ABOUT COHERANY
We built this for the ones who already knew.
The people who understand your data best have always been the last ones allowed to act on it. Coherany exists to fix that.
LIVE. Actively demoing with Fortune 500 prospects. Pilots open.
Our Mission
Most AI platforms can't answer a simple question: who's accountable for this decision? Coherany can. Every time.
Your SMEs already understand 80% of the matching work in your data backlog. Coherany lets them run it themselves. An LLM coach helps them define each pattern; the deterministic engine runs every decision in production, audit-ready by default.
The result: AI speed with human accountability. Your data scientists sponsor and review. Your SMEs operate. The 20% that actually needs a data scientist stays with them. Decisions that auditors, regulators, and customers can trust.
"The goal isn't to replace human judgment. It's to scale it."
Coherany founding principle
What We Believe
Transparency Over Black Boxes
Every decision should be explainable. When someone asks 'why did this insight apply?', there should be a clear answer that traces back to a human approval.
Humans In The Loop
AI should amplify human judgment, not replace it. The best systems put your team in control and run at machine speed behind them.
Security by Default
Encryption at rest and in transit. Role-based access on every table. Complete audit trail. SOC 2 in progress. Type 1 audit target Q3 2026.
Production-First
Demos are easy. We build for the hard part: production workloads at scale with real SLAs and real consequences.
Audit Everything
Compliance isn't an afterthought. Full revision history, approval workflows, and audit trails are built into the core architecture.
Simple Over Clever
The best platforms are ones you can understand and maintain. We favor proven technologies over novel complexity.
Why We Built This
Coherany started with a conviction: AI is good at finding patterns. The people closest to the data should decide which ones matter.
Most of the matching work piling up in data science backlogs is repetitive. SMEs already know the answer. They just don't have a way to operationalize what they know without writing code or waiting in queue.
So we built one. An LLM coaches them through defining each pattern. A deterministic engine runs every decision against those patterns in production at scale, audit-ready by default. Your data scientists sponsor and review. Your SMEs operate the 80%. The 20% that actually needs a data scientist stays with them. We call it Pattern-Approved Retrieval. PAR.
Accountability and automation belong together. Coherany exists because we believe the people who understand the data best should also be the ones running the rules.
Why Now
$10K+/mo
LLM Costs Are Exploding
Organizations are discovering that per-record API costs don't scale. A million records a month through an LLM costs tens of thousands of dollars.
EU AI Act
Regulation Is Accelerating
The EU AI Act, state-level AI laws, and industry regulators are all demanding explainability and audit trails. Black-box AI is becoming a compliance liability.
Trust Gap
AI Trust Is Eroding
Hallucinations, inconsistent outputs, and unexplainable decisions are making executives hesitant. Organizations need AI that produces the same answer every time and can explain why.
Meet the Founder
I've spent my career building data systems across health insurance, telecom, banking, financial services, and large commercial insurance. Every company I worked with had the same problem: brilliant people whose expertise was locked in their heads.
Claims adjusters who could spot fraud patterns in seconds. Compliance officers who knew exactly which regulations applied to which transaction. Operations leaders who could triage a thousand tickets by instinct. Their domain knowledge was the most valuable asset in the building — and the hardest to scale.
The only way to use that expertise was to keep those people doing the same work, every day, manually. There was no path to operationalize what they knew. No way to turn years of judgment into something the organization could run at scale.
I built Coherany to change that. The platform learns from domain experts, surfaces patterns for their approval, then executes those decisions at scale. Experts stop being bottlenecks and start being architects of systems that make 400K decisions per second with the same judgment they'd apply manually.
That's the mission: give domain experts a way to scale their judgment without being in the loop on every single decision.
