Judge · natural languagev3
Refund policy compliance
Answer must not offer a refund outside the 30-day window unless the customer cites a billing error.
Pass rate87%
Three steps to your first eval.
EvaliQA is the AI evaluation platform for teams shipping AI features. Language models do not fail loudly, so quality has to be measured, not noticed.
The shortest path to evaluate AI and get a score.
Three steps, and you have your first evaluation results for your AI system. No harness to build and no eval code to write.
Your product's rules, not a generic metric catalog.
A hundred generic metrics still will not tell you whether your refund policy held. Write the rule the way you would explain it to a colleague, and it becomes a judge that scores every run.
Code · your PythonCustom
Cited-doc precision
Compares cited passages against the retrieved chunks. Fails when a citation is fabricated.
Pass rate64%
Built-in · eval-ai-libraryGroundedness
Answer faithfulness
Standard groundedness score against retrieved context. Threshold configurable per plan.
Pass rate92%
New custom metric
Save to your workspace catalog
Tone of voice
Fails when the assistant sounds cold, dismissive or overly formal for a support context.
GEval ▾
0.75
CancelSave metric
One loop, and we are on every stage of it.
Evaluation is not a step you do once before launch. It runs before you ship, at the gate, and against live traffic afterwards, and what breaks in production becomes the next test. Open any stage to see what EvaliQA does there.
Define what good means
- Describe the behaviour you care about in plain language
- The wizard proposes the metrics that actually test it, and returns nothing when the goals are too vague to judge
- Save a judge once and reuse it across every plan in your workspace
Build the dataset
- Generate rows across five failure classes, or write them by hand
- Every row is reviewable before it ever scores anything
- Bring your own data, and export it again as CSV, JSON or JSONL
Run and read the failures
- Point it at any HTTP endpoint. Text, multi-turn, red teaming and voice run through one pipeline
- Open a failed row and read the judge's reasoning next to the input and the answer
- Turn the finished run into a written report with concrete recommendations
Gate the release
- Trigger a run from GitHub Actions, GitLab, Jenkins, CircleCI and three more
- One API token per integration, revocable on its own
- Compare against the previous run so a regression stops the release, not a person
Watch live traffic
- Send production traces over OTLP, no proprietary agent
- Session timelines with latency, tokens and cost per step
- Score real traffic with the same metrics the release was gated on
Catch the drift
- Alert rules on the metrics you already defined
- Delivered to Slack, Telegram, a webhook or email
- Severity per rule, so a nightly dip does not page anyone
Feed the failure back
- Save a production trace as a row in an eval dataset
- The next run tests the thing that actually broke in front of a user
- That is what closes the loop, and it is the only reason it is a loop
It plugs into the stack you already run.
Your own keys on any of 100+ model providers, a trigger for seven CI/CD tools, and results delivered where your team already looks. Nothing here asks you to adopt a new runtime or install an SDK.
100+ Model providers
- OpenAI
- Anthropic
- Google Gemini
- Mistral
- Meta Llama
- DeepSeek
- Cohere
- Groq
- xAI Grok
- Perplexity
- Hugging Face
- AWS Bedrock
- Azure AI
- Ollama
- Nvidia
- Replicate
Seven CI/CD tools
- GitHub Actions
- GitLab CI/CD
- Jenkins
- Bitbucket Pipelines
- CircleCI
- TeamCity
- Argo CD
Alerts · Delivery · Voice
- Slack
- PagerDuty
- Mattermost
- Discord
- Grafana
- Zapier
- Twilio
- ElevenLabs
- Deepgram
- Webhooks
- CSV export
Nobody has settled what good evaluation looks like.
So we wrote down the method we use, from the first honest run to a gated, monitored pipeline. Long, specific, and free to keep. No product pitch in the margins.
Evaluating AI Agents
A practical guide to grading an agent's path, not just its answer: tools and MCP, the trajectory, multi-turn conversation, trace analysis, and safety when the agent can act.
Get the guide
Evaluating RAG Systems
A practical guide to measuring retrieval and generation separately, building datasets you can trust, and turning one-off checks into a regression process.
Get the guideRun your first eval before this page loads twice.
One sentence, one key, one score. Then decide whether it is worth more of your day.