AI Assurance Platform

Automated testing of AI applications, built for QA

Test your Agentic, RAG, and Chatbot apps continuously to catch regressions before they ship, with automation that lets your QA team own AI testing at scale.

You already run QA on traditional software. TestSavant.AI gives you the practice, coverage, and evidence to run it on non-deterministic, LLM-based AI applications.

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AI Regression Testing

Catch regressions before your users do.

Every model update, prompt edit, and data refresh can degrade your AI without anyone noticing.

TestSavant.AI runs your test plans on a repeatable cadence, scheduled or triggered from your CI/CD pipeline. The failure-rate trend across runs tells you whether your AI is improving or getting worse, and category and policy-label views pinpoint a regression as soon as it appears.

Trend over time

Reliability is tracked run over run, so a gradual decline shows up well before it reaches users.

Repeatable runs

Schedule them daily, weekly, or monthly, or fire them automatically when a model version or prompt is updated.

Release checks in CI/CD

A failing run blocks the release, like any other pipeline check.

Test Coverage

Coverage for how AI fails.

Twenty evaluators come pre-built and ready to run, so your QA team has coverage on day one. Each one targets a specific way your AI can go wrong, mapped to your application type. Or create your own evaluators, without writing a single line of code.

Agentic AI

Does your agent complete the task, in the format you specified, while keeping its instructions to itself?

Action Item Accuracy & CompletenessConfirms the agent captures every action it should and gets each one right.

Tool calls / tool useVerifies the agent calls tools correctly and returns valid, schema-conformant structured output.

System Prompt ExposureProbes whether the agent can be made to reveal its system prompt or hidden instructions.

RAG Applications

Does your system answer from your sources, and only your sources?

Reference-Based HallucinationCatches answers that wander from your source documents.

Factual ConsistencyHolds every answer against your source material.

Off-Topic ResponseKeeps each answer on the question the user asked.

Shared Quality

The risks that cut across everything you ship.

BiasSurfaces responses that treat people differently across groups.

Sensitive Information ExposureFlags PII and confidential data that slip into responses.

Regulatory ComplianceMeasures outputs against the rules your industry has to follow.

Adaptive AI Test Generation

Generate the tests no one could write by hand.

If you prompt a general-purpose LLM for test cases, you get generic tests that won't fit your app.

With TestSavant, every run makes the next run smarter.

The TestSavant adaptive generator writes application-specific test cases, each tuned to risks in your system. The engine studies the results and aims the next batch at the high-risk categories where your application is failing, exposing your weakest points.

Because testing concentrates on your highest-risk categories, your test budget is only spent where it matters most. You can even preview the projected cost before you commit to a full run.

Runtime Guardrails

Testing finds the failures. Runtime guardrails stop them in production.

Wrap your live AI calls with guardrails to enforce the behavior you expect.

When testing turns up a failure, feed those cases back in to strengthen the guardrail that handles them.

See runtime guardrails →

policy_guardrails.py

from testsavant.guard import InputGuard, OutputGuard

input_guard = InputGuard(api_key=API_KEY, project_id=PROJECT_ID)
output_guard = OutputGuard(api_key=API_KEY, project_id=PROJECT_ID)

prompt = "Summarize the latest release train for executives."
if input_guard.scan(prompt).is_valid:
    completion = llm.generate(prompt)
    verdict = output_guard.scan(prompt=prompt, output=completion)
    if verdict.is_valid:
        ship(completion)
    else:
        escalate(verdict.results)

The Assurance Methodology

The AI Assurance methodology.

A working AI testing program runs the same loop on every release, and TestSavant.AI covers each step. AI testing finds the failures, runtime guardrails enforce the fix, re-runs show the trend, and every run leaves evidence you can hand up.

Test

Run your test suite against your live AI application

Find Failures

Pinpoint regressions by category and policy label

Enforce the Fix

Deploy runtime guardrails targeting the failure mode

Re-test & Track

Confirm the fix holds and watch the trend across runs

Evidence

Export proof that every release met your quality bar

Build vs. Buy

Anyone can build an evaluator. We give you the whole program.

Anyone can vibecode an evaluator. The hard part is the program that turns your testing into a practice you can run on every release.

Capability

Build it yourself

TestSavant.AI

Test case generation

Manual, generic prompts

Adaptive AI: thousands of app-specific cases per run

Evaluator coverage

Whatever you coded

20 pre-built evaluators, ready day one; build your own no-code evaluators

Regression tracking

Manual comparison, if any

Automatic trend across every run

CI/CD release gates

Custom scripting required

Per-policy-label checks built in

Runtime guardrails

Build and maintain yourself

SDK deploy, fed by test failures

Audit evidence

None

Exportable report every run

Why We're Different

Most AI tooling lives at the developer and prompt stage. TestSavant.AI lives at the QA and release stage.

Developer and prompt tools help you build and observe your AI.

TestSavant.AI is built in QA's language for the team that owns the decision to ship, where releases get tested, gated, and signed off.

Proof

Proof you can hand to anyone.

A regression caught before release

You push a model upgrade and the failure rate climbs. The trend report flags it on the next run, so the regression never reaches a user.

A violation stopped in production

A live request tries to pull your system prompt. The guardrail blocks it and logs the policy it broke, the input that triggered it, and the configuration that caught it.

Evidence your CTO can sign off on

Every run produces a report you can export: a plain-language risk summary your QA lead hands to compliance or an exec to approve the release.

Get Started

See it come together.

Book a walkthrough with our team and see the full methodology on an agentic, RAG, or chatbot use case like yours.