AI QA & Evaluation Harness
Let AI build fast. Do not let it ship blind.
We build evaluation harnesses that run repeatable checks against AI-built apps, RAG assistants, APIs, and workflows before changes reach real users.
What we protect.
Business-critical flowsCheckout, forms, bookings, quoting, dashboards, admin actions, and customer-facing replies.
AI answer behaviorGrounding, refusal, citation, handoff, prompt changes, and multi-turn drift.
Agent-built codeChanges made quickly by AI agents still need a stable release gate.
What you get back.
Test casesRepeatable scenarios that can run again after every change.
Failure evidenceScreenshots, logs, expected vs observed behavior, and reproduction notes.
Release confidenceA clear signal for what can ship and what must be fixed first.
What can an AI QA harness test? The harness is built around your highest-risk behavior, not generic testing theater. View technical details
Software checks
- Browser flows with screenshots and assertions.
- API inputs, outputs, schemas, and edge cases.
- Database-safe actions and rollback-sensitive workflows.
- Regression tests for AI-generated code changes.
AI behavior checks
- RAG retrieval and answer grounding.
- Prompt injection and role confusion cases.
- Refusal, clarification, and handoff behavior.
- Model/prompt regression after updates.
What is delivered first? A first harness should be small enough to ship quickly and important enough to prevent real damage. View technical details
First scope
We choose 5-20 critical scenarios, define expected outcomes, run them against your app or assistant, and produce a failure report plus a reusable test base.
AI QA harness
regression testing
LLM evaluation
browser automation
Inputs
Useful inputs include app URL, screenshots, target workflows, known bugs, sample prompts, expected answers, API examples, or anonymized conversations.
Playwright tests
RAG evals
CI checks
failure evidence
Quality Engineering
AI builds faster. Evaluation keeps it stable.
OpsBalance builds custom QA suites, browser regression runners, and adversarial testing harnesses for AI-generated code, RAG models, and complex workflows before updates break production.
RAG Evals
Continuous testing of retrieval databases (Qdrant) and answer grounding.
Browser Checks
Playwright/Selenium script runs simulating exact client order flows.
Prompt Checks
Detects logical drift and system instruction leaks across prompt iterations.
Tool Execution
Validates that AI-triggered functions do not dispatch bad database payloads.
JUnit & CI Logs
Native JSON/JUnit reports easily integrated into GitHub Actions pipelines.
Continuous Testing
Why AI-built apps require automated test runners.
Generative coding models make building software 10x faster, but they expand the QA validation bottleneck. We solve this by wrapping system updates in strict, testable boundaries.
| Testing Aspect | Traditional Manual QA Testing | OpsBalance Automated AI QA Harness |
|---|---|---|
| Coverage Speed | Slow (manual checks require hours of staff clicking) | Instant (runs hundreds of browser scenarios in seconds) |
| Adversarial Prompts | None (does not simulate hacker prompt injections) | 50+ red-team attacks executed on every build |
| Regression Safety | Prone to human fatigue on repeated tests | 100% stable regression monitoring checkpoints |
| Visual Audit Logs | Incomplete (requires staff to write custom bug cards) | Auto-captured video/screenshots on check failures |
| CI/CD Integration | Unlinked (relies on chat messages or manual signoffs) | Automated gates blocking bad commits in Git pipelines |
Verification Flow
Our systematic QA delivery model.
Designed for fast-moving AI startups, technical agencies, and teams shipping mission-critical client databases.
01
Map Risks
We trace your critical user paths: checkouts, tool-calls, or generative answer outputs.
02
Script Evals
We build automated Playwright scripts and custom semantic evaluation sets.
03
CI Integration
We integrate the test harness directly into your GitHub Actions or private build runner.
04
Triage Alerts
Your team receives immediate slack/telegram alerts and video logs if updates trigger regressions.
Operating Safety
Sandboxed test executions.
We strictly isolate testing datasets. Our browser scripts are run on staging databases with synthetic customer logs — keeping proprietary live data completely secure.
- No production database access or active credit cards requested for checkout tests.
- FOP-directed QA methodology: all test cases reviewed by real human engineers.
- Clean, exportable JUnit XML reports matching global standard CI formats.
Harden your AI-built systems.
Send us your active checkout URL or RAG system description. We will map critical failure vectors and return a complete Playwright or eval suite proposal.