From a promising idea to a model that earns its keep in production.
Most AI projects stall between the demo and the deployment. We build the unglamorous parts properly — the data pipeline, the evaluation harness, the guardrails, the cost controls — so the system you launch keeps working after the launch. Every engagement starts with the question most vendors skip: is AI actually the right tool for this problem?
What We Do
Inside our ai & machine learning work
The pieces that make up an engagement — pick the ones you need.
RAG Chatbots & Assistants
Assistants that answer from your own documents, policies and data, with citations so staff and customers can check the source. We handle chunking, embeddings, retrieval quality and the evaluation set that proves it works.
Agentic Workflows
Multi-step automations that use your tools — reading an inbox, pulling a record, drafting a reply, filing the result. Built with retries, human approval gates and hard cost caps so a loop can never run away with your budget.
Computer Vision & OCR
Image and document understanding: defect detection, damage assessment, form and invoice extraction, ID verification. Trained on your data, measured against your acceptance criteria.
Predictive Models
Forecasting demand, churn, pricing and risk from your historical data — with honest error bars and a plain explanation of what the model can and cannot tell you.
LLM Integration
Adding AI to a product you already run: a summariser, a search box that understands questions, a drafting assistant. Provider-agnostic, so you are never locked to one vendor's pricing.
Evaluation & Guardrails
A test set that reflects real usage, automated scoring on every change, prompt-injection defences, PII redaction and audit logs. This is what separates a demo from a system you can defend.
Where It Helps
Problems this solves
Customer support that scales
A retailer cut first-response time from hours to seconds with a RAG assistant grounded in their returns policy and order data — and routed anything uncertain straight to a human.
Document processing
Contracts, invoices and claim forms read, extracted and indexed automatically, with a confidence score that decides what a person still needs to check.
Search that understands intent
Internal knowledge bases where staff ask a question in plain language instead of guessing the right keyword.
Operational forecasting
Stock, staffing and cash-flow projections built from your own history rather than a generic industry template.
How We Work
From first call to running system
- 1
Feasibility check
We look at your data and tell you honestly whether AI helps here, what accuracy is realistic, and what it will cost to run each month.
- 2
Prototype
A working slice on your real data within two to three weeks, with an evaluation set so 'it seems better' becomes a number.
- 3
Harden
Guardrails, rate limits, cost caps, logging, fallbacks and a human-in-the-loop path for the cases the model should not decide alone.
- 4
Ship & monitor
Deployment, dashboards for quality and spend, and a retraining or re-evaluation schedule so performance does not quietly drift.
Tools We Use
The stack behind this service
Questions
What clients ask us first
Will our data be used to train someone else's model?
No. We use enterprise API tiers with training disabled by contract, or run open models on your own infrastructure. Which of the two we recommend depends on your sensitivity requirements and budget, and we will explain the trade-off before you commit.
How much does it cost to run each month?
It depends on volume and the model tier, and we model this before you build, not after. A typical internal assistant for a small team runs on a modest monthly budget; a customer-facing system at scale costs more, which is why cost caps and caching are part of the build rather than an afterthought.
What if the model gets something wrong?
It will, sometimes — any honest vendor says so. The engineering question is what happens next: confidence thresholds, citations the user can check, a human approval step for consequential actions, and logs that let you find out why.
Do we need a huge dataset to start?
For RAG and LLM work, usually not — your existing documents are often enough. For custom vision or predictive models you do need labelled examples, and part of the feasibility check is telling you how many before you spend anything.
Ready to start with AI & Machine Learning?
Tell us what you're trying to build and we'll come back with an approach and a number.