Kiln for PMs, QA, and Experts.

Drive quality without coding

Define quality standards, track AI bugs with reproducible traces, run reviews and ratings, and make data-driven model decisions—all from an intuitive desktop app, no code required.

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Give your team what they really need

Evals replace PRDs

AI is non-deterministic. Engineers and data scientists can't tune to a product requirement doc (or worse, Slack messages). Give them evals they can use to really improve product quality, measurably.

AI-native bug reports

JIRA tickets aren't reproducible. Kiln creates bugs with the exact trace and configuration that triggered them, and captures feedback in a format you can use to evaluate and improve at scale.

Focused feedback

Target each issue and requirement with small, targeted evals, allowing you to measure every axis you care about, and prevent regressions as you iterate.

02 Solution · for

PMs and subject matter experts

PM / SME
What hurts today
  1. 01 You can't define 'good enough' in a way the engineering team can test against.
  2. 02 AI bugs live in JIRA tickets with no input/output data, model info, or way to reproduce them.
  3. 03 A prompt change fixes one issue and silently breaks three others—you only hear about it from users.
  4. 04 Creating an eval requires a data scientist and hours, so your team skips it or does it too late.
  5. 05 Model and prompt decisions come down to whoever tested it last, not systematic comparison.

AI product management before and after Kiln

Without Kiln
  • File AI bugs in JIRA with a description and a screenshot—no structured data, no way to measure if the fix worked.
  • Ask engineering 'is it better now?' and hope the answer is based on more than a gut check.
  • Quality standards live in your head or in a Notion doc that nobody references when changing prompts.
With Kiln
  • Create an AI issue in under 10 minutes with input/output data and an eval that automatically verifies the fix.
  • Compare model and prompt combinations across every eval at once—with cost data—and pick the winner.
  • Write a Kiln Auto-Eval in 10 minutes and get a production-ready eval, synthetic dataset, and calibrated judge.

From reported issue to verified fix in 4 steps

01
Report

Create an issue with the failing input, output, model info, and a description of what went wrong.

02
Evaluate

Kiln turns your issue into an eval and generates synthetic test data that reproduces the problem at scale.

03
Iterate

Engineering changes the prompt, model, or fine-tune. The eval re-scores automatically so you see progress.

04
Monitor

The issue eval keeps running over time, catching regressions before they reach users.

What changes for PM, QA, and Experts

Evals in under 10 minutes

Kiln Auto-Evals walk you through quality requirements with an AI assistant that detects gaps, generates test data, and calibrates the judge against your preference. No data science background needed.

An animated illustration of eval specs being generated row by row, with green checks and red x marks evaluating each one.
Generating Eval

Domain experts' ratings & feedback

Subject matter experts provide ratings and feedback in an intuitive visual app. No spreadsheets or custom dashboards. Kiln learns from all of their feedback, improving your agent.

Agent decisions backed by data

The Compare view scores any combination of models, prompts, and fine-tunes across every eval — with cost per run. See the quality/cost tradeoff yourself and communicate the decision with concrete numbers.

Many small evals, for separate concerns

Large evals take weeks to develop, break down when requirements change, and miss regressions on subsets of their dataset. Kiln allows dozens of small parallel evals — easier to create, and each team owns the areas they care about: PMs, QA, subject matter experts, business teams, and engineers.

Your AI co-pilot for quality

An AI assistant that reads your evals, traces, and feedback — then proposes new evals, runs experiments, and explains tradeoffs in plain language. So you can drive AI quality without learning a new toolkit.

PM & QA workflows mapped to Kiln

Evals under 10 minutes

Define requirements and get a calibrated eval with AI-powered gap detection.

AI-native issue tracking

Report bugs with structured data and get eval-backed fix verification.

Cross-eval comparison

See every model/prompt combo scored across all evals with cost.

Regression monitoring

Issue evals keep running after fixes ship, catching silent regressions.

Prompt library

Track prompt history, use prompt generators, and automatically build optimal prompts with evals.

Ratings and feedback

Rate and provide feedback on outputs, then watch it improve the AI system.

Team collaboration

PMs, SMEs, QA, and engineers work in the same project.

Intuitive + powerful app

Create evals, write feedback, rate datasets, or even try new agent designs — all without coding.

Where Kiln fits for product managers

Two common shapes for PM-driven AI quality management.

Scenario 1
A PM at a fintech ships a customer-support agent.
Problem

Users report the agent gives wrong refund policy answers. The PM files a JIRA ticket, but engineering can't reproduce it and closes it as 'works for me.'

With Kiln

The PM creates a Kiln Issue with the exact input/output pair, generates synthetic data that exercises the refund scenario, and builds an eval that fails on the bad behavior.

Outcome

The eval is picked up automatically in the next optimization run, resolving the issue. All future changes are validated against it, ensuring it never regresses.

Scenario 2
A PM at a healthcare company ships a clinical note summarizer.
Problem

The team wants to switch from GPT to a cheaper model but has no systematic way to know if summary quality holds up. The last model change caused silent regressions that took weeks to surface.

With Kiln

The PM builds Kiln Auto-Evals for each quality requirement (accuracy, completeness, clinical terminology). Domain experts rate a golden dataset. The Compare view shows both models scored across all evals with cost.

Outcome

The team ships the cheaper model with documented evidence that quality meets the bar—eval creation to decision in under a day.

Give your team evals, not vibes.

Define quality standards, track issues with real evals, and make data-driven decisions.

Download Kiln Read the docs