Schemantic builds context layers — rigorously, rapidly, and verifiably.
▸ Data catalog. Context layer. Semantic layer. Three names for a living meta data artifact — essential to analysts and AIs alike.
▸ From your data and query history, Schemantic automatically generates a full context layer — including schema, statistics, hygiene categories, descriptions, lineage, joins, and entities, and the most-used events and attributes.
▸ With Schemantic, enjoy your first robust, auditable, and easily maintained data catalog for your warehouse (in as little as an hour).
Schemantic handles your data mess.
We build your context layer even if primary or foreign keys are missing, column names are mismatched or mistyped, joins were never documented, values are mixed with nulls and placeholders, or the semantic layer is half-finished — or missing entirely.
This is a tiny fraction of what we handle:
Context layers as quickly as a few hours.
Manual catalog projects can take months or years to reach completeness, if ever. Schemantic delivers the full nine-element layer with up to 99% less setup time than manual approaches.
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.
Schemantic automatically generates the nine-element context layer from your raw data and query history — in hours. The catalog lives inside your environment.
Humans review, edit, and approve metadata via a real-time collaborative web app. Tribal knowledge enters the catalog in hours — not months or years.
Agents access the same context your analysts have — automatically, through MCP. Both inherit access from existing warehouse credentials.
The full nine-element catalog — schema, statistics, hygiene, descriptions, lineage, joins, entities, events, attributes — produced automatically.
▸ Applies to typical 50th-percentile enterprise warehouse — e.g.: ~1,000 tables, ~10,000 columns, ~10 TB.
The context layer is foundational to enterprise AI.
In February 2026, OpenAI announced Frontier, naming "a semantic layer for the enterprise" as its foundational pillar. A few days earlier, OpenAI's data team published "Inside OpenAI's in-house data agent," describing how they painstakingly built six context layers on their internal data for agents and analysts using six methods: schema metadata extraction, query history mining, human annotation, codebase crawling, institutional knowledge processing, and a memory loop that persists corrections.
Leading edge methods are effective and limited.
| CODE | METHOD | SIGNAL SOURCE |
|---|---|---|
| Method-01 | ▸ Schema metadata extraction | Schema / DDL |
| Method-02 | ▸ Query history mining | Query logs |
| Method-03 | ▸ Codebase crawling | Source repositories |
| Method-04 | ▸ Institutional knowledge processing | Docs / wikis / people |
| Method-05 | ▸ Human annotation | People |
| Method-06 | ▸ Memory loop that persists corrections | Feedback over time |
These methods are effective and effortful, and some are probabilistic, leading to increased errors, less coverage, or both — particularly in diverse and difficult enterprise data scenarios.
A more powerful method for context layer creation.
We propose an incremental and more effective method, which introduces deterministic, multi-layered pre-processing. Through multi-layered statistical inference — combined with traditional methods such as query and usage mining — Schemantic generates more accurate and complete context layers in enterprise data warehouses. Schemantic is covered in part by 50+ patent claims granted by the USPTO.
Schemantic builds schema, statistics, hygiene, descriptions, lineage, joins, entities, and the most-used events and attributes — in as little as an hour, typically saving several hundred to thousands of hours of manual effort per warehouse.
The catalog in addition to traditional techniques like query mining, leverages multi-layered deterministic pre-processors that include over 200 bespoke algorithms and metrics. LLMs are used primarily for specific semantic assessments and virtually every element remains auditable to the data that produced it.
LLMs fail on raw data.
Without a maintained context layer, AI reliability collapses on real-world schemas, yielding only 15% to 45% accuracy on enterprise benchmarks. With one, accuracy can climb up to 92.5% in third-party research.
AI accuracy collapses on real-world schemas. Agents hallucinate joins, miss entities, and misread metrics — the failure mode is silent.
Accuracy climbs to enterprise-grade in third-party research. The catalog anchors every answer in named, validated structure.
The nine context layer elements
Here's what Schemantic helps you ship — most automatically and a few with limited review and validation. Five metadata elements describe your data; four query-able objects compose into queries.
| ID | ELEMENT | DESCRIPTION |
|---|---|---|
| Element-01 | ▸ SCHEMA | Virtually every table, column, and type inventoried automatically. |
| Element-02 | ▸ STATISTICS | Over 100 different descriptive stats calculated across columns, tables, and joins. |
| Element-03 | ▸ HYGIENE | 15+ quality categories surfaced at four grains. |
| Element-04 | ▸ DESCRIPTIONS | Generated descriptions for most tables and publicly-documented columns. |
| Element-05 | ▸ LINEAGE | Whole-warehouse upstream and downstream, derived from query history. |
| Element-06 | ▸ JOINS | Virtually every valid join between column pairs from different tables, confidence-scored. |
| Element-07 | ▸ ENTITIES | Virtually every entity in your warehouse, mapped — even when the same object lives in dozens of tables. |
| Element-08 | ▸ EVENTS | How entities interact and when, proposed from your historic activities. |
| Element-09 | ▸ ATTRIBUTES | What characterizes each entity, proposed from your historic activities. |
Find quality issues before something breaks.
Acquisitions, team turnover, platform migrations, years of organic growth — every enterprise warehouse accumulates quality issues that no metadata query alone can find.
- ▸ Missing, null, or duplicate primary keys
- ▸ Duplicate tables
- ▸ Date gaps
- ▸ Orphaned tables
- ▸ Mistyped columns
- ▸ All or mostly null columns
- ▸ Implausible values
- ▸ Single-value columns
- ▸ Broken joins
- ▸ Mixed-type joins
- ▸ Mismatched column names
- ▸ Transforms required
- ▸ Inconsistent column names
- ▸ Inconsistent types
- ▸ Orphaned entities
Reduce inaccuracies from your agents.
Schemantic exposes the context layer via MCP. Agent answers are anchored in a maintained catalog, so there is drastically less information the agent has to calculate, guess, or hallucinate on the fly.
The catalog reduces low-accuracy guessing by pre-computing the joins, entities, events, and attributes the agent would otherwise have to derive (potentially inaccurately) on every query.
No raw data egress.
Schemantic compute runs against your warehouse. SOC 2 Type II attested across all five Trust Service Criteria.
Where we operate.
Schemantic reads from your warehouse via read-only credentials — Snowflake, Databricks, Azure Databricks, BigQuery, and Redshift. Compute runs inside your cloud environment.
Schemantic can save you money.
We can take most of the catalog tax off your team's plate.
Even when teams don't put dedicated headcount against catalog work, the upkeep is a tax on everyone's time. The math below is typical Total Cost of Ownership across tools and time, not a worst case.
| LINE ITEM | ANNUAL COST |
|---|---|
| Common industry catalog tool license cost per year | $150K — $300K |
| 2-3 FTE fully loaded cost at $145K–$185K | $290K — $555K |
| TOTAL PER YEAR · LEGACY | $440K — $855K |
Common questions.
For the full FAQ and product documentation, see FAQ and Docs.
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.
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.
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.
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.
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.
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 context layer.
Connect Schemantic to your warehouse. Validate the catalog in as little as a few hours. Measure the before and after.
- 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 context layer.
- 05 Enjoy your new context layer.