Copies everywhere
Every AI project makes its own extract, and none of them match.
What waiting costs: answers that disagree depending on who asked.
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- Vertex AI, BigQuery and data
Vertex AI, BigQuery and data
An agent is only as trustworthy as the data it reads. Can you show where that data came from?
We put models on Vertex AI and BigQuery where the data already lives, with data access, schema governance and lineage designed in the first sprint.
Illustrative exampleOne answer, traced.
- QuestionWhich accounts are past due?
- Source tableGoverned copy, current to this morning
- AccessRole allowed. Personal fields masked.
- LineageRecorded from source to answer
A model you cannot trace back to its data is an opinion, not an answer.
Where you are
Your data is in BigQuery or moving there, the reports run, and now a model or an agent wants to read the same tables on its own. The question has changed from what the numbers say to where an answer came from.
Retrieval nobody owns
Agents ground on documents with no owner, no expiry and conflicting versions.
What waiting costs: confident answers from stale sources.
No evaluation
Models go live without a test set anyone agreed.
What waiting costs: drift you discover from a complaint.
What is in the way
The data platform was built for reports, not for agents that read it on their own.
For the last answer your AI gave a customer, which table and which version did it come from?
How we clear it
From the answer back to the source
- 01
Governed data in place
Access, masking and schema governance on BigQuery so agents read the source, not copies.
You hold: one governed copy with an owner.
- 02
Retrieval readiness
Golden sources, expiry and ownership for the documents agents ground on.
You hold: a source list your business owners signed.
- 03
Models with evaluation and lineage
Vertex AI models and agents tested against agreed tasks, traced from source to answer.
You hold: an evaluation set and a lineage record.
Easy to start
Start with one workflow, not a program.
You
Choose one workflow, name one owner, and give us time with the people who sign it off.
Us
We bring engineers and the gateway design, we work inside your environment under your security rules, and we write the review pack alongside the build.
A good first step is the Agentic Readiness Assessment: it shows which data your first agent needs and whether that data is ready. See the fixed-price ways to start.
What the work feels like
What the first month looks like from your side.
The first week
One workflow is chosen and one person owns it. The questions your reviewers will ask are written down before any build starts.
The first sprint
Governance goes in before the first agent does: the gateway, the audit trail and the access rules. Your risk team sees them working.
Every week
A working demo you can show your own review board, not a status slide.
The sign-off
A decision log that can rebuild any action, and a go-live date that was agreed at the start.
Production readiness check
Test one workflow against the five questions.
A short check on a live or planned agent workflow. You get a score and the practices worth another look.
Take the readiness checkQuestions we are asked before the first conversation.
Do we have to move all our data to BigQuery first?
No. We start with the data one workflow needs.
Can you work with Looker and our existing reports?
Yes. Reports and agents read the same governed tables.
How do you keep personal data out of prompts?
Masking and access rules at the data layer, and policy at the gateway.
Show us the answer you are not sure you could trace.
Talk to an architectTell us what you are working on. Our Customer Partnership Team will reply to arrange a conversation.
