Make your data something agents can actually reason over.
Most queries against your data are already written by agents. There's no analyst to read the docs, work out the joins, or know which column actually means revenue. We build the model, semantic layer and entity graph that let an agent navigate your data cold, assembled from the metadata and business context you already have, then improved continuously as real usage reveals what's missing.
The near future
An agent navigates your data like your best analyst, without being told how.
Agents don't read documentation. They need the model, the definitions and the relationships to be part of the product itself. That context layer can be assembled from the technical metadata and business knowledge you already have. Once it's live, background agents keep it current as your data and your customers' questions change.
What it takes to support that
- Schemas, identifiers and entity models built for agent retrieval
- A semantic layer assembled from technical metadata and business context
- A unified catalog and relationship graph across every table and dataset
- Entity resolution and blending across datasets, agent-first
- Background agents improving the data, context and model continuously
Agent turn · result
The practice
Modeled for agents. Maintained by agents.
Build the model, semantic layer and entity graph agents need: assembled from existing metadata and business context, evolving autonomously with usage.
Modeled for Agents
- AI-friendly schemas and stable identifiers
- Entity models and relationships an agent can traverse
- Structures optimized for retrieval, not just storage
- Recommended joins between your data and your customer's
- Models built to answer the question actually being asked
A Living Context Layer
- A semantic model assembled from your existing technical metadata
- Business glossary, ontologies and definitions in one place
- Context synchronized across every platform you sell through
- Usage signals that expose missing or ambiguous definitions
- Background agents that keep it current instead of letting it go stale
One Entity Graph
- A unified catalog and relationship graph across every table and dataset
- Entities resolved consistently everywhere you publish
- Blending across datasets in an agent-first workflow
- Identifiers that survive joins with customer data
- Continuous agent-proposed improvements to data, context and model
Success looks like
Discoverability
Any agent can discover, understand and query your data without being told how.
Autonomy
Your semantic layer improves on its own as usage reveals what's missing.
How we work
Benchmark it. Spec it. Build it.
We start with a short, scoped benchmark of this practice: where you stand, against whom, and what to build first. You own everything we produce, with us or without us.
Benchmark
- Run real agent queries against your data
- Score how much context an agent has to be handed
- Rank what's standing between agents and right answers
Spec
- Target entity model, identifiers and semantic layer
- Map entities and relationships across every dataset
- Design how context gets built and kept current
Build
- Remodel schemas for machine reasoning
- Ship semantic context to every platform
- Stand up the catalog, entity graph and resolution logic
Manage
- Watch which questions agents get wrong
- Keep semantics in sync as data changes
- Let background agents propose model and context improvements
The AI-readiness benchmark
See your data the way an agent sees it.
We run real agent queries against your products, score the model and semantic layer on machine readiness, and map what an agent has to be told before it can get an answer. You keep the scored readout, the gap list, and the ranked build plan.
Request the benchmark