From first conversation to production model
We've refined this process over dozens of engagements. It's designed to reduce risk at every stage: you never commit to the full project cost upfront, and you always have a working artefact to evaluate before the next phase begins.
Discovery workshop
We spend a half-day (in person or remote) with your domain experts and decision-makers. The goal is to understand the business problem, map the data sources available, and identify the metric that matters most. By the end of the session we produce a one-page problem statement and a rough feasibility assessment. This workshop is free for projects we decide to take on.
Typical output: a two-page brief covering the target variable, the data inventory, known constraints (latency, compliance, budget) and a recommended approach. We share this within three working days.
Data audit and preparation
Good models need good data. We connect to your databases, warehouses or flat files, profile the contents, and flag quality issues: missing values, class imbalance, inconsistent labels, duplicated records. We then build an automated cleaning and feature-engineering pipeline so the work is reproducible.
This phase usually takes one to two weeks. At the end you receive a data quality report with specific recommendations. If the data isn't ready for modelling, we'll tell you what needs to change before we proceed.
Proof of concept
We train candidate models, compare their performance on a held-out validation set, and select the architecture that best balances accuracy, speed and interpretability for your use case. The proof of concept runs on a sample of your data and produces real predictions you can inspect.
Duration: two to four weeks. Deliverable: a Jupyter notebook or dashboard showing model performance, example predictions and an error analysis. This is the decision point where most clients choose to move forward, because they can see concrete results.
Production engineering
The proof-of-concept code is rewritten for reliability and speed. We containerise the model, build REST or gRPC endpoints, write integration tests, and set up logging. If the model needs to run at the edge (on a camera, a sensor or a local server), we optimise it for that hardware.
We work in two-week sprints during this phase. Each sprint ends with a demo and a review session where you can reprioritise features. Most production builds take four to eight weeks.
Deployment and integration
We deploy the model into your environment: cloud, on-premise or hybrid. We connect it to the systems that consume its predictions, whether that's a CRM, an ERP, a mobile app or a simple email alert. We run a parallel period where the model's output is logged but not acted on, so you can verify it against human decisions.
This phase includes load testing, security review and documentation for your ops team. We don't consider a project deployed until the monitoring dashboard is live and someone on your side knows how to read it.
Monitoring and iteration
After launch, we track prediction accuracy, data drift and system health. When performance drops below the agreed threshold, we retrain the model on fresh data. We also review edge cases flagged by your team and adjust the model accordingly.
Clients can choose a monthly maintenance plan or handle monitoring in-house using the tools and runbooks we provide. Either way, we're available for quarterly review calls to discuss model performance and potential improvements.
What a real project looks like
Demand forecasting for a food distributor
A Cardiff-based distributor of chilled goods was over-ordering perishable stock by an average of 12 %, leading to waste costs of roughly £9,000 per month. We built a gradient-boosted regression model trained on two years of order history, weather data and promotional calendars. The model predicts next-week demand per SKU per depot.
After eight weeks of development and a two-week parallel run, the system went live. Waste dropped to 4.5 % within the first quarter. The model retrains weekly on the latest sales data, and the distributor's operations manager reviews the forecast each Monday morning in a simple web dashboard we built alongside the model.
Document extraction for an insurance broker
A London insurance broker processed around 400 policy documents a week, each requiring manual data entry into their underwriting system. We trained an NLP extraction pipeline that reads scanned PDFs, identifies key fields (policyholder name, coverage limits, exclusions, renewal dates) and populates the system automatically.
Accuracy reached 94 % on first pass, with a human reviewer checking flagged low-confidence extractions. Processing time per document fell from seven minutes to under 40 seconds. The broker reassigned two full-time staff from data entry to client-facing work.
Ready to start?
Book a free discovery workshop and find out whether AI can solve the problem you're facing. No commitment, no jargon, just a clear assessment.
Get in touchContact details
Prefer to call or email directly? Here's how to reach us.
Address: 82 High Road, New Kohlerfield, Wales, FI9 6EF, United Kingdom
Phone: +44 338 117 0180
Email: [email protected]