AI-ready data
Your GenAI pilot works in the demo and fails on real data.
We build the pipelines, retrieval and access controls that keep answers current, permitted and measurably correct.
The problem
What we usually find
Stale and duplicated documents
The vector store holds three versions of the same policy, and nobody knows which one the assistant quoted.
Permissions disappear at the embedding step
Documents are chunked and embedded without their access controls, so the assistant can surface data the user was never allowed to see.
No way to measure answer quality
Teams judge the system by trying a few questions. Changes to chunking or models ship without knowing whether answers got better or worse.
Our approach
Four steps, each with a deliverable
- 01
Source and use-case mapping
We pick two or three use cases with a clear owner and list the documents and tables each one needs.
- 02
Ingestion with freshness SLAs
Documents and tables flow through governed pipelines with deduplication, versioning and agreed freshness targets.
- 03
Retrieval with permissions
Chunking, embeddings and hybrid search are tuned per source. Row- and document-level access is enforced at query time.
- 04
Evaluation harness
A labelled question set scores retrieval and answers on every change, so quality is a number, not an opinion.
Reference architecture
Deliverables
What you receive
- Use-case brief with owner, sources and success metric
- Governed ingestion pipelines with freshness monitoring
- Retrieval layer with permission enforcement
- Evaluation dataset and automated scoring in CI
- Production runbook and cost model per query
Typical timeline
How the work runs
Scope1–2 weeks
Use cases, sources, success metric and evaluation set.
Build4–6 weeks
Pipelines, retrieval and permissions for the first use case.
Evaluate2 weeks
Tune against the evaluation set until the target score is met.
ExtendOngoing
Further use cases reuse the same pipelines and controls.
Frequently asked questions
Which model and vector database do you use?
Whichever fits your platform and security posture: Databricks Vector Search, Snowflake Cortex Search, Azure AI Search, pgvector and others. The data layer is designed to outlive any single model.
How do you stop the assistant exposing restricted data?
Access controls travel with each chunk and are checked against the user's identity at query time. Restricted content is never retrieved for users without permission.
How do you measure whether answers are correct?
We build a labelled set of questions with expected sources and answers, then score retrieval precision and answer accuracy automatically on every change.
Do you build the chatbot interface too?
We focus on the data and retrieval layer. We integrate with your chosen front end or agent framework, or work alongside your application team.
How do we control cost?
Each use case ships with a cost model per query and per refresh, and we size embeddings and refresh schedules to that budget.
Have 30 minutes? Get a senior architect’s view of your data platform.
No sales deck. You talk to the engineer who would lead the work.
Book an architecture review