How we deliver Artificial Intelligence projects

Discovery and data audit

We spend two to five days on-site (or via screen-share) examining your existing data sources, database schemas, and data-quality metrics. The goal is to answer two questions: do you have enough labelled data to train a useful model, and is the data clean enough to trust? We write a short report that lists every gap we found, estimates the effort to close each one, and recommends whether to proceed.

If the data is not ready, we help you fix it. That might mean writing ETL scripts, setting up a labelling workflow with your subject-matter experts, or connecting disparate systems through an API layer. We charge this work separately and at a lower rate than model development, because it is important but less specialised.

Problem framing

AI can answer many kinds of questions, but it answers them differently. Is this a classification problem or a regression problem? Should the model predict a single number or a probability distribution? What is the cost of a false positive compared to a false negative in your business context? We sit down with your domain experts and translate their operational knowledge into a formal machine-learning problem statement, complete with an evaluation metric that maps directly to a business outcome you care about.

Rapid prototyping

Over two to four weeks, we build a minimum viable model. This is not a polished product; it is a Jupyter notebook, a set of evaluation charts, and a candid assessment of where the model succeeds and where it struggles. We present this to your team in a working session where you can interrogate individual predictions, spot patterns the model misses, and suggest features we should add. If the prototype does not meet the agreed performance threshold, we stop here and you owe nothing beyond the prototyping fee.

Production engineering

Once the prototype proves the concept, we rebuild it for production. That means containerising the model, writing automated tests, setting up a CI/CD pipeline, and deploying to your preferred cloud provider (we work with AWS, Azure, and GCP). We also build a monitoring dashboard that tracks prediction latency, input-data distributions, and model accuracy over time so you can see the moment performance starts to degrade.

Typical deployment takes three to six weeks depending on the complexity of your existing infrastructure and the volume of predictions the model needs to handle. We document every component in a runbook your own engineers can follow.

Validation and handover

Before we call a project complete, we run a two-week shadow period where the model produces predictions in parallel with your existing process. Your team compares the two sets of outputs and flags any cases where the model's recommendation would have led to a worse outcome. We use those cases to fine-tune the model and update the decision thresholds. At the end of this period, we hand over all source code, trained weights, documentation, and a model card that describes the model's intended use, known limitations, and ethical considerations.

Ongoing support and retraining

Machine-learning models decay. Customer behaviour shifts, product catalogues change, sensor calibrations drift. We offer a monthly retainer that covers scheduled retraining on fresh data, infrastructure maintenance, and up to eight hours of ad-hoc support. If you prefer to bring maintenance in-house, we run a half-day knowledge-transfer workshop with your engineering team and remain available for occasional consulting at an hourly rate.