Three weeks to a data agent your business trusts.

Your BigQuery agent is already running but it  doesn’t know your data well enough to be trusted yet.
We fix that. In a fixed scope, on a fixed price.

Book a discovery call

Why your agent isn't
there yet.

Conversational analytics is built into BigQuery. Every warehouse has it — but most teams don't trust it. Here's why.

01
Metadata is thin or stale.

Column descriptions are missing. Table comments are outdated. Synonyms aren't mapped. The agent guesses — and sometimes it guesses wrong.

02
Conventions live in people's heads.

Which date column is the source of truth? What's the canonical channel hierarchy? Your analysts know. The agent doesn't.

03
No examples to learn from.

The model has never seen a correct answer for your business. So it will give you answers that might look confident — but not surely true.

“

The proof of concept demonstrated how AI agents can already address today's analytical questions.

”
Hans Heidenreich
Global Head of Financial Planning,
Triumph International

In four moves, we close all gaps the default agent would leave open.

01
Review existing metadata.

We audit table descriptions, column docs, lineage, and ownership. Flag what's missing, what's wrong, what's misleading. The agent can only be as good as what it can read.

02
Extract conventions from real queries.

We mine your regularly running queries to surface the joins, filters, and date logic your team already trusts, and codify them so the agent uses the same logic every time.

03
Create golden queries.

A curated set of question-and-answer pairs: reference examples the agent learns from in every future use. Real-life questions your business asks.

04
Prove accuracy with a QA framework.

A qualitative assessment that grades agent answers from simple to complex. You see exactly where it's strong, and where the limits are.

3 weeks. 1 domain.
Fixed price.

Platform: BigQuery + Gemini Google Cloud fund-eligible

Week 01
Discover.

Inventory the chosen domain. Read the metadata. Profile the regularly running queries. Interview the analysts.

Output: Gap map→
Week 02
Configure.

Enrich metadata. Codify conventions. Author golden queries. Stand up the QA harness with stakeholder questions.

Output: Configured agent→
Week 03
Validate & hand over.

Run the QA suite end to end. Score answers, fix what fails, document what's left. Train the business users.

Output: QA report→

What you walk away with

– A configured data agent, ready for business users. Tuned against your metadata, conventions, and classic queries. Live in the chosen domain from day one of week four.
– A QA report from simple to complex — a graded set of questions across the domain, with the agent’s answer, the expected answer, and the verdict for each.
– Updated metadata for future agentic use. Descriptions, synonyms, and conventions written back to your warehouse. Every future agent starts smarter because of this work.
– Platform optimization findings via a bundled Rabbit trial. Rabbit scans your BigQuery footprint for cost, performance, and hygiene wins. Enabled at kickoff.

Which domain do you start with?

The discovery call is 20 minutes. We’ll identify which business domain has the most to gain, confirm the three prerequisites, and tell you exactly what the three weeks look like.

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