Intelligence
AI & ML Development
Most AI projects do not fail on the model. They fail on everything around it — the data, the evaluation, and the fact that nobody agreed in advance what "working" meant.
We start with the decision you want the system to make, not the technique. That determines whether the answer is a fine-tuned model, a retrieval pipeline over your own documents, a classical algorithm that costs a thousandth as much to run, or a well-designed form that does not need AI at all. We will tell you when it is the last one.
The engineering around a model is the part that decides whether it survives contact with production: how data reaches it, how outputs are validated before anyone acts on them, what happens when the provider has an outage, and how you measure whether it is still right six months from now. That work is where we spend most of the budget, and it is the part most proposals leave out.
Scope
What the work includes.
Written into the scope before anything starts, so there is nothing to discover on the invoice.
Problem framing before technique selection
A written definition of what the system decides, what inputs it gets, and what an acceptable answer looks like — agreed before anyone picks a model.
Retrieval over your own knowledge
RAG pipelines that answer from your documents, policies and records, with citations back to the source so an answer can be checked rather than trusted.
Evaluation you can actually run
A test set and a scoring harness delivered with the system, so you can prove it still works after a model upgrade instead of hoping.
Guardrails and fallbacks
Output validation, confidence thresholds and a defined path for the cases the model should refuse. A system that fails loudly beats one that is confidently wrong.
Cost and latency modelling
Token and inference costs projected against your real volumes before commitment, not discovered on the first monthly invoice.
Questions
Before you commit.
Whichever your problem actually needs, and for most business problems that is an existing foundation model with good retrieval and careful prompt engineering around it. Training a model from scratch is rarely justified outside specialised domains with proprietary data at volume. When fine-tuning is the right answer we will say so, and when it is not we will not sell it to you.
Wherever you decide, and we design for that constraint from the start. If your data cannot leave your infrastructure, we build against models that can run inside it. If a hosted provider is acceptable, we will tell you exactly what is sent, what their retention terms say, and what we can turn off. This is a decision you make with full information, not one we make quietly for you.
Because you can measure it. Every system ships with an evaluation set drawn from your real cases and a scoring harness you can run yourself. Without that you are relying on the demo having gone well, which is how most AI projects end up quietly abandoned.
Ready to scope aI & ML Development?
Tell us what the system has to do. You get a fixed written scope and a quote that does not move after you sign.