Ship AI fast without letting support bugs churn your customers
Slow fixes on AI bugs cost you customers, not just hours
AI products carry non-deterministic failure modes: prompt drift, schema mismatches, hallucinated tool calls, model-version regressions. Every one surfaces in the support inbox before your dashboards catch it. With a small team and no dedicated triage engineer, those tickets pile up, the customer waits, and trust erodes faster than the bug itself. And the file the model named in its output is rarely the file that needs to change, so even the hunt is slow.
Read the LLM output, not a paraphrase.
Your customers screenshot the broken chat, not the stack trace. Watari reads every attachment in the ticket (chat transcripts, structured-output JSON, broken tool calls), preserves the verbatim text untouched, parses structured fields, and emits a bug grounded in what the customer actually saw.
- LLM-output aware
- Chat transcripts, JSON blobs, tool-call traces parsed as evidence.
- Verbatim preserved
- Customer quotes survive extraction; nothing is paraphrased into the bug report.
- Structured fields
- Severity, repro steps, and expected vs actual pulled into a typed schema.
Prompts mapped like code.
A prompt edit that ships a regression is just as load-bearing as a function edit, and your map needs to know that. Watari indexes prompts, system messages, schemas, and eval fixtures and maps them the same way as your TypeScript or Python, so a prompt-driven regression maps to a prompt file, not a guess at the call site.
- Prompts indexed
- .md, .txt, and prompt template files indexed alongside code.
- Schemas indexed
- JSON schemas, Zod, and Pydantic models mapped the same way as your code.
- Eval fixtures indexed
- Fixtures and golden files become candidate map targets, not noise.
Real failure log, flakes flagged.
Non-deterministic model output makes CI fail in ways a synthetic repro can’t reach. Watari follows up on the draft PR against the real failure log. Flaky failures are flagged to engineers instead of retried forever.
- Failure-log driven
- Your CI failure log steers the follow-up commit.
- Flakes flagged
- Flaky failures are flagged to engineers instead of retried forever.
- Reviewer follow-up
- Reviewer comments that ask for a change get a follow-up commit.
We run Watari on our own support queue. Eat your own cooking.
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Frequently asked questions
Does Watari work on LLM SDK code?
Yes. Tool definitions, message construction and streaming handlers are indexed like any other code.
How does Watari handle prompts and system messages as source?
Prompts, system messages, and schemas are indexed alongside your code and mapped the same way as your code. A prompt edit that ships a regression maps the same way a code edit does: the draft PR opens against the prompt file, the diff is focused, and CODEOWNERS routes the prompt-domain reviewer.
Can Watari debug LLM-output-related bugs?
Yes. Watari reads the LLM output the customer screenshotted, parses structured fields, and surfaces hallucinated tool calls or schema mismatches as concrete bug reports. The map then routes to the prompt, schema, or guardrail that needs the change.
What about non-deterministic test failures from model output?
Your CI failure log steers the follow-up commit, not a synthetic repro. Flaky failures are flagged to engineers instead of retried forever, so engineers get a real signal.
Do you store our prompts?
Prompts are indexed inside your own isolated tenant so the mapper can find them. They are never used to train models. Indexed data stays in your isolated workspace, protected by row-level security.
When a support ticket reports a bug, your engineers get a drafted fix.
Connect your help desk or your issue tracker, then GitHub or Azure DevOps. Watari maps each bug to the exact file and function in your code and drafts the fix as a pull request your CI checks.
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