No-Code AI Quality Loop

For product teams that take AI quality seriously

TryEval brings PMs, QA, domain experts, and engineers into one workflow to evaluate AI systems, review failures, track regressions, and make confident ship/no-ship decisions.

New to AI evaluation? See how quality checks become release decisions →

Test AI quality
Catch regressions
Ship with confidence

Most teams ship AI blind.
The rest evaluate the wrong way.

54% of enterprises have no formal AI evaluation. The 46% who do use dev-only tools that lock product, QA, and domain experts out of the loop.

Without evals, quality regresses
Your AI works in the demo, then drifts in production. Spot-checks and spreadsheets can't catch regressions — quality fails silently, and you find out from your users.
Dev-only tools exclude your team
Engineers can run the tests, but product, policy, and domain experts understand the real edge cases. When evaluation lives in code, the people who set the quality bar can't touch it.
Evidence turns opinions into decisions
An evaluation tests your AI on real-world cases and returns scores, failure reasons, and a verdict — so “should we ship?” is answered with data, not hunches.

The AI Quality Loop

From test cases to release confidence in four steps — a no-code workflow to test every model, prompt, and agent, find failures, and decide if it's ready to ship.

Define Quality
Tell us what good looks like — accuracy, safety, tone, policy compliance. Import your own test cases or start from curated datasets that mirror real user journeys.
Run Evals
Test across models, prompts, and versions using LLM judges, preset metric packs, custom checks, and human review. No SDKs, no YAML configs.
Diagnose Failures
Cluster failures, surface weak segments, and catch regressions and safety issues before users do. Every failure comes with a reason, not just a score.
Decide & Improve
Get a release readiness scorecard with a clear ship / no-ship signal. Fix the prompt, model, or data — then re-run the loop and track quality over time.

→ Every model, prompt, or data change gets a quantified quality signal — so your team can ship, monitor, and improve without guesswork.

What product leaders tell us

Quality is becoming a board-level conversation.

Engineering can test the AI, but product, policy and claims teams understand the real edge cases. We need them in the review loop.

VR

Vishwanath Ramarao

Head of Product, Acko

PMs are already doing AI quality review manually — they need better workflows.

HP

Harsh Pitaliya

AI Product Lead, smallcase

As we scale we need evals as a task done by our product team members to reach 99% accuracy.

AA

Amit Agarwal

AI Lead, Indiamart

100+ product & AI teams interviewed85% → production-ready performanceSupports OpenAI, Anthropic, Google Gemini, Groq

One quality loop. Every team. Zero code.

AI evaluation today is developer-owned. Production-grade quality needs everyone who sets the bar.

Product Managers
Define outcomes, prioritize evals, and own quality as a product metric — without waiting on engineering.
QA Teams
Build test suites and guardrails. Track repeatable failures and regressions across releases.
Engineering
Integrate, iterate, and compare models and versions with quantified signals instead of vibes.
Domain Experts
Provide ground truth and edge cases. Judge correctness and risk where automated metrics can't.

Frequently asked questions

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