01 / Definitions
One metric, many answers
Revenue, customer, and channel can each mean several things. If the approved definition lives in a dashboard, a wiki, or someone's memory, an agent has to guess.
Services
Semantic foundations for dependable AI.
Refinity architects the semantic foundations that make agentic analytics dependable — then builds on them. Here is what that covers and how an engagement runs.
The semantic gap
Your AI can query the data. It still doesn't know what the data means.
Tables expose fields. They do not explain definitions, grain, relationships, and ownership. Refinity turns that missing business context into a semantic contract both people and agents can use.
02 / Context
Context is a contract
A longer prompt cannot repair ambiguous data. Agents need explicit grain, relationships, business rules, and ownership close to the data they query.
03 / Evaluation
Trust must be testable
A plausible answer is not enough. Every important claim needs a source, an expected result, and a quality gate that catches drift before a decision does.
What we do
Architecture first. Builds that last.
Engagements scoped to your stage — from a first architecture decision to a system your team can run without us.
Semantic layer architecture
Data models designed for the questions your business actually asks — dimensional design, governance, and capacity-tested sizing so the layer holds up at enterprise scale, not just in the demo.
Agentic analytics enablement
The connective tissue between your data and your AI: agent-readable context, semantic-model tooling, and evaluation harnesses that make agent answers checkable before anyone bets a decision on them.
AI strategy & architecture decisions
Contested platform choices settled with evidence: requirements turned into testable criteria, designs stress-tested against them, decisions framed by cost of change. Your board gets reasons, not vibes.
Scoped builds
Focused agents, analytics workflows, and automation tooling — deliberately scoped so one senior architect can own the build end-to-end and leave your team with a system it can run.
The method
A path built for shipping.
No open-ended research projects. Every engagement moves along the same four steps, each with a concrete deliverable and a decision point.
01
Diagnose
An audit of your data foundations and AI readiness. You get a written requirements register you keep either way.
02
Design
The architecture spec: semantic model design, sizing analysis, integration and evaluation plan. Go/no-go before any build.
03
Build & harden
Implementation with quality gates — CI-checked models, evaluations, monitoring. Not just the happy path.
04
Ship & transfer
Documentation, enablement, handover. We don't want to be a dependency — your team owns and extends what we build together.
Give your AI one version of the truth.
Bring the business question and the data you have. Refinity will map the semantic gap and tell you what it takes to close it.