How we deliver Artificial Intelligence projects

Every engagement follows the same six-step framework. It keeps costs predictable, timelines honest, and lets you pull the plug early if the data does not support a viable model.

The six steps

Below is the path a typical project takes from your first phone call to a model running in production. Timelines vary by complexity, but the sequence stays the same.

1

Discovery call

A 30-minute video call where you describe the business problem and we ask pointed questions about the data you already collect. We are looking for three things: volume (how many records), freshness (how often the data updates), and labelling (whether outcomes are recorded). If any of those three is missing, we flag it immediately so there are no surprises later.

This call is free. You do not need to prepare slides or a formal brief; a screen-share of a typical spreadsheet or dashboard is enough.

2

Data audit and feasibility report

We request a sample of your data, usually between 5,000 and 50,000 rows depending on the domain. Over two weeks our engineers profile the data for completeness, distribution skew, duplicates, and potential leakage. The output is a 12-to-18-page report containing statistical summaries, a preliminary feature-importance ranking, and a go/no-go recommendation.

This phase costs a fixed fee agreed in writing before we start. If the report concludes that the data cannot support a useful model, you receive the full report and owe nothing further.

3

Model development

Once you approve the feasibility report, we begin building. For tabular problems we typically benchmark gradient-boosted trees against a small neural network. For text and image tasks we fine-tune a pre-trained transformer on your labelled examples. We track experiments in MLflow so every hyperparameter choice is recorded and reproducible.

You receive a weekly update email with current accuracy, precision, and recall numbers. If performance plateaus below the target we agreed on, we discuss whether to gather more data, adjust the target, or stop the project.

4

Validation and testing

Before deployment we run the model against a held-out test set that it has never seen. We also perform fairness checks: if the model makes decisions about people, we test for bias across protected characteristics. Results go into a model card document that you can share with your compliance or legal team.

For safety-critical applications (medical triage, structural inspection) we add an adversarial testing phase where we deliberately feed the model edge cases and corrupted inputs to measure how gracefully it fails.

5

Deployment

We package the model as a containerised REST API and deploy it on your preferred infrastructure. Most clients use Azure UK South or AWS eu-west-2; we also support on-premises GPU servers for clients with strict data-residency requirements. Typical API response time is under 120 milliseconds for a single prediction.

Integration usually takes one to two sprints, depending on how your existing software consumes the predictions. We provide OpenAPI documentation and a Postman collection so your developers can start testing immediately.

6

Monitoring and retraining

A deployed model is not a finished product. Data distributions shift, business rules change, and accuracy degrades over time. We set up automated monitoring that tracks prediction confidence, input-feature distributions, and latency. When drift exceeds a threshold, the system triggers a retraining pipeline that revalidates the model on fresh data before promoting it to production.

You get a live dashboard (Grafana-based, accessible from any browser) and a monthly summary email. Our support contract covers quarterly retraining cycles and unlimited bug-fix patches for twelve months after go-live.

What changes when you work with us

A concrete before-and-after based on patterns we see repeatedly across client engagements.

Before

  • Decisions rely on monthly Excel reports that arrive two weeks late.
  • Customer-churn signals are spotted only after the customer has already left.
  • Quality inspectors visually check 8 % of production output; the rest ships unchecked.
  • Support staff manually read and route every inbound email, averaging 4 minutes per message.

After

  • A real-time dashboard updates every 15 minutes with predictions for the next 7 days.
  • Churn-risk scores flag at-risk accounts 30 days early, giving your retention team time to act.
  • A vision model inspects 100 % of output at 120 items per minute, catching defects human eyes miss.
  • An NLP classifier routes 83 % of emails automatically; staff handle only the ambiguous remainder.

Questions we hear often

It depends on the task. For structured tabular predictions (sales forecasting, churn scoring), 5,000 labelled rows is often enough for a useful first model. Image classification typically needs at least 500 labelled images per category. During the data audit we will tell you exactly where you stand and whether augmentation techniques can bridge any gaps.

Yes. We deploy on Azure, AWS, or Google Cloud, whichever you already pay for. If you prefer on-premises hosting, we support that too, provided the hardware meets the minimum GPU and memory requirements we specify during the feasibility phase.

We set a minimum performance threshold in the statement of work. If the model cannot reach it after two rounds of iteration, we present three options: collect more or better data, relax the target to a level the data can support, or end the project. You will never be billed for a model that does not work.

Every deployment includes 12 months of monitoring and quarterly retraining. After that period you can renew the support contract annually, bring maintenance in-house (we provide full documentation and training), or hand it to another provider. We do not lock you in.

All data is encrypted in transit (TLS 1.3) and at rest (AES-256). We process personal data only within UK-based data centres unless you request otherwise. Our team holds Cyber Essentials certification, and we sign a data-processing agreement before receiving any files. For projects involving health or financial data, we can work within an isolated environment that your IT team controls entirely.