Build — AI
Practical AI, integrated into your stack.
Lead qualification, document processing, workflow automation, and LLM-powered interfaces that ship.
The problem
Most AI projects stall between the demo and production. The pilot works on curated examples, and then meets real inputs — a scanned invoice at an angle, a customer enquiry that is three questions at once, a document in the wrong language. The gap is rarely the model. It is that nobody scoped what happens when the model is unsure, who reviews the output, and what the system does on the day the provider changes.
What you get
- Lead qualification and routing against your actual criteria
- Document processing — extraction, classification, validation with a review step
- Workflow automation where a human stays in the loop on the decisions that matter
- LLM-backed interfaces integrated into the tools your team already uses
- Evaluation harness so quality is measured rather than asserted
- Fallback behaviour and cost controls defined before launch, not after the first bill
How it works
We start with the failure modes, not the happy path. What does the system do when it is unsure, who sees that, and what does it cost when volume triples. Then we build against a held-out evaluation set so quality is a number rather than an impression. Integration goes into your existing stack — the CRM, the inbox, the ERP — because an AI feature living in its own dashboard is a feature nobody uses. We are explicit about where a human should stay in the loop, and we will tell you when a rules engine would do the job better and cheaper.
Technical detail
Models and providers
Provider-agnostic by default, behind an interface, so a change of model or vendor is a configuration change rather than a rewrite. We benchmark candidates against your data rather than against a leaderboard.
Evaluation
A held-out set drawn from your real inputs, scored on the decisions that matter to the business rather than on generic accuracy. Regressions fail the build, the same way a broken test does.
Cost and limits
Token budgets, caching and rate limits designed in from the start. Retrieval where it reduces cost or improves grounding, not by default — most workflows do not need a vector database.
What's not included
- Model training from scratch. We fine-tune and integrate; if your problem genuinely needs a bespoke model, that is a research engagement and not ours.
- Fully autonomous decisions in regulated contexts. Where an outcome affects someone's credit, employment or legal standing, we build the review step in and we will not remove it.
- Chatbots as a deflection layer. If the goal is to stop customers reaching a human, we are the wrong partner.
Where we hand off, we tell you before the invoice arrives — not after.
Related services
You dream it. We create it.
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