Why Prisma?

The Problem

Most validation tools use AI to score AI. They ship generic metrics — "relevance", "helpfulness", "toxicity" — and call it done. This works for surface-level checks, but it breaks down where it matters most: environments with specific rules, nuanced compliance standards, and edge cases that only your domain experts truly understand.

A generic metric can't tell you whether an agent followed your company's specific escalation procedure, or whether a clinical communication met your organization's patient safety standards. And when the AI scorer gets it wrong on an edge case, nothing changes — the same mistake repeats on the next similar case.

Prisma's Approach: Validation That Learns

Prisma takes a fundamentally different approach. Instead of static AI scoring, Prisma builds a system that learns from your organization's expertise over time.

Policy Metrics — Your Rules, Encoded

You define what compliance looks like for your organization using the 4-prompt pattern — a structured format that turns any business rule, quality standard, or regulatory requirement into an automated validator. A financial services firm encodes disclosure requirements. A contact center encodes escalation procedures. A healthcare provider encodes clinical communication standards.

These aren't "custom evaluators" bolted onto a generic framework. Policy metrics are how Prisma is designed to be used.

Human Review — The Source of Truth

When Prisma validates an interaction and the result is ambiguous — the policy doesn't clearly cover this specific scenario — it doesn't guess. The case is routed to your domain experts for review.

This is where Prisma diverges from every other tool: the human review isn't a fallback. It's the mechanism by which the system builds knowledge.

Metamemory — The System Gets Smarter

Expert feedback doesn't just resolve individual cases. It gets abstracted into controls — structured knowledge that captures the reasoning behind the expert's decision. These controls are stored in what we call Highly Superior Catalogued Memory (HSCM) — Prisma's metamemory system.

When a similar ambiguous case arrives in the future, Prisma retrieves relevant controls from memory and uses them as additional context for the validation. The expert's reasoning from months ago is applied to today's edge case — automatically.

The concept draws from research in metacognitive skill learning — the ability to monitor, evaluate, and regulate one's own thinking. Most AI research applies this at the model level. Prisma applies it at the system level: the platform itself develops the ability to recognize what it doesn't know, seek expert input, and integrate that input into its future reasoning.

The Flywheel

Expert reviews edge cases
        ↓
Feedback → abstracted as controls
        ↓
Controls stored in HSCM (metamemory)
        ↓
Future validations retrieve relevant controls
        ↓
Ambiguous rates drop (~30% → under 5%)
        ↓
Fewer cases need human review
        ↓
Experts focus only on truly novel scenarios

Over time, your organization's institutional knowledge — the nuances, the exceptions, the judgment calls that exist only in your senior team's heads — becomes encoded in Prisma's memory and applied consistently at scale.

Who Uses Prisma

• Contact centers — Validate agent interactions against response policies, escalation procedures, and quality standards
• Financial services — Enforce disclosure compliance and regulatory communication standards
• Healthcare — Validate clinical communication against patient safety requirements
• Insurance — Ensure claims handling follows procedural and regulatory guidelines
• Any regulated environment where interactions must meet specific organizational standards

Enterprise-Grade, Self-Hosted

Prisma is built for enterprise deployment. The platform runs on your infrastructure — Azure, GCP, or on-premises — so sensitive interaction data never leaves your security boundary. See Licensing for deployment options.

Traditional AI Metrics

Prisma also includes built-in correctness and hallucination metrics for teams that need standard AI performance checks alongside policy-based validation.