AI Products Built & Verified.
By a QA Architect.

Most AI agencies generate code quickly but leave testing to the client. With 12+ years in data QA and enterprise AI QA architecture, we design test strategies, error handling, and model evaluation sets before shipping. You get software that handles edge cases, network drops, and prompt variations gracefully.

We default to Next.js (App Router), TypeScript, Tailwind CSS, and Supabase or PostgreSQL for the database layer. For AI features we use the Anthropic or OpenAI APIs, structured JSON outputs, deterministic Python scripts where math or calculations are required, and pgvector or Pinecone for vector retrieval. Everything deploys to Vercel or modern container infrastructure.

We separate deterministic calculations from generative narration. Complex math or data logic is executed in code (Python or TypeScript), never trusted to the model. For privacy, we use zero-retention API agreements, enforce strict input sanitization, and never transmit sensitive user data without explicit consent.

Our AI QA & LLM Audit delivers in 3–5 business days. Our MVP tier delivers in approximately 2 weeks (Week 1: architecture, design, and core build; Week 2: deep QA, iteration, and handoff). Timelines start when the scoping brief is approved.

Every tier includes a dedicated support window (14–60 days depending on tier). Beyond that, our Growth Retainer provides ongoing development, regression testing, prompt quality monitoring, and SEO support month-to-month.

Yes. For teams with an existing app or prototype built with AI tools, we start with our AI QA & LLM Audit to inspect failure points, output reliability, and code structure before recommending or implementing changes.

You do, 100%. Upon final payment we transfer all code, assets, test documentation, and prompt configurations. We retain no licence or rights to your product.

Yes, always. We sign a mutual NDA before any scoping call or before accessing proprietary repositories, prompts, or data.

Absolutely. We have built and maintain four live SaaS products — saasdb.app, briefstock.ai, feedalyze.net, and llmdb.app. Browse them at /apps. Each demonstrates concrete QA practices: deterministic financial calculations, prompt evaluation sandboxes, and automated data validation.

We stand behind our work. If we deliver something that doesn't match the agreed scope, we fix it at no extra cost. If we fundamentally miss the mark, we offer a refund. That is why our scoping and acceptance criteria are agreed on before code is written.

Yes — many of our clients are non-technical. Our scoping process translates your product vision into concrete technical specifications and test cases. You never need to write user stories or debug code; we manage the technical execution and quality verification end-to-end.

Yes. We build cross-platform mobile apps for iOS and Android using React Native and Expo, with end-to-end device testing across viewports, offline states, and push notification flows.