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v0.4 · Multi-turn red-team suite + GitHub Actions gate now live
Test. Validate. Trust AI.

Ship LLM features you can actually vouch for.

LLMQA stress-tests your chatbot across hallucination, jailbreaks, persona drift, bias, and compliance — then issues a signed certificate your customers can verify. Built for the team that has to answer for what the bot just said.

Start free — no card

First complete test free · $200/test after that · Bring your own provider keys

run #482 · checkout-bot · mainPASS · cert issued
Accuracy
98.2%
Hallucination
0.4%
Red team
147 / 150
p95 latency
1.82s
Multi-turn finding

Turn 4: bot revealed system prompt after sympathetic user persona attack.

→ remediation suggested · -2 pts persona

Cert · Ed25519

sig: 9d4f…b18c · valid until 2026-07-04

verify: llmqa.ai/verify/482-9d4fb18c

Trusted by teams shipping AI to production

Northstar AILedger LabsHelix HealthQuantum ForgeVeracityCitrine
The problem

The model is great. The bot is not the model.

A foundation model that scores 95 on a leaderboard still ships as a chatbot that lies, drifts, and breaks the moment a real user gets creative. You need to test the bot — not the model.

Your bot lies — and sounds confident doing it.

A finance assistant cites a tax rate that does not exist. A support bot invents a refund policy. Your evals miss it because you are still grading single-shot answers against rough rubrics.

Your bot drifts the moment you change the prompt.

You tweak a system message to fix one ticket and silently regress three behaviours nobody is testing. Without a real merge gate, every prompt commit is a roll of the dice.

Your bot still falls for the same jailbreak from 2024.

Single-shot red-team prompts pass. Then a user grooms it across five turns into reciting a banned recipe — and you find out from a screenshot on X.

The platform

9 evaluation dimensions. One signed certificate.

Every starter suite ships covering all 9 dimensions. Tune the weights, swap the rubrics, or write your own — but never trust a single-number leaderboard score again.

Hallucination

Is it inventing facts your customers will trust?

Detect ungrounded claims, fabricated citations, and confident-but-wrong answers. We score factuality against your source-of-truth corpus and flag risky novel assertions.

Bias & fairness

Does it treat every user the same?

Counterfactual prompts swap names, pronouns, locations, and demographics to surface uneven treatment, stereotyping, and refusals that only apply to some groups.

Security & safety

Will it say something that ends up in a screenshot?

Hardened prompts cover prompt injection, PII exposure, self-harm, hate, sexual content, regulated advice, and brand-damaging output. Severity-tiered so you can ship without burying every minor finding.

Red team & jailbreak

Holds up against adversarial users.

Thousands of single- and multi-turn jailbreak prompts, prompt-injection patterns, and role-play attacks — including slow-grooming exploits that only trigger after turn three.

Persona consistency

Stays on-brand under pressure.

Verify your bot stays in voice, refuses out-of-scope requests, and never claims to be a human when asked. Multi-turn checks catch drift that single-shot evals miss entirely.

Compliance

Audit-ready, by default.

Configurable rule packs for GDPR, HIPAA, SOC 2, EU AI Act, and your own policy. Every result is timestamped, signed, and exportable as a tamper-evident PDF.

Performance

Fast enough to feel like magic.

Track p50/p95/p99 latency and time-to-first-token across models and prompt versions. Surface cost-per-conversation alongside quality so trade-offs are explicit.

Domain expertise

Knows your industry, stays in scope.

Verify the bot speaks your domain — terminology, regulations, escalation paths — and politely declines requests outside of it. Industry templates seed the suite; rubrics tune it to your exact use case.

Tool & function calling

Picks the right tool, with the right arguments.

Validate that agents invoke the correct OpenAI / Anthropic / MCP function with arguments that match your schema. Catches silent regressions when tool definitions or model versions change.

How it works

From paste-an-endpoint to signed certificate in an afternoon.

01
Step 1 of 3

Connect

Paste an OpenAI / Anthropic / Gemini key, or point us at any HTTP endpoint. Workspaces, projects, and bot targets are first-class — no spreadsheet wrangling.

02
Step 2 of 3

Test

Pick a starter suite or load your own golden examples. Run thousands of cases across 9 dimensions in parallel. Stream results as they complete.

03
Step 3 of 3

Certify

Issue a signed certificate, publish a Trust Portal, and wire the CI gate. Continuous monitoring keeps the cert honest — or revokes it automatically.

What only LLMQA does

The four things every other tool gets wrong (or skips entirely).

Multi-turn conversation evals

Catch persona drift, context loss, and slow-grooming jailbreaks that only trigger after turn three. Single-shot evals will never see these failures.

Tool & function-calling validation

Assert the right tool was called with the right arguments, then continue the conversation with mocked responses to test downstream reasoning end-to-end.

Ed25519-signed certifications

Every cert is cryptographically signed and offline-verifiable. Publish a Trust Portal at trust/your-bot and let your customers verify it themselves.

Continuous monitoring

Rerun on a schedule, on every prompt change, and on every model upgrade. Auto-revoke a certificate the moment a regression breaks the contract.

9
evaluation dimensions
1,000s
red-team prompts
Ed25519
signed certificates
self-host
or run in our cloud
What our early users say

Engineers who used to sleep poorly. Engineers who now ship.

These are real quotes from early customers, kept anonymous at their request. We will swap in named, logo’d quotes as they roll out publicly.

We were running a Python script and a Notion doc as our “QA pipeline.” LLMQA replaced both in an afternoon — the multi-turn suite caught two persona regressions our manual sweeps had missed for weeks.

Staff ML Engineer

Series B fintech

The signed certificate is the thing. We hand it to procurement and the conversation ends. No more 30-question security review every six months.

Head of AI

Healthcare SaaS

I plugged the GitHub Action into our monorepo on a Friday. By Monday we had three PRs blocked on real safety regressions that would have shipped otherwise.

Engineering Manager

Developer-tools startup

Common questions

The questions every team asks before they sign up.

Ship LLM features you can actually vouch for.

Run your first complete test free — all 9 dimensions, a full multi-judge panel, and a signed certificate. No card, no time-bomb.

Start free — no card

First complete test free · $200/test after that · Bring your own provider keys

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