Primary outcome
detect quality issues earlier and standardize review
What we ship
quality monitoring assistant, inspection workflows, defect taxonomy, and corrective action summaries
KPIs we report on
defect rate, review cycle time, rework, and audit findings
What "automating quality assurance with AI" actually means
Automating quality assurance with AI is not a single product you buy. It is a workflow you redesign around AI as the operating layer. The agent handles the high-volume, high-structure tasks. Humans handle edge cases, exceptions, and trust-sensitive decisions. The system is instrumented to measure defect rate, review cycle time, rework, and audit findings and improve weekly.
What changes by industry is the systems the agent integrates with, the data it retrieves over, the controls it operates under, and the KPIs it has to defend. The architecture is similar; the integration and the controls are different.
The architecture we use for AI quality assurance
- Frontier LLM — Claude, GPT-4-class, or Gemini. We benchmark candidates on a labelled test set during Discovery.
- Retrieval layer over your approved internal sources, with source citations on every output.
- Tool use for reads and writes against your operational stack (CRM, ERP, ticketing, data warehouse).
- Reviewer queue for low-confidence outputs. Confidence thresholds set per workflow.
- Evaluation harness — labelled test set, weekly accuracy reports, regression alerts.
- Versioned prompts and reviewer-action audit logs for traceability.
3 industries with a scoped engagement page for quality assurance. Each is a dedicated build with industry-specific systems, controls, and pricing.
How do you automate quality assurance with AI?+
We map your existing quality assurance workflow, identify high-volume and high-structure tasks, build an AI agent that handles those tasks, and route low-confidence cases to a human reviewer. The build connects to the systems your industry already runs on, runs against a labelled test set, and ships behind a reviewer queue before it sees production traffic. We measure defect rate, review cycle time, rework, and audit findings from day one and improve weekly.
What is the best AI agent for quality assurance?+
There is no single off-the-shelf "best" agent for quality assurance — the right architecture depends on the systems and data of your industry. We typically combine a frontier LLM (Claude, GPT-4-class, or Gemini) with a retrieval layer over your approved sources, tool-use for your stack, and a reviewer queue. We benchmark candidates against a labelled test set during Discovery and pick the model with the best accuracy/cost ratio.
What does AI quality assurance cost?+
Three phases, billed separately. Discovery sprint: $8k. Build engagement: $30k–$40k. Run retainer: $4k–$6k / mo. ~$52k–$90k typical year 1 (~80% take the run option, regulated workflows need ongoing controls). Pricing varies slightly by industry — see the industry-specific pages below.
How long does it take to deploy AI quality assurance?+
Thin-slice in production in ~6 weeks after Discovery, full Build phase over 8-12 weeks. By day 90, defect rate, review cycle time, rework, and audit findings is instrumented and you have a baseline against which to expand to adjacent workflows.
Which industries do you build AI quality assurance for?+
3 industries listed below have a scoped engagement page for quality assurance, each with industry-specific systems, controls, and KPIs. Common starting industries include Healthcare Providers, Pharmaceuticals, Biotechnology, and others. Don't see yours? We build for any sector — tell us about your workflow and we'll scope it.
What do we own, and what do you own?+
We own workflow design, prompts, retrieval architecture, evaluation harness, and weekly improvement. You own data access, policy, exception approval, and final commercial decisions. At the end of the engagement, every prompt, eval, and config is handed over — no lock-in.
Selected portfolio
Real builds tied to quality assurance
A rotating selection of engagements where quality assurance was a primary driver, drawn from our active portfolio. Sectors and scope are accurate; client identities are withheld under engagement NDAs.
Q3 2025
Radiology workflow application — case handling and reporting
Medical imaging operator · Europe
Application supporting radiology workflow: case intake, structured reporting, document handling, and quality-assurance loop. Designed for regulated medical-imaging context with audit trail and role-based access.
- Web app + secure storage
- Structured reporting
- Audit-trail compliance
Q2 2026
Authenticated remote voting platform — AGM resolutions, audit trail, EN/AR bilingual
Mid-market property operator · GCC region
Purpose-built e-voting system: per-unit cryptographic authentication, AGM resolution console for admins, real-time tally, full per-vote audit log. Federated identity with the OA management platform so owners use one login. Bilingual EN/AR from day one.
- Next.js + tRPC
- Per-unit auth + audit trail
- Bilingual EN/AR (next-intl)
Q4 2025 → Q1 2026
Owners-association management SaaS — 55+ screens, 47 normalized tables
Mid-market property operator · GCC region
Full operational backbone for a property operator running multiple owners associations: properties, units, owners, accounting, service charges, budgets, maintenance, violations, and a resident-facing community portal — replacing a patchwork of spreadsheets and disconnected accounting tools.
- Next.js + tRPC
- PostgreSQL · Drizzle ORM
- JWT federated identity
Client identities withheld under engagement NDAs. Sector, geography, and scope are accurate. Full case studies on request.