Does Symbric Context move or write my data?

The layer reads. It never writes to your database or warehouse.

Is my data used for training?

We never train models on your data. Model calls run under your own key.

What does it plug into?

Warehouses: Snowflake, Databricks, BigQuery, Postgres. Systems of record: Salesforce, Stripe, NetSuite, HubSpot. Agents and MCP clients: Claude Code, Cursor, ChatGPT, any MCP client.

The context layer for your data

What your business means, in one place.

Q: Who owns the context layer?

You do. Every definition lives as a plain, versioned file held in the customer's own repository and warehouse, readable without Symbric. That makes it an asset the customer owns outright.

Symbric Context is a context layer that holds what your business means: what counts as an active customer, how revenue is measured, which system holds which fact, and who is allowed to see it. Assembled from the systems you already run. You own it.

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Lower maintenance. More accurate answers. More confidence to act.

01

Lower maintenance cost

Symbric detects product, schema, and definition changes, then revalidates the context they affect. Your team spends less time tracking drift and repairing stale definitions.
02

More accurate AI answers

Symbric reads business logic across your warehouse, codebase, queries, dashboards, documents, and spreadsheets. Agents reason from the full business picture.
03

Answers people can trust

Every answer shows where it came from and which checks it passed, so your team can verify it before acting.

Your systems feed it. Every operation reads it.

Systems of record

Database

CRM

ERP

Billing

Documents

Spreadsheets

Business Context EngineRead before the work runs

DefinitionsWhat counts as an active customer, and at what grain.

MetricsHow revenue is measured, and which month it lands in.

Where each fact livesWhich system to believe when two disagree.

Working knowledgeWhich cases are exceptions, and who decided.

PoliciesWhat needs approval, and above which limit.

Access rulesWho can see what, and what is masked before a model sees it.

Versioned in your repository, reviewed like codeRe-validated when a column, table or upstream definition changes

Symbric AI core

Gather
Reconcile
Investigate
Decide
Execute or escalate

Systems of action, in the scope you agree to

CRM

ERP

Billing

Ticketing

It reads what's running. Nothing moves first.

Schemas, table relationships, the queries your team already writes, and the business logic sitting in code, dashboards and spreadsheets: Symbric reads all of it and reconciles it into one model. Nothing has to move first.

Every definition is a file you can read.

Entities, join paths, caveats and permissions each live as a plain, versioned file: reviewable, diffable, testable, held in the customer's own repository and warehouse. Readable without us. That's what makes it an asset the customer owns outright.

context/entities/customer.ymlpull request #58

entity customer:
  grain: account_id
- active_if: last_login_at within 30d
+ active_if: subscription.status = 'active'
  owner: revops

This is the job a semantic layer does. Symbric builds it from the systems you already run and leaves you owning the files.

A defined metric covers a fraction of what you know.

The rest is working knowledge: what counts as enterprise, which quarter an exception applied to, why one number excludes a category. Context captures it where the work happens, in the tools the team already uses.

That makes the context self-improving: every question, correction, and confirmed rule improves the next answer for every tool and teammate.

It notices when the ground moves.

Columns get added. Tables get renamed. Definitions move to a different system. None of it waits for a person to notice.

detecta column, table or upstream definition changes
re-readevery table and query the change touches
re-validatethe definitions that depend on it
refreshevery tool is current on its next call

Access is governed the same way definitions are.

Access is governed by your own roles and row scoping, personal data is masked before it reaches a model, and writes to the layer are logged.

What it plugs into.

Warehouses

Snowflake
Databricks
BigQuery
Postgres