Forecast
Demand, next 12 weeks
your history · not a benchmarkMAPE 5.8%
Machine learning integration & consulting
The data you already have, turned into the next decision.
Machine learning integration & consulting: know what’s likely before it happens, and act on it. Forecasting what’s coming, scoring what matters, flagging what looks wrong — trained on your own data and built around the call you want sharper, rather than buried in a report.
forecast ›
On your history
5.8%
MAPE on the 12-week demand forecast, from your own history.
01Where it earns its keep
Patterns in your data, turned into decisions.
At its best where there’s history to learn from and a decision you repeat — the shapes below are starting points, not the limits.
01
Demand & inventory forecasting
Past sales, seasonality and lead times become projected demand and what to reorder.
02
Classification & routing
Incoming work sorted into the right category and sent the right way — tickets, documents, transactions, enquiries.
03
Scoring & prioritisation
Leads, risks or jobs ranked by how likely they are to convert, fail or matter — so attention goes where it counts.
04
Anomaly detection
The unusual transaction, the failing machine, the figure that’s off — surfaced the moment it appears.
05
Recommendation
The next best action, product or step, suggested from what has worked before.
06
Churn & risk prediction
Early signals that a customer is drifting or an account is at risk — in time to do something about it.
02How it fits
Machine learning and LLMs — better together.
Machine learning predicts; a large language model explains.
Trained on your records, a model finds patterns in numbers and history to forecast, classify and score. The engine behind the call.
An LLM turns a raw prediction into a plain-English report, a drafted email or an answer — usable, not just a number.
The two combine in one system that proposes the next step and waits for your approval on anything consequential.
03How we build it
Machine learning integration & consulting: from your history to a model that stays sharp.
- Step 01
We pin down the decision
The specific, repeated call you want sharper — what to reorder, which lead to chase, what looks wrong. A clear target beats a vague model.
- Step 02
We train on your data
We use the history you already keep — spreadsheets, point-of-sale, CRM — to train and validate a model on your reality, not a generic benchmark.
- Step 03
We build it into your workflow
The model goes inside a single-purpose system on infrastructure we manage, with a person approving consequential calls at a clear gate.
- Step 04
We monitor and retrain
We track accuracy and retrain on fresh data as your business changes, so the model stays in tune instead of quietly drifting out of date.
04FAQ
What is machine learning integration?
Machine learning integration is the work of training a model on your own historical data to spot patterns and make predictions — such as forecasting demand, scoring leads or flagging anomalies — and building that model into an agent that runs in your workflow, with a person approving consequential decisions.
Prediction is one of three ways in under artificial intelligence. The investigation that comes first is data science. It lands best where decisions are made — inside the data and analytics dashboards your team already reads, or handed to agentic AI to act on automatically. New to the idea? Read the plain-English guide to machine learning.
How is machine learning different from an LLM?
A large language model is built for language — reading and writing text. Machine learning models find patterns in your numbers and records to predict and classify. They are complementary: an LLM can explain and write up what a machine learning model predicts, and the two are often combined in one agent.
Do I need a huge amount of data to use machine learning?
Less than people expect. Many useful forecasts and classifiers work from the history you already keep in spreadsheets, your point-of-sale system or your CRM. In the scoping call we will be straight about whether your data is a strong fit before anything is built.
What can machine learning predict for my business?
Common examples include demand and inventory forecasting, classifying or routing incoming work, scoring and prioritising leads or risks, detecting anomalies such as fraud or failures, and recommending the next best action — examples of the shape, not the limit. Each model is built around a specific decision you make repeatedly.
Does the model keep improving over time?
Yes. As part of keeping it running, we monitor accuracy and retrain on fresh data so the model stays in tune as your business changes — rather than quietly drifting out of date.
Where does the model run, and is my data safe?
It runs on infrastructure we manage, behind a secure login. For sensitive work an open model runs on our own infrastructure, so your data is never sent to a third-party model provider.
Start here
Tell us the decision you’d like to get sharper.
Send a few details and we’ll set up a short call to scope it and give you a tailored quote. No obligation, no hard sell.