Claude + BigQuery

Claude + BigQuery isn't enough to get answers your team trusts

it takes more than a connector

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Trusted by brands making $2M - $100M

The fix isn’t a better prompt. It’s a governed context layer

iQ sits between Claude and BigQuery. It trains on your schema, your definitions, your logic. So every query is grounded in what your numbers actually mean.

Bring context to your queries, book a demo

WHERE ARE YOU TODAY?

Three ways to connect Claude to your data

EXPLORING

Claude + BigQuery MCP

Direct warehouse access. Powerful for technical analysts running ad-hoc queries
and deep dives.

No shared metric definitions
Hallucinations on ambiguous tables
Breaks when schema changes

Best for: Technical analysts · Data scientists · One-off exploration

Recommended

IQ MCP + Claude

iQ serves as the governed context layer between Claude and your warehouse. Every query grounded in certified definitions and your logic.

Consistent answers across prompts
Schema-aware queries
Certified metric layer

Best for: Teams already on Claude who want accuracy without switching tools

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Full Platform

Saras iQ

One trusted number across every team.
Trained and maintained by your Saras POC. No technical knowledge needed.

Orchestration + scheduling
Role-based access control
Full governance out of the box

Best for: Business teams. Leadership · Org-wide rollout

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Claude + BigQuery alone isn’t enough

These aren't edge cases. They're what happens when AI meets data without a governed layer.

Same question, different answers

No shared metric definitions. Two users, two prompts, two numbers.

Hallucinations that look like data

Wrong table, wrong filter, fabricated column. Still sounds confident.

Someone has to maintain context forever

Schema docs, business logic, metric updates. A full-time job nobody signed up for.

No way to know when it goes wrong

No validation. No feedback loop. Errors compound silently.

Connect a Source

Frequently Asked Questions

We already have Claude on BigQuery. Why change?

iQ sits between Claude and BigQuery. It trains on your schema, your definitions, your logic. So every query is grounded in what your numbers actually mean.

Is historical DCL Logistics data supported?

iQ sits between Claude and BigQuery. It trains on your schema, your definitions, your logic. So every query is grounded in what your numbers actually mean.

How are schema changes handled?

iQ sits between Claude and BigQuery. It trains on your schema, your definitions, your logic. So every query is grounded in what your numbers actually mean.

Is my DCL Logistics data secure?

iQ sits between Claude and BigQuery. It trains on your schema, your definitions, your logic. So every query is grounded in what your numbers actually mean.

Can I customize fields or extend schemas?

iQ sits between Claude and BigQuery. It trains on your schema, your definitions, your logic. So every query is grounded in what your numbers actually mean.