AI & Automation
AI applied where it changes a number, not where it demos well
Most AI projects fail on the same question: what decision does this change? We start there. If a model cannot be tied to a cost that falls or a conversion that rises, it is a demo, and demos do not survive a budget review.
Why AI pilots stall
The technology is rarely the blocker. The blocker is data that was never collected, evaluation nobody defined, and a pilot that was never designed to reach production. A model that works in a notebook and a model that runs inside your product are separated by most of the actual work.
- No baseline, so nobody can say whether the model is better than what it replaced
- Training data that does not resemble live traffic
- Costs that scale linearly with usage and were never modelled
- No fallback path for when the model is wrong or the provider is down
What you get
A defined decision and a baseline
Before any modelling, we agree what the system decides and what the current performance is. Without a baseline there is no way to justify the spend later.
LLM integration with guardrails
Retrieval over your own content, structured outputs, and evaluation on cases you care about. Plus the unglamorous parts: rate limits, retries, caching and a defined behaviour when the provider fails.
Automation of the work behind the work
Much of the return is not a customer-facing feature. It is document handling, classification, routing and reconciliation that currently costs a person several hours a day.
Cost modelled before rollout
Per-request cost projected against expected volume, so the unit economics are understood before launch rather than discovered on an invoice.
A proven path from idea to launch
The methodology behind every project we ship. No surprises, no black boxes.
Discovery & Strategy
We analyse your business needs and develop a comprehensive technology strategy.
Design & Planning
Our team creates detailed project plans and architectural designs for your solution.
Development & Testing
We build your solution using agile methodologies with continuous testing and feedback.
Deployment & Support
We deploy your solution and provide ongoing support and maintenance services.
What we build it with
Common questions
Do we need our own model?
Usually not. Most business problems are solved better and far more cheaply by a hosted model with good retrieval over your own data. Training something bespoke makes sense when you have proprietary data at volume and a task that general models handle poorly.
What about our data privacy?
We scope data handling explicitly at the start: what leaves your infrastructure, what is retained by a provider, and what must stay in-house. Some architectures keep sensitive data entirely on your side, and that constraint shapes the design rather than being bolted on.
How do you know it works?
An evaluation set drawn from your real cases, scored against the baseline we agreed at the start. Vibes are not an evaluation method, and neither is a demo that was run five times.
What if the model is wrong?
Every design includes what happens on a bad output: human review for high-stakes decisions, confidence thresholds, and a defined fallback when the provider is unavailable.
Still have a question about ai-driven solutions?
Tell us what you are building. You will get a straight answer on scope, timeline and cost within 24 hours.