How our Artificial Intelligence projects run

Six stages, each with a clear deliverable. You know exactly where your project stands at every point.

The six stages

Most projects complete stages one through five in eight to twelve weeks. Stage six is ongoing for as long as you need us.

1

Scoping call

We spend 30 to 45 minutes on a video call learning what problem you want to solve. What decisions would change if you had a reliable prediction? Where does the data live? Who will use the output? These are the questions we focus on. By the end of the call we can usually tell you whether the project is feasible and give a ballpark timeline. There is no charge for this conversation.

2

Data audit

We connect to your data sources (databases, spreadsheets, API feeds, whatever you have) and run a structured assessment. We check volume, completeness, label quality and potential bias. This takes about five working days. The output is a short written report that tells you which datasets are usable as-is, which need cleaning, and which are missing entirely. If the data situation is too thin to support a model, we say so here and suggest ways to start collecting what is needed.

3

Experiment design

Before we write any training code, we agree on the success metric. For a churn-prediction model that might be precision at the 90th-percentile recall threshold. For a document classifier it could be macro-F1 above 0.85 on a held-out test set. We also define the baseline: what accuracy would a simple rule or the current manual process achieve? The model only goes forward if it beats that baseline by a margin worth the infrastructure cost.

4

Model development

This is the hands-on engineering phase. We iterate through feature engineering, architecture selection and hyperparameter tuning. For tabular data we typically start with gradient-boosted trees (XGBoost or LightGBM) because they train fast and give us a strong benchmark within days. If the problem needs deep learning (images, long documents, time-series with complex seasonality) we move to PyTorch. You receive a weekly update email with current metrics, a confusion matrix and a plain-language summary of what changed.

5

Deployment

We package the trained model into a Docker container with a REST API. Depending on your infrastructure, we deploy to AWS (ECS or Lambda), Azure (Container Apps) or your own on-premise Kubernetes cluster. We write integration documentation that your developers can follow without our help. Before handover we run a load test to confirm the endpoint handles your expected query volume with latency under 200 ms at the 95th percentile.

6

Monitoring and retraining

Models degrade as the world changes. We set up automated drift detection that compares incoming feature distributions against the training data. When drift crosses a threshold you agreed to in stage three, we trigger a retraining pipeline. For managed-AI clients this happens automatically each quarter. For one-off builds we hand over the retraining scripts and runbook so your team can do it independently.

What guides our work

Two principles shape every decision we make during a project.

Transparency over polish

We share raw experiment logs, not just polished slide decks. If a model is underperforming, you hear about it the same week, along with our plan to fix it. We have found that clients who see the messy middle of model development make better decisions about scope, timeline and budget. Hiding complexity helps nobody.

Every weekly update includes the actual metrics, not a summary written to sound encouraging. When numbers go down, we explain why and what we are trying next. When they go up, we explain what drove the improvement so you can judge whether it will hold on new data.

Your data stays yours

We never retain copies of client data after a project ends. During active work, data lives in an environment you control (your cloud account, your servers) or in an isolated tenant we provision specifically for you. We delete our access credentials within five working days of project close, and we confirm deletion in writing.

If you need a data-processing agreement or specific GDPR documentation, we provide that at the start of the engagement. We have template DPAs ready for UK and EU requirements, and we are happy to work with your legal team to adapt them.

Tools and technologies we use

We pick tools based on the problem, not on what is fashionable. Here is what we reach for most often and why.

Languages

Python for nearly everything. We use it for data wrangling (pandas, polars), model training (scikit-learn, PyTorch, XGBoost) and API serving (FastAPI). Occasionally we write performance-critical inference code in Rust when latency budgets are very tight.

Infrastructure

Docker containers deployed to AWS ECS, Azure Container Apps or bare-metal Kubernetes. For batch jobs we use Airflow or Prefect. We avoid vendor lock-in where possible: if you decide to move clouds next year, the migration should take days, not months.

Experiment tracking

MLflow for logging metrics, parameters and artefacts. Every experiment is reproducible: we pin library versions, record random seeds and store the exact dataset hash used for training. If you ask us to recreate a result from three months ago, we can.

Monitoring

Evidently AI for data-drift reports and Prometheus plus Grafana for infrastructure metrics. Alerts go to a shared Slack channel (or email, if you prefer) so both our team and yours see issues at the same time.

Ready to start?

The first step is a free scoping call. Tell us what you are working on and we will let you know how we can help.

Get in touch