "Cloud security posture extends naturally into AI posture as models and agents become part of the attack surface.\n" }, { "vendor_id": "crowdstrike", "category": "commercial_tools", "monthly": [ { "month": "2026-03", "position": "leader", "score": 76, "momentum": "flat" }, { "month": "2026-04", "position": "leader", "score": 77, "momentum": "up" }, { "month": "2026-05", "position": "leader", "score": 78, "momentum": "up" } ], "evolution_read": "Security workflow ownership makes CrowdStrike relevant as AI changes detection, response, and governance boundaries.\n" }, { "vendor_id": "anthropic", "category": "commercial_tools", "monthly": [ { "month": "2026-03", "position": "leader", "score": 74, "momentum": "flat" }, { "month": "2026-04", "position": "leader", "score": 76, "momentum": "up" }, { "month": "2026-05", "position": "leader", "score": 78, "momentum": "up" } ], "evolution_read": "Anthropic's trust posture creates a model-provider benchmark for enterprise AI governance.\n" }, { "vendor_id": "credo-ai", "category": "commercial_tools", "monthly": [ { "month": "2026-03", "position": "specialist", "score": 60, "momentum": "flat" }, { "month": "2026-04", "position": "specialist", "score": 62, "momentum": "up" }, { "month": "2026-05", "position": "specialist", "score": 64, "momentum": "up" } ], "evolution_read": "Pure-play governance remains a specialist category while enterprise buyers decide whether controls live in platforms or standalone systems.\n" }, { "vendor_id": "writer", "category": "commercial_tools", "monthly": [ { "month": "2026-03", "position": "challenger", "score": 68, "momentum": "flat" }, { "month": "2026-04", "position": "challenger", "score": 70, "momentum": "up" }, { "month": "2026-05", "position": "challenger", "score": 72, "momentum": "up" } ], "evolution_read": "Writer is a commercial-tool challenger where enterprise writing workflow depth must defend against model-provider services.\n" }, { "vendor_id": "harvey", "category": "commercial_tools", "monthly": [ { "month": "2026-03", "position": "challenger", "score": 66, "momentum": "up" }, { "month": "2026-04", "position": "challenger", "score": 69, "momentum": "up" }, { "month": "2026-05", "position": "challenger", "score": 72, "momentum": "up" } ], "evolution_read": "Harvey remains a strong vertical specialist because legal workflow depth is harder to collapse into generic model APIs.\n" } ], "positionMonths": [ "2026-03", "2026-04", "2026-05" ], "modelCards": [ { "model_id": "gpt-5-5", "market_role": "Frontier closed reasoning default", "capability_signature": { "reasoning": 5, "coding": 5, "multimodal": 5, "tool_use": 5, "long_context": 4, "cost_efficiency": 3, "openness": 1 }, "operating_envelope": { "deployment": "Hosted API and first-party applications", "openness": "closed", "best_fit": "High-stakes reasoning, agentic coding, multimodal work, and workloads where frontier quality outweighs portability.\n", "poor_fit": "Strict self-hosting, permissive weight access, or workloads optimized only for lowest unit cost.\n" }, "evidence_stack": [ "src-artificial-analysis-models", "src-openai-enterprise-privacy" ], "lifecycle_status": "active_frontier", "drift_watch": "Track price/performance compression from open-weight reasoning models and whether default-agent behavior becomes more important than raw benchmarks.\n", "trust_surface": "Strong enterprise posture through hosted controls; portability and inspectability remain closed-model constraints.\n" }, { "model_id": "claude-opus-4-7", "market_role": "Frontier enterprise reasoning and coding model", "capability_signature": { "reasoning": 5, "coding": 5, "multimodal": 4, "tool_use": 5, "long_context": 4, "cost_efficiency": 3, "openness": 1 }, "operating_envelope": { "deployment": "Hosted API and first-party coding/application surfaces", "openness": "closed", "best_fit": "Long-form reasoning, coding, tool-use workflows, and enterprise contexts that weight safety and trust posture heavily.\n", "poor_fit": "Self-hosted deployments or highly cost-sensitive bulk inference.\n" }, "evidence_stack": [ "src-artificial-analysis-models", "src-anthropic-trust-center" ], "lifecycle_status": "active_frontier", "drift_watch": "Watch whether agentic coding and tool-use reliability remain the primary differentiator as other reasoning models converge.\n", "trust_surface": "Strong trust-center posture; still requires application-level output review and action controls.\n" }, { "model_id": "gemini-2-5-pro", "market_role": "Frontier multimodal reasoning model", "capability_signature": { "reasoning": 5, "coding": 4, "multimodal": 5, "tool_use": 4, "long_context": 5, "cost_efficiency": 3, "openness": 1 }, "operating_envelope": { "deployment": "Hosted API, Google products, and Google Cloud surfaces", "openness": "closed", "best_fit": "Multimodal and long-context workloads, especially where Google Cloud or Google product distribution is already strategic.\n", "poor_fit": "Strict model portability or open-weight governance requirements.\n" }, "evidence_stack": [ "src-artificial-analysis-models", "src-google-cloud-ai", "src-techcrunch-gemini-400m-mau" ], "lifecycle_status": "active_frontier", "drift_watch": "Track how consumer adoption and cloud integration compound into enterprise workflow share.\n", "trust_surface": "Strong cloud control surface; model behavior remains closed and requires independent application evals.\n" }, { "model_id": "deepseek-r1", "market_role": "Open-weight reasoning pressure point", "capability_signature": { "reasoning": 5, "coding": 4, "multimodal": 1, "tool_use": 3, "long_context": 4, "cost_efficiency": 5, "openness": 4 }, "operating_envelope": { "deployment": "Open-weight and hosted ecosystem deployments", "openness": "open_weights", "best_fit": "Reasoning workloads where cost, portability, and self-hosting leverage matter more than first-party product polish.\n", "poor_fit": "Buyers requiring closed-provider indemnity, centralized enterprise support, or native multimodal coverage.\n" }, "evidence_stack": [ "src-artificial-analysis-models", "src-huggingface-models", "src-stanford-ai-index-2025-technical-performance" ], "lifecycle_status": "active_frontier", "drift_watch": "Track whether efficiency gains persist as closed frontier models lower price and latency.\n", "trust_surface": "Openness improves inspection and hosting control; customers inherit more responsibility for safety, deployment, and monitoring.\n" }, { "model_id": "llama-4-maverick", "market_role": "Open ecosystem frontier family", "capability_signature": { "reasoning": 4, "coding": 4, "multimodal": 4, "tool_use": 3, "long_context": 4, "cost_efficiency": 4, "openness": 4 }, "operating_envelope": { "deployment": "Open-weight ecosystem and partner-hosted deployments", "openness": "open_weights", "best_fit": "Enterprise and developer contexts that value ecosystem breadth, inspectability, and deployment flexibility.\n", "poor_fit": "Workloads needing a single vendor to own the full model-operation responsibility boundary.\n" }, "evidence_stack": [ "src-meta-ai-llama", "src-huggingface-models", "src-artificial-analysis-models" ], "lifecycle_status": "active_frontier", "drift_watch": "Track whether open ecosystem gravity converts into application and platform control points.\n", "trust_surface": "Strong portability; customer must own hosting, eval, and policy controls unless using a managed provider.\n" }, { "model_id": "qwen-3-thinking-2507", "market_role": "Open-weight reasoning and regional sovereignty signal", "capability_signature": { "reasoning": 5, "coding": 4, "multimodal": 3, "tool_use": 3, "long_context": 4, "cost_efficiency": 4, "openness": 4 }, "operating_envelope": { "deployment": "Open-weight and Alibaba ecosystem deployments", "openness": "open_weights", "best_fit": "Reasoning workloads where open weights, regional availability, and China-scale ecosystem signals matter.\n", "poor_fit": "Buyers that require US/EU-only supplier posture or closed-provider contractual controls.\n" }, "evidence_stack": [ "src-artificial-analysis-models", "src-huggingface-models" ], "lifecycle_status": "active_frontier", "drift_watch": "Track benchmark parity against US frontier models and enterprise acceptance across regions.\n", "trust_surface": "Openness aids inspection; jurisdiction, hosting, and data governance need explicit buyer review.\n" }, { "model_id": "mistral-large-3", "market_role": "European frontier and sovereignty model", "capability_signature": { "reasoning": 4, "coding": 4, "multimodal": 3, "tool_use": 3, "long_context": 4, "cost_efficiency": 4, "openness": 3 }, "operating_envelope": { "deployment": "Hosted and open ecosystem variants depending on model tier", "openness": "mixed", "best_fit": "European enterprise and sovereignty-sensitive workloads that need strong performance without a single US frontier dependency.\n", "poor_fit": "Workloads that require the absolute top frontier score regardless of jurisdiction or portability.\n" }, "evidence_stack": [ "src-artificial-analysis-models", "src-huggingface-models" ], "lifecycle_status": "active", "drift_watch": "Track whether sovereignty demand produces durable enterprise pull rather than occasional procurement preference.\n", "trust_surface": "Regional positioning helps governance narratives; individual deployment controls still determine practical risk.\n" }, { "model_id": "command-a", "market_role": "Enterprise retrieval and workflow specialist", "capability_signature": { "reasoning": 3, "coding": 3, "multimodal": 1, "tool_use": 4, "long_context": 4, "cost_efficiency": 4, "openness": 1 }, "operating_envelope": { "deployment": "Hosted enterprise model services", "openness": "closed", "best_fit": "Retrieval-heavy enterprise workflows, knowledge applications, and workloads that value business-context fit over raw frontier rank.\n", "poor_fit": "General-purpose frontier reasoning races or open-weight deployments.\n" }, "evidence_stack": [ "src-artificial-analysis-models" ], "lifecycle_status": "active_specialist", "drift_watch": "Track whether specialist enterprise models keep their advantage as frontier models add stronger retrieval and tool-use defaults.\n", "trust_surface": "Enterprise positioning is useful, but buyer-side evals should prove retrieval quality and data handling.\n" }, { "model_id": "grok-4", "market_role": "Frontier challenger with social distribution", "capability_signature": { "reasoning": 5, "coding": 4, "multimodal": 3, "tool_use": 3, "long_context": 4, "cost_efficiency": 3, "openness": 1 }, "operating_envelope": { "deployment": "Hosted API and X-linked application surfaces", "openness": "closed", "best_fit": "Buyers exploring frontier alternatives and consumer/social-context distribution signals.\n", "poor_fit": "Regulated enterprise workflows requiring mature trust documentation and stable procurement channels.\n" }, "evidence_stack": [ "src-artificial-analysis-models" ], "lifecycle_status": "active_frontier", "drift_watch": "Track whether model progress translates into enterprise adoption rather than consumer attention.\n", "trust_surface": "Closed-provider controls apply; enterprise trust posture should be treated as less proven than the leading incumbents until evidence improves.\n" }, { "model_id": "gpt-oss-120b", "market_role": "Open-weight strategic hedge from a closed-model leader", "capability_signature": { "reasoning": 4, "coding": 4, "multimodal": 1, "tool_use": 3, "long_context": 4, "cost_efficiency": 4, "openness": 4 }, "operating_envelope": { "deployment": "Open-weight and hosted ecosystem deployments", "openness": "open_weights", "best_fit": "Portability-sensitive teams that want open-weight leverage without leaving the OpenAI model family narrative entirely.\n", "poor_fit": "Native multimodal workloads or applications that need fully managed frontier hosted behavior.\n" }, "evidence_stack": [ "src-huggingface-models", "src-artificial-analysis-models", "src-openai-enterprise-privacy" ], "lifecycle_status": "active", "drift_watch": "Track whether closed-model leaders use open weights as ecosystem defense and bargaining leverage.\n", "trust_surface": "Open weights shift more operational responsibility to the deployer while retaining strategic familiarity with a leading model provider.\n" } ], "sources": [ { "id": "src-nist-cloud-reference-architecture", "type": "standard", "title": "NIST Cloud Computing Reference Architecture", "url": "https://www.nist.gov/publications/nist-cloud-computing-reference-architecture", "retrieved_date": "2026-05-14", "public": true, "summary": "Reference model for cloud actors and service boundaries; used as the analogy for service-model clarity and shared responsibility in AI.\n" }, { "id": "src-nist-ai-risk-management-framework", "type": "standard", "title": "Artificial Intelligence Risk Management Framework", "url": "https://www.nist.gov/itl/ai-risk-management-framework", "retrieved_date": "2026-05-14", "public": true, "summary": "Public risk-management framework for AI governance, measurement, and trust controls.\n" }, { "id": "src-stanford-ai-index-2025-technical-performance", "type": "report", "title": "AI Index 2025: Technical Performance", "url": "https://hai.stanford.edu/ai-index/2025-ai-index-report/technical-performance", "retrieved_date": "2026-05-14", "public": true, "summary": "Public benchmark trend source for frontier capability, open-vs-closed model convergence, and agentic task performance.\n" }, { "id": "src-stanford-ai-index-2025-economy", "type": "report", "title": "AI Index 2025: Economy", "url": "https://hai.stanford.edu/ai-index/2025-ai-index-report/economy", "retrieved_date": "2026-05-14", "public": true, "summary": "Public source for AI investment, enterprise adoption, and market-scale indicators.\n" }, { "id": "src-artificial-analysis-models", "type": "benchmark", "title": "Artificial Analysis: Models", "url": "https://artificialanalysis.ai/models", "retrieved_date": "2026-05-14", "public": true, "summary": "Live model comparison source for intelligence, price, speed, latency, context, and openness measures.\n" }, { "id": "src-delloro-q1-2025-data-center-capex", "type": "market_research", "title": "Hyperscaler Blackwell and Custom Accelerator Rollouts Drive 53 Percent Capex Growth in 1Q 2025", "url": "https://delloro.com/news/hyperscaler-blackwell-and-custom-accelerator-rollouts-drive-53-percent-capex-growth-in-1q-2025", "retrieved_date": "2026-05-14", "public": true, "summary": "Public data-center capex signal for the infrastructure layer and accelerated-server share.\n" }, { "id": "src-cbinsights-ai-agent-market-map-2025", "type": "market_map", "title": "AI Agent Market Map", "url": "https://www.cbinsights.com/research/ai-agent-market-map/", "retrieved_date": "2026-05-14", "public": true, "summary": "Public agent-market landscape and funding/adoption signal for agentic workflow categories.\n" }, { "id": "src-nfx-generative-ai-five-layer-stack", "type": "analysis", "title": "Generative AI Tech: The 5 Layers", "url": "https://www.nfx.com/post/generative-ai-tech-5-layers", "retrieved_date": "2026-05-14", "public": true, "summary": "Prior-art AI stack framing; useful as a contrast to this reference architecture's service-model and responsibility-boundary approach.\n" }, { "id": "src-techcrunch-gemini-400m-mau", "type": "news", "title": "Google's Gemini AI app has 400M monthly active users", "url": "https://techcrunch.com/2025/05/20/googles-gemini-ai-app-has-400m-monthly-active-users/", "retrieved_date": "2026-05-14", "public": true, "summary": "Public consumer-adoption signal for leading AI applications.\n" }, { "id": "src-techcrunch-microsoft-copilot-20m-paid-users", "type": "news", "title": "Microsoft says it has over 20M paid Copilot users", "url": "https://techcrunch.com/2026/04/29/microsoft-says-it-has-over-20m-paid-copilot-users-and-they-really-are-using-it/", "retrieved_date": "2026-05-14", "public": true, "summary": "Public enterprise-seat signal for AI application adoption.\n" }, { "id": "src-huggingface-models", "type": "directory", "title": "Hugging Face Models", "url": "https://huggingface.co/models", "retrieved_date": "2026-05-14", "public": true, "summary": "Public source for open-model availability, download signals, model cards, and license metadata.\n" }, { "id": "src-menlo-2025-enterprise-ai-report", "type": "report", "title": "2025: The State of Generative AI in the Enterprise", "url": "https://menlovc.com/2025-the-state-of-generative-ai-in-the-enterprise/", "retrieved_date": "2026-05-14", "public": true, "summary": "Enterprise spend and application-layer adoption signal for generative AI.\n" }, { "id": "src-nvidia-investor-relations", "type": "filings", "title": "NVIDIA Investor Relations", "url": "https://investor.nvidia.com/", "retrieved_date": "2026-05-14", "public": true, "summary": "Public filings and earnings material for accelerated-compute revenue and market demand signals.\n" }, { "id": "src-microsoft-investor-relations", "type": "filings", "title": "Microsoft Investor Relations", "url": "https://www.microsoft.com/en-us/Investor", "retrieved_date": "2026-05-14", "public": true, "summary": "Public filings and earnings material for cloud AI, Copilot, and enterprise distribution signals.\n" }, { "id": "src-openai-enterprise-privacy", "type": "docs", "title": "OpenAI Enterprise Privacy", "url": "https://openai.com/enterprise-privacy/", "retrieved_date": "2026-05-14", "public": true, "summary": "Public documentation for enterprise data handling and model-provider trust posture.\n" }, { "id": "src-anthropic-trust-center", "type": "docs", "title": "Anthropic Trust Center", "url": "https://trust.anthropic.com/", "retrieved_date": "2026-05-14", "public": true, "summary": "Public trust-center source for enterprise controls and model-provider governance posture.\n" }, { "id": "src-google-cloud-ai", "type": "docs", "title": "Google Cloud AI and Machine Learning Products", "url": "https://cloud.google.com/products/ai", "retrieved_date": "2026-05-14", "public": true, "summary": "Public source for Google Cloud AI platform and application surfaces.\n" }, { "id": "src-aws-ai-services", "type": "docs", "title": "AWS AI Services", "url": "https://aws.amazon.com/ai/", "retrieved_date": "2026-05-14", "public": true, "summary": "Public source for AWS AI infrastructure, platform, model marketplace, and application services.\n" }, { "id": "src-azure-ai", "type": "docs", "title": "Microsoft Azure AI", "url": "https://azure.microsoft.com/en-us/solutions/ai", "retrieved_date": "2026-05-14", "public": true, "summary": "Public source for Azure AI infrastructure, platform, model, and application services.\n" }, { "id": "src-meta-ai-llama", "type": "docs", "title": "Meta Llama", "url": "https://www.llama.com/", "retrieved_date": "2026-05-14", "public": true, "summary": "Public source for Meta's open-weight model ecosystem and Llama releases.\n" }, { "id": "src-databricks-ai", "type": "docs", "title": "Databricks Data Intelligence Platform", "url": "https://www.databricks.com/product/data-intelligence-platform", "retrieved_date": "2026-05-14", "public": true, "summary": "Public source for Databricks' data and AI platform positioning.\n" }, { "id": "src-snowflake-ai", "type": "docs", "title": "Snowflake AI Data Cloud", "url": "https://www.snowflake.com/en/data-cloud/workloads/ai-ml/", "retrieved_date": "2026-05-14", "public": true, "summary": "Public source for Snowflake's AI and data-platform positioning.\n" }, { "id": "src-pinecone-docs", "type": "docs", "title": "Pinecone Documentation", "url": "https://docs.pinecone.io/", "retrieved_date": "2026-05-14", "public": true, "summary": "Public source for managed vector database, retrieval, and AI-ready data patterns.\n" }, { "id": "src-weaviate-docs", "type": "docs", "title": "Weaviate Documentation", "url": "https://weaviate.io/developers/weaviate", "retrieved_date": "2026-05-14", "public": true, "summary": "Public source for vector search, hybrid search, and retrieval patterns.\n" }, { "id": "src-zilliz-milvus-docs", "type": "docs", "title": "Zilliz and Milvus Documentation", "url": "https://milvus.io/docs", "retrieved_date": "2026-05-14", "public": true, "summary": "Public source for open vector database and high-scale retrieval patterns.\n" }, { "id": "src-mongodb-vector-search", "type": "docs", "title": "MongoDB Atlas Vector Search", "url": "https://www.mongodb.com/products/platform/atlas-vector-search", "retrieved_date": "2026-05-14", "public": true, "summary": "Public source for operational datastore plus vector search positioning.\n" }, { "id": "src-elastic-vector-search", "type": "docs", "title": "Elastic Vector Search", "url": "https://www.elastic.co/what-is/vector-search", "retrieved_date": "2026-05-14", "public": true, "summary": "Public source for hybrid/vector search over operational data.\n" }, { "id": "src-brianletort-ai-capability-postures", "type": "analysis", "title": "Three Postures of AI Capability", "url": "https://brianletort.ai/blog/ai-capability-three-postures", "retrieved_date": "2026-05-14", "public": true, "summary": "Public local framing for Chat, Build, and Automate postures, including Hermes, Zeroclaw, Agent Zero, and openclaw-style agent estates.\n" }, { "id": "src-writer-ai", "type": "docs", "title": "Writer", "url": "https://writer.com/", "retrieved_date": "2026-05-14", "public": true, "summary": "Public source for enterprise writing and knowledge-work AI positioning.\n" }, { "id": "src-harvey-ai", "type": "docs", "title": "Harvey", "url": "https://www.harvey.ai/", "retrieved_date": "2026-05-14", "public": true, "summary": "Public source for legal AI specialist positioning.\n" }, { "id": "src-heygen", "type": "docs", "title": "HeyGen", "url": "https://www.heygen.com/", "retrieved_date": "2026-05-14", "public": true, "summary": "Public source for AI video generation specialist positioning.\n" }, { "id": "src-glean", "type": "docs", "title": "Glean", "url": "https://www.glean.com/", "retrieved_date": "2026-05-14", "public": true, "summary": "Public source for enterprise search, assistant, and knowledge-work AI positioning.\n" } ] }