The Category
Enterprise Context for Business Decision-Making
An enterprise context layer is the governed layer between your data and every decision. It gives every person and every AI agent the same business context — approved definitions, rules, what a number should be, the memory of how past decisions turned out, and a safe boundary to act within. Where a semantic layer or BI tool tells you what a number is, an enterprise context layer makes the decision trustworthy — so every person and every AI agent decides consistently.
Reporting is the floor. Deciding is the goal.
Most analytics can report a correct number; almost none can make a decision. A semantic layer (dbt, Cube, LookML, Snowflake Semantic Views) makes a number correct and consistent. That is real, valuable foundation work — but it only tells you what the number is. It has no expectation of what the number should be, no memory of what happened last time, and no way to act.
An enterprise context layer sits on top of that foundation and closes the gap between seeing a number and acting on it — for every person and every AI agent in the business.
What an enterprise context layer does
One trusted number
FoundationEvery person and every AI agent gets the same answer for the same metric. (A good semantic layer does this too — it's the floor, not the finish line.)
Shared business context
FoundationYour rules, definitions, and entities are written down once, kept current, and available to every tool and agent.
Knows what's normal
Decision-makingIt holds an expectation for each important number — from a forecast or trend — and flags the moment reality deviates, and who it affects.
Remembers and learns
Decision-makingOutcomes are captured and fed back, so recommendations get more accurate over time instead of starting from scratch.
Acts within guardrails
Decision-makingIt can take action — draft, alert, adjust — only within governed limits: what's allowed, who approves, budget, and preview-or-undo.
Building one? Talk to us first.
The hard parts — causal metric graphs, cross-source entity resolution, governance that holds across AI agents, and a safe action boundary — are where most in-house builds stall. Whether you're scoping it, mid-build, or evaluating vendors, spend 30 minutes with the engineers who've already mapped the terrain. We'll tell you what to build, what to buy, and where the landmines are — even if the answer isn't Fig.
Talk to our engineersHow it compares
Frequently asked
What is an enterprise context layer for business decision-making?
An enterprise context layer is the governed layer between your data and every decision. It gives every person and every AI agent the same business context — approved metric definitions, rules and policies, what a number should be, the memory of how past decisions turned out, and a safe boundary to act within. Where a semantic layer or BI tool tells you what a number is, an enterprise context layer makes the decision trustworthy: it knows what the number should be, notices when it's off and who it affects, and can act within governed guardrails.
How is an enterprise context layer different from a semantic layer?
A semantic layer makes a number correct and consistent — it's a dictionary of definitions. An enterprise context layer sits on top and adds what a semantic layer structurally cannot do for decisions: expectations (what a number should be), surprise detection, memory of outcomes, and governed action. A semantic layer reports; an enterprise context layer decides.
Does an enterprise context layer replace my BI tools or data warehouse?
No. It sits on top of your existing warehouse (Snowflake, BigQuery, etc.) and works alongside your dashboards and semantic layer. You keep reporting where it is; the enterprise context layer adds the expectations, memory, and governed action that reporting was never designed to provide.
Why does an enterprise context layer matter for AI agents?
AI agents are only as trustworthy as the context and guardrails around them. Without an enterprise context layer, agents reason from raw data with no shared definitions, no expectation of what's normal, no memory, and no safe way to act. The context layer gives every agent the same governed business context and the boundary that makes acting safe.
Should we build an enterprise context layer ourselves or buy one?
It's a deceptively deep problem — causal metric graphs, cross-source entity resolution, governance that holds across AI agents, and a safe action boundary are where most in-house builds stall. Whether you build, buy, or do a hybrid, talk to engineers who've already mapped the terrain before you commit two quarters to it. Fig is the enterprise context layer built for exactly this — and we're happy to advise even if the answer isn't Fig.
Is Fig an enterprise context layer?
Yes. Fig is the governed enterprise context layer for business decision-making — built from your own data, your own rules, and your actual outcomes. It connects to your warehouse, learns what drives what in your business, enforces your definitions and policies on every query, and works with any AI agent via MCP.