Automates AI agent evaluation with diagnostic reporting for ML engineers.
Product memo
AI developers and ML engineers use Cipherra to automate agent evaluation suites at scale. It tackles the flakiness and manual effort of testing AI agents by integrating directly into CI/CD pipelines. This approach provides diagnostic reports that classify failures by root cause, moving beyond simple scores to offer actionable insights for remediation.
For who
AI developers and ML engineers
Solves what
Automating AI agent evaluation suites at scale with diagnostic reporting.
- Agent eval suites at scale
- Prioritized diagnostic reports
- Model agnostic integration
In their own words
Agent Evals at Scale. Wired Into Your Pipeline.
Run your eval suite on every model checkpoint. Get prioritized diagnostic reports — not just a score. Bring any model. Trigger from GitHub Actions, webhooks, or CLI.
Commercial cues
Model
free only
Free tier
Yes
Trial
No
Pricing Strategy
Cipherra offers a free tier, allowing early adopters to integrate its scalable AI agent evaluation into their workflows without upfront cost.
- • A free tier lowers adoption friction for AI developers and ML engineers.
- • CI/CD pipeline integration creates workflow lock-in for testing suites.
- • Diagnostic reporting differentiates from basic scoring, adding specific value.
Operator context
Founded
May 2026
Platform
Web app
Audience
Developers
Public footprint
Tech stack
Builder Strategy
- Strategy Type
- Niche Specialist
- Stage
- Pre Revenue
- Effort
- Solo Buildable
About Cipherra Expand
Cipherra provides a specialized platform for AI developers and ML engineers to automate the evaluation of AI agents. It addresses the common challenge of ensuring agent reliability and performance by offering scalable infrastructure for running evaluation suites.
The product integrates directly into CI/CD pipelines, allowing teams to test every model checkpoint. Its core value lies in diagnostic reporting, which moves beyond simple pass/fail scores to identify the root causes of agent failures, enabling faster and more precise remediation.
This focused approach helps teams build more specific AI agents by embedding continuous, actionable testing into their development workflows.