Skip to main content
TestSync Lab

QA lane — live now

AI testing that protects the release

Senior QA for AI features product teams are already shipping: chatbots, copilots, RAG search, and AI workflows. We catch hallucinations, broken tools, weak retrieval, and release regressions — before your users do.

Most vendors demo prompts. We test AI like a release: happy path, failure path, jailbreaks, and a golden set you can re-run when the model changes.

Talk about AI testingFree QA Audit

What we test

AI surfaces that already hit customers — chat, search, agents, and workflows.

  • Chatbot & copilot QA

    Conversation quality, guardrails, and tool actions — not just “does the widget open?”

    • • Intent coverage across real user journeys
    • • Tone, refusal, and escalation checks
    • • Tool/function-calling failures and side effects

  • RAG & knowledge QA

    Grounded answers from your docs and tickets — with clear failure when retrieval is weak.

    • • Citation / source faithfulness spot-checks
    • • Stale, missing, and conflicting document cases
    • • “I don’t know” behavior when context is thin

  • LLM evals & prompt regression

    Repeatable eval sets so a model or prompt swap does not silently break production.

    • • Golden-set scoring before you change models
    • • Prompt and system-instruction regression
    • • Release gates you can plug into CI

  • AI product & workflow QA

    End-to-end quality for AI-built features: triage, summaries, classification, draft replies.

    • • Human-in-the-loop paths and override behavior
    • • Structured output contract checks
    • • Privacy, PII leak, and abuse scenarios

How AI testing runs here

Same QA discipline as your retainer — applied to models, prompts, and retrieval.

  1. 01

    Map AI risk on your product

    We list the AI surfaces (chat, search, agents), failure modes that hurt revenue or trust, and the thinnest useful test plan for the next release.

  2. 02

    Build a golden set you own

    Real prompts, expected behaviors, and severity. This becomes the regression memory every time you swap a model or tweak a prompt.

  3. 03

    Run release gates with QA retainers

    Findings go to Slack/Jira like the rest of your QA. Run a focused AI Testing Sprint before launch — or add AI regression beside Growth or Scale.

How we price AI testing

Basic, Growth, and Scale stay predictable for product QA. AI surfaces need a short discovery before a fair quote — so we sell a scoped sprint or a retainer add-on, not a vague line item inside Basic.

Most teams start with an AI Testing Sprint, then add ongoing AI regression beside Growth or Scale.

  • Best first buy

    AI Testing Sprint

    Scopedfixed quote

    Time-boxed QA for one AI surface — chatbot, RAG, or workflow — with a written risk map and golden-set starter.

    • AI surface + risk discovery
    • Golden-set starter (prompts + expected behavior)
    • Happy path, failure path, jailbreak / abuse checks
    • Written findings with severity and repro
    • Fixed quote before we start
    Scope AI testing

  • Pairs with Growth or Scale

    AI + QA retainer add-on

    Custommonthly

    Ongoing AI regression beside your monthly QA package — so model and prompt changes do not surprise production.

    • Add-on to Growth / Scale (Basic on request)
    • Prompt & retrieval regression each cycle
    • Chatbot / RAG release smoke
    • Shared Slack and weekly status
    • Scoped month to month
    Add AI to QA

Classic monthly QA packages stay on /pricing. AI testing is quoted after discovery.

AI testing FAQ

Straight answers on packages, add-ons, and when to start.

  • Do you test AI chatbots and LLM products?

    Yes. AI testing is a live QA lane: chatbots, copilots, RAG search, prompt regression, and AI workflow quality. We build golden sets, check hallucinations and jailbreaks, and gate releases the same way we gate the rest of your product.

  • How is AI testing priced compared with monthly QA retainers?

    QA stays on clear monthly packages ($999 / $1,899 / $2,799). AI testing is a scoped sprint (fixed quote) or a monthly add-on beside your retainer. You get a written scope before we start — not an open-ended AI experiment billed by the hour.

  • Should AI testing sit inside Basic, Growth, or Scale — or stay separate?

    Keep it separate. Monthly QA packages stay predictable for product coverage. AI surfaces need discovery before a fair quote, so we sell an AI Testing Sprint or an AI + QA add-on (best beside Growth or Scale). We do not bury open-ended AI work inside Basic.

  • Can you also build AI features, or only test them?

    Testing is the default offer — that is what most buyers need first. If you also need a small feature built, we can scope build + test together. Custom software and BPO stay on the later roadmap.

More retainer questions: full FAQ →

Shipping a chatbot or RAG feature? Tell us the product, the model stack, and the next release date. We will say what to test first.

Talk about AI testing