AI Quality Assurance Consulting

Prove your AI works — at the scale humans can no longer review

Most teams ship AI without a way to prove it works. Outputs look fluent, demos go well, and then no one can answer the only question that matters in production: how accurate is this, against what benchmark, and who is allowed to change it. FastTech’s AI quality assurance practice closes that gap. We build the evaluation, calibration, and monitoring layer that lets you measure AI quality at volume and put the controls in the hands of the people who own the outcome.

AI QA at scale, not a one-off test

We treat evaluation as a system, not a single pass before launch. Our work spans defining what “good” means for your specific use case, establishing a human benchmark to score against, calibrating the AI pipeline question by question until accuracy is defensible, and standing up monitoring so quality holds as data and models drift over time.

The deliverable is not a slide of vanity metrics. It is a working capability your team operates after we leave: clear accuracy figures, transparent scoring, and a tuning loop a non-technical owner can run without engineering support. This applies to LLM systems, multi-modal pipelines spanning audio, speech-to-text and language models, autonomous agents, and any AI making decisions at a volume people can no longer manually sample.

How LLM evaluation works in practice

The method is straightforward and proven. Analysts manually evaluate a well-distributed sample to set a statistical benchmark. The AI pipeline is then scored against that benchmark, question by question. Keywords, descriptions, and score normalisation sharpen interpretation over a few iterations until the configuration is trustworthy. The optimised configuration ships to score every interaction in production — not a sample of them.

We deliberately cut dashboards down to what drives action: accuracy, high/medium/low confidence labels, recommended next steps, and accuracy evolution across cycles so trust and adoption build over time. The point is not to admire a chart. It is to give the owner of the outcome a control they can actually pull.

Evidence, not assertion

The flagship engagement: an AI calibration framework for a European telecom operator. Our team took contact-centre QA from under 1% manual sampling to 100% automated coverage, reaching 82% accuracy against a human benchmark and a 72% automation score across 51 accounts and 9,400 users, with a 50% improvement in process efficiency. The platform was architected to scale to 9M+ daily interactions and 20K+ users. Crucially, QA managers tune the AI themselves, with no engineering in the loop.

Where this work sits inside a fresh 0-to-1 build, it runs on our WASP method — a structured sprint that validates the approach against real users in five business days before a heavier investment follows.

If you need to trust and tune your AI in production — and prove its quality to the people who depend on it — that is exactly the gap we close.