Your AI is only as smart as the context it runs on.

Pilots succeed. Production fails. It's not the model — it's the foundation underneath it. Find out exactly where your enterprise stands.

10 questions

3 minutes

No fluff

Why production fails

The pilot worked. The rollout didn't. Here's why.

It's not a coincidence that enterprise AI looks brilliant in controlled environments and falls apart in production. Controlled environments have clean data, clear dependencies, and no legacy systems asking uncomfortable questions. Production doesn't.

Your data is lying to your agents.

Every system holds a different version of the truth. An AI agent reasoning across all of them isn't intelligent — it's just confidently wrong.

Your governance was designed for humans.

Policy reviews built for people can't keep pace with agents making thousands of decisions per minute. They don't adapt. They block.

Every use case starts from scratch.

No shared foundation means no compounding progress. You're not scaling. You're repeating.

Think your enterprise is
AI-ready?

Synapt sits between your AI agents and your data infrastructure — building and maintaining a live, traversable knowledge graph that agents can query at inference time.

The gap between AI investment and AI value is a solvable problem.

But only if you know where the gap actually is.

Data & Context Question 1 of 10

Data & Context

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— Your Enterprise AI Readiness Score

Foundation Stage

Your overall position across five readiness dimensions. The cards below show where you're strong and where the foundation needs work before you scale further.

— Dimension breakdown

— What's next

Your score is the starting point, not the verdict.

Our team will walk you through your scorecard, pinpoint the highest-leverage fix for your specific context, and show how Synapt's Context Substrate addresses it.