There is a wide gap between an impressive AI demo and a system that holds up in production. Demos run on curated inputs with a human ready to intervene. Production systems face malformed documents, ambiguous questions and users who will act on a confident wrong answer.
We build AI features that are scoped against a specific business metric — hours saved, error rate reduced, response time improved — and we are direct about where the technology is not yet dependable enough for the use case.
Where it reliably works today
Document processing. Extracting structured data from invoices, purchase orders, contracts, KYC documents and forms. This is the most consistent return we see: high volume, well-defined output, and straightforward validation against existing records.
Retrieval-based assistants. Answering questions from your own documentation, policies or product data. Grounding responses in retrieved source material — with citations — makes answers checkable and dramatically reduces fabrication compared with an ungrounded model.
Classification and routing. Categorising support tickets, prioritising leads, flagging anomalous transactions. Narrow, measurable, and easy to evaluate against historical data.
Forecasting. Demand planning, inventory optimisation and churn prediction, where sufficient clean history exists.
Evaluation before deployment
We build an evaluation set from your real data before shipping anything — inputs with known correct outputs, including the awkward cases. That establishes a measured accuracy figure rather than an impression, sets the threshold at which output needs human review, and provides a regression check when models or prompts change.
Without this you cannot tell whether a change improved the system or quietly degraded it.
Keeping humans in the loop
For anything consequential we design a review step rather than full automation. Extracted invoice data above a value threshold goes to a person; low-confidence classifications get queued rather than actioned. This is what makes AI deployable in finance, healthcare and legal contexts, where a silent error is far more costly than a slower process.
Data protection and cost
Where data is sensitive we discuss what leaves your environment. Options include enterprise API agreements with no-training guarantees, regional deployment, or self-hosted open-weight models where the requirement justifies the operational overhead.
Token costs are modelled per transaction before build so unit economics are understood upfront, and we apply caching, prompt efficiency and appropriate model tiering rather than defaulting to the largest available model.