Quality Process
QA as a delivery culture, not a department.
InWork's quality approach is embedded into the AI-first SDLC — quality checkpoints at every stage, from discovery through post-production, so defects are caught where they're cheapest to fix instead of after release.
Quality is a system, not a sprint
Quality is embedded across the AI-first SDLC.
Most teams treat QA as a checkpoint bolted onto the end of delivery — a separate sprint, a shared resource, a final pass before release. That model lets defects accumulate where they're most expensive to fix and leaves the team perpetually playing catch-up.
InWork embeds quality into every layer of the AI-first SDLC instead. Acceptance criteria are testable from discovery, architecture is peer-reviewed before code is written, tests ship with the code that creates them, and AI behavior is evaluated continuously. Quality is a property of how we build, not a phase at the end.
Stage → checkpoint
A quality checkpoint at every stage of delivery.
Discovery — requirements are written with testable acceptance criteria.
Architecture — peer review of the data model, API contracts, and integration design.
Sprint planning — QA tasks are in every sprint, not deferred to a separate QA sprint.
Development — unit tests ship with the code; a PR is blocked without passing tests.
Staging — full regression plus AI evaluation run before every deploy.
Production — monitoring, alerting, and AI sampling running from day one.
Post-production — monthly accuracy review for AI systems and a defect retrospective for software.
Why this matters for AI systems
The risks embedded QA removes.
AI fails in ways traditional software doesn't. Each of these failure modes is caught by a specific checkpoint in our process.
Our QA team
Dedicated engineers, not rotating resources.
Quality is owned by people who stay on your project and understand your domain.
Dedicated QA engineers per project
Quality is owned by engineers assigned to your project — not shared resources rotating across clients between sprints.
Domain-specific QA expertise
Automotive compliance testing, HIPAA-aware healthcare testing, and financial data validation — QA that understands the rules of the domain it's testing.
AI evaluation specialists
Engineers who understand both software testing and LLM behavior — so probabilistic AI outputs are evaluated as rigorously as deterministic code.
Before you ship a RAG system