Schemantic automatically generates robust context layers for your data warehouses.
- Context layers are required to make enterprise relational data legible to AI — to encode what the data means, how its tables join, and which entities, events, and attributes it describes.
- Schemantic automatically infers and validates schema, statistics, hygiene categories, descriptions, lineage, joins, entities, and the most-used events and attributes.
- Schemantic is SOC 2 Type II attested on all 5 TSCs and operates with no raw data egress, read-only access, and tenant data plane isolation.
The Frontier Agrees: Context Layers Are Essential, and LLMs Do Not Build Them Effectively
Anthropic settles the case for agent-ready data warehouse preparation.
“The most important aspect of ensuring analytics agents are accurate is via strong data foundations, which include the data models, transforms, tests, and tables in a data warehouse, along with the metadata describing them.”
“Standard data engineering and data quality practices such as dimensional modeling, shift-left testing, freshness and completeness checks on critical pipelines all still apply (and we won’t re-litigate these).”
Palantir echoes Anthropic: LLMs need material data preparation.
“Pointing an LLM at hundreds of disconnected, ungoverned databases gets you a system that hallucinates, is insecure, and unauditable.”
Take it from a Turing Award winner: Text-to-SQL on an unprepared warehouse severely underperforms.
“We tried out various LLMs on the MIT data warehouse and we got accuracy of zero. Not low—zero.”
“…if your database has any of these four characteristics—not public, idiosyncratic, semantic overlap, and complicated queries—I’m not optimistic that we’re going to get anywhere using an LLM.”
Schemantic goes further than the leading edge methods for context creation.
| CODE | SIGNAL SOURCE | METHOD |
|---|---|---|
| Method #1 | Schema / DDL | Schema metadata extraction |
| Method #2 | Query logs | Query history mining |
| Method #3 | Source repositories | Codebase crawling |
| Method #4 | Docs / wikis / people | Institutional knowledge processing |
| Method #5 | People | Human annotation |
| Method #6 | Feedback over time | Memory loop that persists corrections |
Where traditional methods struggle to generate context layers
Schemantic provides rapid and robust context layer generation
Schemantic delivers your semantic layer, domain ontology, and statistical structure as quickly as a few hours.
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Step 1: Set up identity
We guide you to create a dedicated service account with workload identity federation and read-only access. Schemantic operates inside your VPC — no shared credentials, no raw data egress.
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Step 2: Generate context
Schemantic automatically infers your semantic layer, domain ontology, and statistical structure from your raw data, documentation, and query history — in hours. The catalog lives inside your environment.
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Step 3: Review & enrich
Humans review, edit, and approve metadata via a real-time collaborative web app. Tribal knowledge enters the catalog in hours — not months or years.
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Step 4: Serve agents & analysts
Agents access virtually the same context as your analysts — through an MCP. Analysts have a UI. Both inherit access from existing warehouse credentials.
A Few Testimonials About Schemantic
Our concern with automated inference was the tradeoff: completeness, precision, or trust, pick two. Schemantic didn’t force the tradeoff… The time savings with Schemantic were significant. A trusted data architect scoped the warehouse migration alone (without a semantic layer) at well over 700 hours. With Schemantic we completed that work, plus a full semantic layer across hundreds of tables, in roughly 100 hours, without lowering the bar for rigor. That changes how fast we can stand up canonical and reliable semantic layers for our warehouses.
Schemantic is the perfect tool for overcoming the challenge of efficiently building (and maintaining) both reliable and verifiable context layers. Most organizations have the data, but the meaning is scattered across schemas, queries, documentation, and team knowledge. Schemantic helps turn that fragmented context into a usable layer by discovering joins, entities, events, definitions, and aliases directly from the warehouse.
Most tools tell you they can make enterprise data easier to understand. Schemantic showed it.
The moment I saw the join map, it clicked. This is the problem analysts like myself deal with every day: once you get outside the handful of datasets your own team knows well, figuring out the right joins becomes a free-for-all. You are relying on tribal knowledge, scattered validation, and whatever someone believes is the source of truth. Schemantic turned that hidden mess into clear context: the relationships, the evidence behind them, and the human review path that makes them trustworthy.
If you run or manage a data warehouse and need it to be performant for everything from simple visualizations all the way up to AI training and inference, Schemantic is a must-have. It fits easily into your data processes to clean messy data, create vital joins, and add context layers that ensure your outputs are defensible, high-quality, and trustworthy.
Schemantic offers enterprise security.
No raw data egress
Row-level data and table contents — including values like minimums, maximums, and modes — do not leave your cloud. Where the UI displays such a value, it is passed through at render time and never stored externally.
Strict tenant isolation
Strict tenant isolation — no data co-mingling across customers. Deleted after subscription termination.
SOC 2 Type II attested
SOC 2 Type II attested across all five Trust Service Criteria.
Read-only access
Read-only, per-workspace service-account credentials (OAuth 2.0 / IAM). No write access requested or required.
Compute in your cloud
All calculations on raw data execute inside your cloud environment.
Encrypted metadata only
Only structural metadata and aggregate statistics (schemas, types, null rates, row counts, join scores) are temporarily held outside your cloud — encrypted at rest and in transit, hashed where applicable.
Schemantic operates in most popular cloud warehouses.
Schemantic reads from your warehouse via read-only credentials — Snowflake, Databricks, Azure Databricks, BigQuery, and Redshift. Compute runs inside your cloud environment.
- GOOGLE CLOUD
- AWS
- AZURE
- SNOWFLAKE
- DATABRICKS
- AZURE DATABRICKS
Common questions.
For the full FAQ and product documentation, see FAQ and Docs.
FAQ 1 ▸ What is a context layer?
A context layer is most of, perhaps everything, an analyst or AI agent needs to understand your warehouse — not just what to query, but what the data means, how it connects, and whether it's reliable. A semantic layer is one piece of that: it defines things like business metrics, validated join paths, access controls, and terminology in a governed model. A full context layer adds data quality signals, entity resolution across source systems, lineage metadata, and patterns discovered from query history. Applications query the context layer instead of raw tables, eliminating ambiguity and improving consistency.
FAQ 2 ▸ What does "find virtually every valid join" mean?
Schemantic finds virtually every valid join between any two columns referencing the same real-world entity. We estimate 99.9%+ capture for most customers. Edge cases: creatively mangled IDs with random inserted characters, true decimal ID values without common naming conventions, and column pairs where both have highly unfavorable statistical properties. If you spot a missed join, email hello@schemantic.io.
FAQ 3 ▸ What data does Schemantic access?
Schemantic accesses your warehouse via read-only, per-workspace service-account credentials (OAuth 2.0 / IAM), and all calculations on raw data execute inside your cloud environment. Row-level data and table contents — including values like minimums, maximums, and modes — do not leave it; where the UI displays such a value, it is passed through at render time and never stored externally. The only information temporarily held outside your cloud is structural metadata and aggregate statistics (schemas, types, null rates, row counts, join scores) — encrypted at rest and in transit, hashed where applicable, held under strict tenant isolation with no co-mingling across customers, and deleted after subscription termination. Schemantic is SOC 2 Type II attested across all five Trust Service Criteria.
FAQ 4 ▸ Do I need standard naming, foreign keys, or documentation?
No. Schemantic is built for imperfect, real-world data environments. You do not need consistent naming conventions, explicit foreign keys, or well-maintained documentation. The system uses statistical, analytic, and natural-language-based checks to identify and score joins even when columns look mismatched or contain missing values.
FAQ 5 ▸ How long does deployment take?
Hours from initial authorization to viewing valid joins for many use cases. Large or complex datasets may take a few days. Extremely large environments (dozens or hundreds of terabytes) may take proportionately longer.
For petabyte-scale or further clarification, contact hello@schemantic.io.
FAQ 6 ▸ What is the MCP server?
Schemantic exposes the semantic layer as a Model Context Protocol (MCP) server. AI agents connect through a typed, discoverable interface to query available metrics, retrieve validated join paths, and generate SQL — without parsing raw DDL or relying on prompt-injected schema context.
Build a better semantic layer, ontology, and statistical structure.
Connect Schemantic to your warehouse and validate the context layer in as little as a few hours.
REQUEST DEMO- 01 Brief intro call with the team and paperwork.
- 02 Read-only credentials provisioned by your team.
- 03 Schemantic runs against a slice of your data.
- 04 Reviewer who knows the warehouse confirms the semantic layer, ontology, and statistical structure.
- 05 Enjoy your new semantic layer, ontology, and statistical structure.