Products/AI Safety / Output Verification / Control Layer/Perf

Perf

AI Output Control Layer

AI Safety / Output Verification / Control Layeraimachine-learningoutput-verificationReviewed

Our Take

Perf is the checkpoint you didn't know AI needed. It's a verification layer that sits between whatever your AI model spits out and your actual user—catching hallucinations, bad math, weird formatting, and all the ways LLMs get confidently wrong before anyone sees it. Think of it as the seatbelt for your AI pipeline.

Right now there's not much public info on who's behind it or the numbers they're doing—but the problem they're solving is real. Every company shipping AI to users is living with that knot in their stomach wondering "what if it says something stupid?" Perf is the answer to that question. We'll see more once they get some runway out in the open.

producthunt.com/perf-technology-inc →

Perf is a control layer for AI outputs that sits between your AI model and your application. It checks every output before it reaches users or downstream systems - verifying structure, grounding, policy compliance, and business-specific constraints; repairing fixable errors like malformed JSON, missing fields, unsupported claims, and constraint violations; and blocking outputs that cannot be trusted with structured diagnostics.

Key Features

Verify - check outputs for structure, grounding, policy compliance, and business-specific constraints, Repair - fix errors when recoverable (malformed JSON, missing fields, unsupported claims, constraint violations), Reject - block outputs that cannot be trusted with structured error context, Real-time interception before outputs reach users or downstream systems, Field-level, claim-level, and rule-level failure identification, Schema, policy, source data, and business rule validation

Problem It Solves

AI outputs are not automatically safe to use. Models can produce fluent answers that are wrong, malformed, off-policy, or unsupported by source data. Hallucinated or unsupported claims reach users, business rules are inconsistently enforced, broken structure disrupts workflows, and failures are caught too late.

Target Customer

enterprises deploying AI in customer-facing and workflow-critical roles, particularly in Customer Support, Financial Services, Legal & Compliance, and Healthcare & Life Sciences

Use Cases

Customer support response verification before reaching users, Financial services compliance enforcement (disclosures, risk thresholds, regulated communication), Legal and compliance source fidelity checking, Healthcare clinical safety and privacy enforcement, Preventing AI hallucinations from reaching production, JSON and schema validation for downstream systems

Differentiator

A dedicated control layer that sits between AI models and applications - not just prompt engineering or guardrails. It provides verify, repair, and reject capabilities with structured diagnostics for system integration.

Why Now

As AI starts touching customers, workflows, and systems of record, the risks of unverified outputs have increased. Most teams handle this with prompts, retries, and custom checks, but that approach doesn't scale when AI reaches production systems.

Key Facts

The people behind Perf

Ivan Charapanau

profile

Maker

Engineering inclined

Links

Website Source: product-hunt

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