How we deliver Artificial Intelligence projects
Every engagement follows six stages. The first two are free. If we cannot find a viable path to measurable ROI in those stages, we say so and you owe us nothing.
The six stages
Discovery call
A 45-minute video call where you walk us through the business problem. We ask about the data you already collect, the decisions the model needs to support and the systems it will plug into. No slides on our side; just questions. By the end we have a shared document listing the problem statement, the success metric and any obvious data gaps.
Data audit
You share a sample of your data, typically one to four weeks of records. We run automated quality checks: missing values, class imbalance, distribution drift, label noise. The output is a two-page report that tells you whether the data can support the accuracy target we discussed. If it cannot, we recommend specific collection changes and revisit after you have had time to implement them. This stage takes three to five working days.
Proposal and fixed-price quote
Based on the audit we write a statement of work. It includes the model architecture, the training plan, the accuracy threshold, the delivery date and the total cost. You sign it or you negotiate. There are no hourly rates to guess at.
Model development
Our engineers train, validate and iterate. You get a weekly update email with current metrics plotted against the target. We use reproducible experiment tracking so every training run is logged and auditable. Most projects need two to six weeks at this stage, depending on data volume and model complexity.
Integration and deployment
We package the model as a Docker container with a REST API and deploy it to your infrastructure or to a managed cloud instance. We write the integration code that connects the API to your existing applications, whether that is an ERP system, a web front-end or a mobile app. Load testing happens before go-live; we simulate peak traffic at 2× your expected volume.
Monitoring and support
After launch we monitor prediction accuracy, latency and data drift for 90 days. If accuracy drops below the agreed threshold we retrain at no extra cost. After the 90-day window you can extend monitoring on a monthly retainer, or bring it in-house using the runbooks we hand over.
What happens behind the scenes
During stage four we train multiple candidate models and compare them on the same held-out test set. For tabular data we usually start with gradient-boosted trees because they train fast and handle mixed feature types well. If the data is sequential or spatial we move to recurrent or convolutional architectures.
Feature engineering is where most of the value comes from. Raw timestamps become day-of-week flags, rolling averages and lag features. Free-text fields become TF-IDF vectors or transformer embeddings. We document every transformation in a feature registry so your team can reproduce the pipeline without us.
Hyperparameter tuning runs on a Bayesian search. We set a compute budget in advance so you never get a surprise cloud bill. The best-performing configuration is frozen and exported as the production artefact.
Ready to start?
The discovery call and data audit cost you nothing. If we find a viable project, you will have a fixed-price quote within two weeks of first contact.
Book a discovery call