What is Encephalon?

Encephalon is an Enterprise AI Governance Practice for the whole business: development teams, business and management teams, and executive teams. The Practice encodes four governance objects (sanctioned-model lists, verification thresholds, jurisdictional standards, and human-acceptance authority) that run inside the AI sessions it governs. The audit record covers the autonomous build runs, the work headed to production, so the rigor lands where the risk is. Its product is Enterprise Intelligence, the AI Governance Harness for Claude Code. The methodology is the Integrated Requirements Methodology, adapted from the Kimball Lifecycle, a dimensional-modeling lineage spanning three decades of enterprise data work. Encephalon addresses the requirements gap RAND Corporation identified as the #1 root cause of the 80%+ enterprise AI project failure rate.

Enterprise AI Governance · Enterprise Intelligence

Your AI governance ends at the policy.
Your AI work happens in the session.

Your standards, your security rules, and your domain knowledge reach the Claude Code sessions your teams run. The audit record covers the autonomous build runs, the work headed to production, so the rigor lands where the risk is. The methodology behind it governs the rest of your AI stack.

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Where to Start

Where are you on your AI Governance journey?

"I need to see it work before I can ask for budget."

Proof of Concept

About a month, one team, a few hours a week.

"Our developers already use AI and the output is all over the place."

Enable Your Development Teams

4 to 12 weeks, no freeze, teams keep working.

"I have a list of AI ideas and no idea which ones are real."

Data and AI Roadmap

2 to 6 weeks, mostly conversations with your people.

"We sped up engineering and now everything else is the bottleneck."

Enable Your Entire Organization

Roadmap first, then waves you approve one at a time.
See all four, in detail

The Problem

AI is in production.
Governance is in SharePoint.

Teams across the business are already working with AI, and engineering teams are shipping AI-generated code today. The governance program of record either doesn't exist on paper or lives in policy documents no AI session reads.

Today

  • No audit trail for AI-generated code when the auditor asks
  • AI throughput exceeds review throughput, and your senior people become the bottleneck
  • No program of record the auditor, the board, or the new CISO can point to

With the regime encoded in Enterprise Intelligence

  • Standards encoded once, enforced in every session
  • Review shifts from every change, manually, to exceptions only, against codified rules
  • The audit artifact accumulates at the close of every autonomous build, ready when the question comes

This is the product

Enterprise Intelligence

The AI Governance Harness for Claude Code.

Enterprise Intelligence loads your conventions, security policies, and domain knowledge into Claude Code, and every autonomous build leaves a queryable audit record of what was produced and how.

See the product

Watch it build (7 min)

Measured, not asserted.

Enforced

Nothing ships without independent sign-off.

These three numbers measure one step: finding the right file before any work can start. Unaided, a Claude Code session searches, opens files that turn out to be wrong, and you pay for every token it spent getting there. That cost repeats on every session your team runs, which is why the saving below is a running one rather than a one-off.

Relevance

2.3×

More of what it retrieves is on-target. 96% vs 42% token-weighted precision, across 32 blind-graded discovery tasks.

Accuracy

84% vs 31%

Opened the right file first, versus unaided search. Across 32 discovery tasks.

What you stop paying for.

~43%

Average tokens saved per file-discovery operation, measured in the aggregate across 21 tasks.

From the blog

Field notes on governed AI

Practical guides on agentic orchestration, AI governance, and context engineering for development, business, and executive teams.

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