SemanticFed
Launching 2026 · Join the waitlist

Query all your data in place — without moving it

SemanticFed is the data federation platform that unifies your databases, warehouses, lakes, and APIs behind one governed semantic layer. Write one query, get one answer — with access policy, lineage, and audit built in.

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  • Zero-copy federation
  • Governed semantic layer
  • Policy at the query edge
  • Trustworthy AI context
A conceptual diagram of a central federation hub linking databases, a warehouse, a data lake, and APIs into one unified query layer

One query interface across every source

  • PostgreSQL
  • MySQL
  • Snowflake
  • BigQuery
  • S3 / Object storage
  • Parquet & Iceberg
  • REST & GraphQL APIs
  • Redshift
  • MongoDB
The federation approach

Stop copying your data just to ask a question

Most analytics stacks answer questions by moving data — pipeline after pipeline, copy after copy, each one a thing to schedule, monitor, and reconcile. SemanticFed takes the opposite path: it queries data where it already lives.

No data movement by default
Live results, not stale copies
Push-down for speed and low cost
Governance in one place
Browse use cases See how it works
An abstract visualization of a federated query plan pushing work down to multiple data sources and merging the results

Built for the whole data team

Whether you analyze data, engineer it, or govern it, SemanticFed gives you one consistent, governed way to reach every source.

For Analytics Teams

Query every source through one governed semantic model. Define a metric once and have it mean the same thing in every dashboard, notebook, and API.

Browse use cases

For Data Engineering

Retire brittle ETL with virtual, federated views. A cost-aware planner pushes work to the source so you move bytes, not terabytes.

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For Governance & Platform

Row- and column-level policy, masking, lineage, and a full audit trail enforced at the query edge — across every source, for every tenant.

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One platform, end to end

Everything you need to federate, model, govern, and serve your data — in a single control plane.

Federated Query Engine

A cost-aware planner that pushes filters, projections, and aggregations down to each source and joins the results — no copy required.

Governed Semantic Layer

Model entities, dimensions, and metrics once in a typed layer that every tool, notebook, and AI agent resolves consistently.

Policy at the Edge

Row filters, column masking, and access rules defined once and enforced on every federated query, whatever source the data lives in.

Trustworthy AI Context

Point agents and copilots at the semantic layer so answers reason over documented metrics with access policy applied — not raw tables.

Lineage & Observability

End-to-end lineage and per-query plans show what ran where, what was pushed down, and where every number came from.

Connect Anything

Databases, cloud warehouses, object-storage lakes, and SaaS APIs register as federated sources behind one SQL and GraphQL interface.

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A conceptual illustration of an AI agent reasoning over a structured semantic layer above many data sources
Semantic layer for AI

Give your AI a source of truth

Agents pointed at raw tables guess at schemas and ignore access rules. Point them at SemanticFed instead: a typed semantic layer of documented metrics, with the same policy enforced on the agent as on a human — so answers are grounded, reproducible, and safe to act on.

  • Agents query documented metrics, not mystery columns
  • Access policy and masking apply to every generated query
  • Every answer is inspectable back to its query plan
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Problems we solve

Data teams adopt SemanticFed because these everyday blockers stop being blockers.

Data lives in too many places to query together

SemanticFed registers databases, warehouses, lakes, and SaaS APIs as federated sources and joins them in one query — without moving the data.

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Every dashboard reports a different number for the same metric

Define entities, dimensions, and metrics once in the governed semantic layer so every tool resolves the same definition.

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Governance fragments across sources and tools

Row filters, column masking, and access rules are enforced at the query edge across every federated source.

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Federated queries are slow and expensive

A cost-aware planner pushes filters, projections, and aggregations down to each source so you move bytes, not terabytes.

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AI agents reason over raw tables and guess wrong

Point agents at the typed semantic layer so they query documented metrics with policy applied — grounded, reproducible, and safe.

See the story

What we're building

SemanticFed is launching in 2026. Here is the platform we are bringing to data teams.

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Semantic layer
6
Use-case areas
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Copies to maintain
2026
Launching

Ready to unify your data?

Be among the first data and platform teams to run SemanticFed when we launch in 2026.

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Launching 2026 • Sign up for early access