We help organisations move from AI experiments to working systems — assistants grounded in your own content, models that forecast and classify, and the engineering discipline that keeps them accurate, governed and affordable in production.
Every engagement starts with a business question rather than a model. We look for the decisions and workflows where prediction or language understanding changes the outcome — and we are equally willing to tell you when a simpler rule or a better report would do the job.
Assistants and search experiences grounded in your own documents, tickets, policies and product data using retrieval-augmented generation — with citations, access control that respects existing permissions, and guardrails against unsupported answers.
Demand, revenue, churn, capacity and risk models built on your historical data, validated against real hold-out periods, and delivered with the confidence intervals that make them safe to plan against.
Extraction, classification and summarisation for invoices, contracts, forms and correspondence — turning unstructured paperwork into structured records your existing systems can process.
Image and video models for quality inspection, safety monitoring, counting and condition assessment — deployed to the cloud or to edge devices where latency and connectivity demand it.
Feature pipelines, model registries, automated evaluation, CI/CD for models, drift and cost monitoring, and rollback paths — so a model that works in a notebook keeps working a year after launch.
Data lineage, PII handling, evaluation sets, human-in-the-loop review, audit logging and clear model documentation — the controls your risk, legal and compliance colleagues will ask for.
Most AI programmes stall between the demo and the deployment. Our delivery model is built to cross that gap: we prove value on a narrow, measurable use case, then invest in the platform only once the value is real.
We stay deliberately portable. Where a managed cloud service is the pragmatic answer we will use it; where lock-in would cost you later, we build on open frameworks that run anywhere.
| Engagement | Typical duration | What you get |
|---|---|---|
| AI readiness assessment | 2–4 weeks | Prioritised use-case backlog, data gap analysis, target architecture and a costed roadmap. |
| Proof of value | 4–8 weeks | Working prototype on your data, measured against an agreed success metric, with a go / no-go recommendation. |
| Production build | 3–6 months | Deployed service, pipelines, monitoring, documentation and handover or managed operation. |
| Managed AI operations | Ongoing | Evaluation, retraining, incident response, cost optimisation and quarterly improvement planning. |
Durations are indicative starting points and are confirmed after discovery — they vary with data readiness, integration surface and compliance requirements.
The hard part of an AI project is rarely the model. It is the data you can trust, the integration into a real workflow, and the operational discipline to keep it honest once it is live.
That is the same engineering rigour BONITS has applied to application development, data warehousing and testing since 2014 — now pointed at a new class of problem.
