AI-powered quality. Perfected by people.
Traditional QA. AI-accelerated pipelines. Behavioral evaluation of AI systems.
18+
Years in QA
3
Service tracks
8+
Testing disciplines
AI
At every layer
Choose the level of AI involvement that fits your team — from classic QA to full AI-driven pipelines.
Hands-on QA by experienced professionals. Manual testing, automation, functional, regression, API, mobile — everything your software needs before it ships.
QA accelerated by AI. Requirements in — test plans, automated Playwright specs, executed results, and filed bug reports out. Human-led methodology, AI-amplified speed.
Behavioral evaluation of LLMs and conversational AI. We don't just test if the AI works — we evaluate how it behaves. Hallucination detection, consistency analysis, tone drift, reasoning stability.
A requirements-first approach — tests are generated from what the software should do, not from what was built.
Acceptance criteria & requirements as input
AI generates scope, strategy & risk assessment
AI writes atomic test cases and executable specs
Tests run, results captured with pass/fail/skip
AI files structured bugs in Jira or Azure DevOps
# QA Report — Todo list management
Generated: 2026-04-04 00:34:11 UTC
## Summary
Passed: 4 | Failed: 1 | Skipped: 1 | Bugs filed: 1
## Execution Results
TC-001 Add a new todo item ✅ passed 843ms
TC-002 Mark a todo as complete ✅ passed 1102ms
TC-003 Delete a todo item ❌ failed 3021ms
TC-006 Submit empty todo ⏭ skipped
## Bug Report — TC-003
Delete button not reliably clickable via hover
Severity: minor Filed to: Jira / Azure DevOps
End-to-end quality coverage across the full testing spectrum — applied to all three service tracks.
01
Ensuring all functionalities work according to specified requirements and perform their intended functions correctly.
02
Assessing speed, responsiveness, and stability under various conditions to ensure optimal performance.
03
Evaluating UI and UX to ensure your software is intuitive, user-friendly, and meets user needs.
04
Ensuring software performs well across different devices, browsers, operating systems, and network environments.
05
Verifying that new code changes do not adversely affect the existing functionality of the software.
06
Testing APIs for functionality, reliability, performance, and security expectations.
07
Comprehensive testing across devices, OS versions, and network conditions for seamless mobile experiences.
08
End-to-end web app validation across browsers and devices for reliability, security, and usability.
Most QA frameworks test whether software does what it's supposed to. But how do you test an AI that doesn't behave deterministically?
We're building a behavioral evaluation framework for LLMs and conversational AI — classifying hallucinations, tone drift, reasoning instability, contextual failures, and more. Not pass/fail. Behavioral analysis.
Hallucination Detection
Identify when AI systems produce confident but incorrect or fabricated responses.
Behavioral Consistency
Evaluate if the AI responds consistently to similar prompts across sessions.
Reasoning Stability
Detect overconfidence, logical drift, and unstable reasoning patterns.
Tone & Safety Patterns
Track unexpected tone shifts, bias indicators, and safety-critical behaviors.
Whether you need traditional QA coverage, an AI-accelerated testing pipeline, or want early access to our AI behavioral evaluation framework — we want to hear about your project.