Quality assurance for Every AI Surface

On-policy answers, every release

Support assistants fail quietly: a confident answer that invents a refund policy is worse than an outage, because nobody files a ticket — they just churn. HelixQA turns your help center and policy docs into evaluation context and scores every draft reply for groundedness: is the answer actually supported by the material the agent was given?

Severity tags keep the signal honest. A rude tone is logged; a fabricated cancellation fee is critical. Runs attach to suites, so the same scenarios re-execute on every prompt or model change, and regression comparison shows exactly which replies got worse before you promote.

Answers supported by retrieval

Retrieval-augmented products drift in ways unit tests never see: chunking changes, indexes go stale, the wrong document wins similarity search. HelixQA evaluates the answer against the retrieved context itself — not a fantasy reference — so 'the model sounds sure' never substitutes for 'the sources say this.'

Pair deterministic checks (required citations, schema, refusal format) with semantic judges for correctness and relevance. When an index rebuild tanks groundedness on last week's scheduled run, you learn it on Sunday night — not from a key account on Monday.

Instruction following under change

Internal copilots bake prompt behavior into people's daily workflows — meeting summarizers, code reviewers, ops assistants. A model upgrade that starts ignoring your output contract breaks downstream scripts and trust at once. HelixQA pins the contract as test cases: exact formats, tone rules, tool-use boundaries.

Before you roll a new model to the company, run the suite and compare against last month's baseline. The report separates 'the API broke' from 'the answer got worse' — so an SDK hiccup doesn't mask a real quality regression, and vice versa.

Quality gates for model endpoints

If you ship a model-powered endpoint, your users' tests are written in production. HelixQA treats the endpoint like any other AI product: register it, define suites of inputs with expected behavior, and execute runs from CI on every release candidate.

Latency budgets and schema checks run as deterministic gates; judges score the harder-to-assert properties. Webhooks fire on completion, so your pipeline can block a deploy on 'pass rate down 12% vs baseline' instead of a green HTTP status that means nothing about answer quality.

Multi-step behavior, end to end

Agents fail in the middle: the right first tool call, then a confidently wrong summary. HelixQA scores the workflow outcome — did the task actually complete? — and attaches per-step evaluation reasons so you can see where the trajectory derailed instead of only that the final message looks off.

Safety boundaries become explicit cases: the agent must refuse out-of-scope tasks, never call destructive tools without confirmation, and stay inside the allowed domains. Those rules are tests, not vibes — versioned in the suite and re-run on every change.

Pick a scenario. Ship with evidence.