Automate the decisions you were never allowed to automate
Frontier models are no longer the bottleneck. What has been missing is the system around them: your policy compiled into constraints before reasoning starts, an independent evaluator checking the result, and a named person authorising it. Brain AI is that system.
Why now
Most enterprise AI never makes it out of the pilot
Not because the pilots fail. Because the decisions worth automating are the ones nobody can sign off on. To move one into production, the output has to be reliable enough to act on and somebody has to be able to show how it was reached. Neither is solved by choosing a better model.
25%
Pilots reaching production
88 percent of organisations use AI in at least one business function. Only a quarter have moved 40 percent or more of their pilots into production.
Stanford HAI AI Index 2026. Deloitte State of AI in the Enterprise 2026.
7 to 18%
Hallucination on supplied documents
Grounding improves reliability considerably and every serious deployment uses it. It does not finish the job.
Vectara HHEM-2.3 leaderboard, 11 May 2026.
1,600+
Court decisions
Rulings worldwide that have addressed reliance on AI-generated material which turned out not to exist.
Damien Charlotin, AI Hallucination Cases database, July 2026.
The difference
A frontier model on its own, and the same model governed
Brain AI does not replace the model you already use. It changes what happens before the model reasons and after it answers.
| A frontier model on its ownAnswer first, checks afterwards | The same model with Brain AIConstraints first, evidence throughout |
|---|---|
| Aligned to the lab’s policy, applied to everyone | Your policy, compiled per tenant and per jurisdiction |
| Behaviour shaped by instruction, which is a request | Tool access and evidence scope enforced at the runtime |
| Checks its own work, so it shares its own blind spots | A separate evaluator scores the answer and the trace |
| The same behaviour whatever the market or business unit | Constraints that differ by market, unit and regulation |
| No durable record of how it reached the answer | A versioned trace, and a named person authorises |
How a request is handled
Governance improves with every interaction
- 01
Context
The request arrives with its context: region, business unit, customer tier, and the regulation that applies. Nothing has been generated, and no policy has been applied yet.
- 02
Policy compiled
CIP selects the applicable profile and compiles it into the constraints the reasoning must operate inside. Tone and empathy bounds, regulatory rules, brand guidelines, and explicit allow and deny lists for tools.
- 03
Structured debate
Seven agents work the request, each mapped to a subsystem of human cognition: planning, memory and evidence retrieval, emotional and cultural judgement, self-reflection, and the executive function that holds them together. They divide the work the way the brain divides cognition, which is where the company takes its name.
- 04
Independent evaluation
EVA scores the response and the full reasoning trace against accuracy, evidence grounding, compliance, contextual fit and quality. It sits outside the reasoning, so it does not inherit its mistakes.
- 05
Governed delivery
A pass returns the response with its trace attached, routed to a named reviewer. A fail sends it back with specific corrections. Recurring findings become policy updates, so the constraints get sharper over time.
Recurring findings return to step 02 as policy updates
Underneath all five
Brain AI Harness
Infrastructure layer
Every stage above runs on the Harness. It routes each step to the right model under your cost, quality and latency constraints, enforces what the system is allowed to touch, and records what happened at each point. It is the reason none of this depends on a single model provider.
- Model routing with no single-lab dependency
- Tool and evidence access enforced at the runtime
- Observability against your own KPIs
Where it runs
The same architecture, encoded with your policy
Brain AI is a horizontal platform. The reasoning cycle is identical in a bank and in a hospital. What changes is the policy compiled into it, and that is the part our engineers encode with your team.
What we build
Three parts, one system
One layer runs the work, one governs the reasoning, and one puts it inside an enterprise.
Where we are
What is actually running
2
Design partner implementations
A telco decision engine, and a medical device business. Four to six weeks each, from first conversation to working implementation.
Delivered.
Pending
Patent status
A provisional specification covering the policy-governed multi-agent architecture and the evaluation feedback loop was filed in January 2026. PCT filing is in progress.
Filed January 2026.
3.96x
Return, in simulation
Against 2.83x for a rule-based team and 2.33x for a corporate agentic team, with the highest quality score of the three at the lowest spend.
Simulation results in a telco scenario, not live deployment data.
Insights
Research and analysis
Talk to us
See a governed decision end to end
Thirty minutes. A decision from your industry, your policy constraints, and the full reasoning trace, so you can see what a reviewer would actually receive.