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 schema, statistics, hygiene categories, descriptions, lineage, joins, entities, and the most-used events and attributes, and performs initial validation on them.
- Schemantic is SOC 2 Type II attested on all 5 TSCs and operates with near-zero raw data egress, read-only analysis 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.”
OpenAI built its in-house data agent on multiple layers of context — even strong models need it to get data right.
“High-quality answers depend on rich, accurate context. Without context, even strong models can produce wrong results, such as vastly misestimating user counts or misinterpreting internal terminology.”
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 the read-only access needed for analysis. Warehouse scans and statistical calculations run inside your cloud environment.
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Step 2: Generate context
Schemantic automatically infers your semantic layer, domain ontology, and statistical structure from things like your data, documentation, and query history — often in hours.
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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.
The Schemantic team and tool were incredibly helpful and effective in reviewing and improving the BEAVER benchmark. Schemantic helped us find hundreds of errors in our manually curated gold SQL set, and organized them into a working ontology of issues. The fixes I examined were accurate, and the corrected queries kept their full complexity and intention. The result was a cleaner benchmark for teams participating in BEAVER. Impressive team and tech, and I learned a lot in the process.
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.
Near-zero raw data egress
Warehouse scans and computation virtually exclusively run inside your cloud environment — Schemantic does not copy source datasets out to perform them. Learn more.
Tenant isolation
Tenant isolation — no data co-mingling across customers. Deleted after subscription termination on scheduled purge cycles; backups clear on their own cycles.
SOC 2 Type II attested
SOC 2 Type II attested across all five Trust Service Criteria.
Read-only analysis access
Generally, read-only, service account credentials for analysis (e.g., OAuth 2.0 / IAM).
Schemantic operates in most popular cloud warehouses.
Schemantic reads from your warehouse via read-only credentials for analysis — 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 what — 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 data does Schemantic access?
Schemantic connects with read-only service account credentials for analysis, and the scans and statistical calculations that read your table values run inside your cloud environment. Structural metadata and statistics are stored outside it in a tenant-isolated data plane so the web interface can render; results that are themselves table values may be stored in your environment or by Schemantic.
Operational records — logs, telemetry, traces, diagnostics, cached responses, and similar records — are kept outside your environment to run and support the product. Cross-cloud and AI features change what leaves, and only if you turn them on.
For the full answer, including what the Terms of Service commit us to, see How does Schemantic securely process your data?
FAQ 3 ▸ 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.
FAQ 4 ▸ 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 [email protected].
FAQ 5 ▸ 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 analysis 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.