AI Assurance Platform

One platform for the full AI assurance methodology

Test, harden, and release your agentic, RAG, and chatbot apps, end to end, with one platform owned by your QA organization.

Manually testing your AI, or vibecoding one-off evaluators, gives you a patchy record of what was checked and whether the AI improved. TestSavant.AI is the centralized platform that operationalizes testing AI into a repeatable practice.

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The Assurance Methodology

Test. Fix. Re-test. Prove.

A working AI assurance program depends on running the same loop on every release. Automated AI testing finds the failures, runtime guardrails enforce the fix, re-runs show the trend, and every run leaves evidence you can defend.

TestSavant.AI is the fabric that connects every step of that loop in one platform, owned by your QA organization.

Test

Run your test suite against your 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

Defensible by design

Trust every verdict.

Any test can come back pass or fail. When you vibecode your own evaluator, you have no way to know whether that pass or fail is right, so you cannot trust it, let alone defend it to leadership.

Tests you can rely on.

TestSavant.AI guides how you build each evaluator, so it focuses on the right risks.

Guided risk definition

TestSavant.AI guides you to define the risks important to your application, so your tests focus on how it can fail.

Focused testing

The platform helps you define a taxonomy of those risks, so testing concentrates on your highest risks and your spend goes where it counts.

Weighted by severity

Set your own pass and violation labels and how severe each one is, so reporting reflects the failures you care about most.

Findings you can defend.

Each evaluator is grounded in your behavior rules and reference documents, so its verdicts judge against how your app is meant to behave, giving you a trustworthy result you can stand behind.

Plain-language reasoning

Every verdict explains why it passed or failed, in words anyone can read.

Pinpointed failures

The tool call or span that triggered the verdict is highlighted, so the finding points to evidence.

Policy and risk on record

Each verdict carries the policy it violated and a risk level, so failures are weighed by what they cost.

Capabilities

Every part of the methodology, in one platform.

One connected practice, from first test to sign-off.

Every LLM app type

Test agentic apps for tool misuse, RAG apps for ungrounded answers, and chatbots for broken intent.

AI test generation

Describe your app and get thousands of risk-specific tests, aimed where it breaks.

AI regression testing

Catch the regression a model or prompt change slips in, before your users do.

Test coverage & evaluator library

Cover known AI failure modes from day one with 20 built-in evaluators, then add your own.

AI red teaming

Find prompt injection, tool abuse, and multi-turn agent attacks before attackers do, aligned to OWASP LLM.

Test at every level

Black box at the interface, grey box at the API, the system prompt, or the LLM itself, so you can pinpoint which layer a failure comes from.

Runtime guardrails

Stop bad behavior in production, on rules strengthened by the failures your testing finds.

Release readiness & evidence

Hand up a release-readiness report and the evidence behind every ship decision.

Integrations

Built to fit your stack.

TestSavant.AI wires into the tools your engineers already work in, from your pipeline and tracker to your production code and infrastructure.

terminal

# Install the harness
pip install ts-harness

# Run a test suite against your project
ts-harness run \
  --project $PROJECT_ID \
  --suite regression \
  --fail-on-violation

# Output: pass/fail per evaluator, violations listed

.github/workflows/ai-tests.yml

name: AI quality gate
on: [push, pull_request]

jobs:
  ai-tests:
    runs-on: ubuntu-latest
    steps:
      - name: Run AI test suite
        run: ts-harness run \
          --project ${{ secrets.TS_PROJECT_ID }} \
          --suite regression \
          --fail-on-violation
        # Non-zero exit blocks the merge

testsavant.config.json

{
  "integrations": {
    "jira": {
      "enabled": true,
      "project": "AI-QA",
      "issue_type": "Bug",
      "on_violation": "create_issue"
    }
  }
}

# Each run's violations become Jira issues
# automatically assigned to your team

app.py

from testsavant.guard import OutputGuard

# Initialise once at startup
guard = OutputGuard(
    project_id=PROJECT_ID,
    policy="production-v2"
)

# Wrap every LLM call
result = guard.scan(prompt, completion)
if result.is_valid:
    ship(completion)
else:
    handle_violation(result.reason)

deployment options

# Option A — TestSavant.AI cloud (default)
No infrastructure to manage.
Data processed in our managed cloud environment.

# Option B — your own infrastructure
docker pull testsavant/platform:latest

environment:
  TS_DATA_BOUNDARY: customer
  TS_STORAGE:      s3://your-bucket/ts-data
  TS_REGION:       us-east-1

# Your data never leaves your boundary.

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.