What our clients say first
We asked Stellar AI Journey to reduce manual invoice processing. Within six weeks they had a working classifier that cut our back-office hours by roughly 40%. The model runs on our own servers — no ongoing cloud bill surprises. — Finance director, mid-size logistics company, Leeds
Their team spent two days just listening before they proposed anything. The recommendation engine they built for our e-commerce catalogue increased average basket value by £8.50 per order in the first quarter. — Head of digital, Yorkshire retail brand
How we bring Artificial Intelligence to your organisation
Most AI projects fail because someone skips straight to the model. We follow a different sequence. Below is the actual path a typical engagement takes, from the first conversation to a system your team uses every day.
Listening session
We sit with the people who do the work. Not the board deck — the actual workflow. A warehouse picker, a claims handler, a customer-service agent. We record where time drains away and where decisions get stuck. This session is free and takes half a day.
Data audit
Your data is probably messier than you think. We map every source — spreadsheets, CRM exports, sensor logs, email threads — and score each one for completeness, freshness and bias. The output is a short, honest report: what is usable today, what needs cleaning, and what is missing entirely.
Feasibility model
Before committing budget to a full build, we train a lightweight prototype on a sample of your data. If the numbers look promising, we continue. If they don't, we tell you. Two of our last nine feasibility studies ended with a recommendation not to proceed — and that saved those clients tens of thousands of pounds.
Build and iterate
Our engineers develop the production model in fortnightly sprints. Each sprint ends with a demo your team can test. We use open-source frameworks wherever possible so you are never locked into a proprietary stack. Typical models include document classifiers, demand forecasters, anomaly detectors and recommendation engines.
Integration and handover
The model connects to your existing systems through APIs or batch pipelines. We write the documentation, train your team, and run the system in shadow mode alongside your current process for at least a week before switching over. You own the code and the model weights outright.
Monitoring retainer
Models drift as the world changes. We offer a lightweight monthly retainer that includes performance monitoring, retraining triggers and quarterly review calls. About half our clients take this up; the rest manage monitoring in-house with the dashboards we leave behind.
Capability map
We don't sell a single product. We match the right technique to your problem. Here is what we actually build.
| Technique | Typical use | Data you need | Timeline |
|---|---|---|---|
| Document classification | Sorting invoices, contracts, support tickets | A few thousand labelled examples | 4–6 weeks |
| Demand forecasting | Stock planning, staffing rotas, capacity | 18+ months of transaction history | 5–7 weeks |
| Anomaly detection | Fraud alerts, equipment failure, quality control | Sensor or transaction logs with timestamps | 3–5 weeks |
| Recommendation engines | Product suggestions, content personalisation | User interaction data (clicks, purchases) | 6–8 weeks |
| Natural language processing | Chatbots, sentiment analysis, summarisation | Text corpus relevant to your domain | 5–9 weeks |
| Computer vision | Defect inspection, document digitisation | Annotated images (we can help annotate) | 6–10 weeks |
A note on hype
Large language models get all the headlines. They are genuinely useful for some tasks — summarising long documents, drafting first-pass copy, answering internal knowledge-base questions. But they are expensive to run, hard to control, and sometimes confidently wrong.
For many business problems, a well-tuned classical model outperforms a general-purpose LLM at a fraction of the cost. We will always recommend the simplest approach that solves your problem. If a rules engine or a spreadsheet formula is enough, we will say so.
Who we work with
Mostly mid-size companies — 50 to 500 employees — in logistics, retail, financial services and manufacturing across the UK. We have also supported two NHS trusts with patient-flow prediction, though healthcare projects take longer because of information governance requirements.
Who we are
Seven people. Four machine-learning engineers, one data engineer, one project lead and one designer who makes dashboards people actually want to open. We are based in Leeds but work remotely with clients across England and Scotland. No offshore subcontracting.
Fit check — is AI right for your problem?
Answer these honestly before you call anyone.
Do you have data?
Is the decision repeatable?
Can you tolerate occasional errors?
Do you have someone internally who will own the system?
Is your leadership patient?
A recent outcome
A regional food distributor asked us to predict next-day order volumes for 1,200 product lines. Their existing method was a spreadsheet maintained by one person who had been doing it for eleven years. When that person went on leave, forecasts collapsed.
We trained a gradient-boosted model on three years of order history, weather data and promotional calendars. Forecast accuracy went from 68% to 89% measured by weighted MAPE. The spreadsheet still exists — as a fallback — but it hasn't been opened in four months.
Pricing principles
We charge for time, not for magic. Typical engagements fall into two bands:
Discovery and feasibility
£3,000 – £6,000 depending on complexity. Covers the listening session, data audit and feasibility model. You get a written report and a working prototype you can test. If you decide not to continue, you keep everything.
Full build and deployment
£12,000 – £35,000 depending on model type, data volume and integration complexity. Includes all engineering, documentation, training and two weeks of post-launch support. The monitoring retainer, if you want it, runs £800 per month.
We do not charge for the initial listening session. We also do not charge if our feasibility study concludes that AI is not the right approach for your situation.
Start a conversation
Fill in the short form below or contact us directly.
[email protected]
+44 113 239 1920
39 Davis Corner, Leeds LS1 4AP, West Yorkshire, United Kingdom