AI Data
Pipelines, embeddings, vector stores, retrieval, labeling, and synthetic data.
Stack position
Where AI Data sits in the AI stack
L12 · Foundation Model
Frontier and open models across text, vision, audio, and reasoning; revenue is token-metered.
L13 · AI Data
Pipelines, embeddings, vector stores, retrieval, labeling, and synthetic data.
L14 · AI Platform
MLOps/LLMOps, model gateways, evaluation, and deployment tooling.
L15 · AI Agent
Agent runtimes, tool calling, MCP, memory, planning, and multi-agent orchestration.
Icons mark layer roles — not company logos. Position only; not a price chart.
Why this layer matters
A model without an organisation's own data answers generically. The path runs enterprise data → pipeline → embedding → vector store → retrieval → model, and most enterprise AI value is unlocked on that path rather than in the model itself.
What is the bottleneck?
Data readiness, not model capability, is usually what stalls an enterprise deployment.
A structural observation about supplier concentration and lead times — not a view on any security.
Who sits in this layer?
This layer is left empty on purpose. Assigning a company to it would be our opinion rather than a statement about the company's primary business, and we would rather publish nothing than publish a classification we cannot defend as fact.
Coverage boundary
Coverage boundary: US-listed tickers or ADRs only (same rule as TSM / ASML). Important suppliers without a US listing are omitted even when they matter to the stack. That is a coverage rule, not a quality ranking.
No hand-listed notable omissions for this layer yet. The US-list / ADR rule still applies.
Checked 2026-10-09. Not exhaustive.
Same track
Descriptive positioning only. The positioning label says whether a company's business is exposed to a layer, never whether to buy it. Layers with no exposure are not recommendations to add any.