Machine learning and LLM integrations aimed at one measurable job: classifying documents, answering questions from your own data, or forecasting what happens next.
The useful AI projects are narrow. Extracting fields from invoices, routing support tickets, answering staff questions from internal documents, predicting stock levels. We start by agreeing how success will be measured, then build the smallest system that meets it. For language tasks we integrate large language models with retrieval over your own content, with guardrails on what the system may and may not answer. For prediction tasks we train models on your historical data and monitor them once they are live.
AI and machine learning. Machine learning models and LLM integrations that do a specific, measurable job.
Who it suits, what you receive, how long it takes and how it is priced, before you commit to anything.
Who it is for
Companies with a repetitive, judgement-heavy task and enough examples of it being done well.
What you receive
A working model or AI feature, an evaluation report against the agreed measure, and monitoring in production.
Typical timeline
A four-week proof of value, then six to twelve weeks to production.
How it is priced
Fixed price for the proof of value, then a separate quote for production.
How the work runs
The same four stages apply to every engagement, so you always know what happens next.
Discover
Workshops on goals, users, risks and constraints.
A written scope and estimate
Design
Architecture decisions and a prototype tested with real users.
Your approval before build
Build
Two-week sprints, each ending in working software.
A demo every sprint
Operate
Deployment, monitoring, support and continuous improvement.
A support agreement with SLAs
One team from first workshop to long-term operation
