Contextual Intelligence
Your databases, dashboards, reports, and institutional knowledge become the context layer that makes automation accurate and trustworthy, not a model reasoning over guesses.
The bridge between your BI heritage and AI-ready platforms
Every AI agent or copilot is only as good as the context it can reach. Most enterprises already have that context, it is sitting in dashboards, reports, data models, and the institutional knowledge locked in spreadsheets and inboxes, but it is not structured in a way anything but a human can query. That gap is exactly where AI initiatives quietly fail: not because the model is weak, but because it has nothing reliable to reason over.
This engagement inventories what your organization already knows, then builds the semantic and metadata layer that turns dashboards, reports, and data models into context a machine can query as reliably as a person reads a report. We connect that layer into the agents, copilots, and workflows that need grounded answers, and we validate recall and accuracy against real questions before anything reaches a user.
The result is automation that answers from your data, with a source behind every answer, not automation that sounds confident and is quietly wrong.
Four phases, from inventory to validated recall
Inventory
Catalog the dashboards, reports, data models, and institutional knowledge already answering questions today.
Structure
Turn that inventory into a semantic and metadata layer machines can query as reliably as people read a dashboard.
Connect
Wire the context layer into the agents, copilots, and workflows that need grounded, source-backed answers.
Validate
Test recall and accuracy against real questions from real users before anything reaches production.
A context layer, not a slide deck
Context inventory
A catalog of the dashboards, reports, models, and institutional knowledge already answering questions across your organization.
Semantic & metadata layer
Shared definitions and structured metadata that make your context queryable by both people and models.
Retrieval architecture
The connective layer that lets agents and copilots pull the right context at the right moment, with a source attached.
Grounding validation suite
A test set of real questions used to measure recall and accuracy before the context layer reaches production.
Freshness monitoring
Automated checks that catch a stale or drifted source before it quietly degrades every answer built on it.
Documentation & handoff
A context layer your own team can extend as new dashboards, reports, and systems come online.
Grounded by design, not by hope
Grounded in what exists
We structure the context you already have rather than starting from a blank retrieval index.
No hallucination by design
Every answer traces back to a source. If the context layer cannot find one, the agent says so.
Works with your BI stack
Built to sit alongside the dashboards and reporting tools your teams already trust, not replace them.
Measured before it ships
Recall and accuracy are tested against real questions before the context layer reaches a single user.
Questions we hear early
Do we need a data warehouse first?
It helps, but is not required. We can build a context layer over well-structured dashboards and reports; a warehouse just gives it a stronger foundation.
How is this different from a generic RAG setup?
A generic retrieval index treats every document the same. We start from your actual semantic layer and BI definitions, so "the numbers" the agent retrieves already agree with the numbers your team reports on.
Can this connect to our existing copilots or agents?
Yes. The context layer is built to be a source other tools query, including the AI Agent Workflows platform or your own copilot investment.
Give your agents something real to reason over
Tell us what your dashboards and reports already know. We will show you what it takes to make that context queryable and trustworthy.
Talk to us