Predictive analytics
Forecast demand, churn risk or equipment failure using your historical records. Models refresh automatically on a schedule you choose, daily or weekly.
Your business runs on data. Artificial Intelligence turns it into action.
We build models that read your spreadsheets, logs and customer records, then hand you clear recommendations you can act on the same week. No jargon-heavy reports that gather dust.
Talk to our team
The problem we keep hearing
Most companies collect far more data than they use. Sales figures sit in one tool, customer support tickets in another, inventory counts in a third. Staff spend hours copying numbers between spreadsheets, and by the time a report reaches a decision-maker, the window for action has already closed.
Hiring a full data-science team is expensive. Freelance consultants deliver a model, then disappear before anyone learns how to maintain it. The result is wasted budget and growing scepticism about whether AI is worth the trouble.
What we do differently
We stay involved after deployment. Every model we build comes with a plain-language handbook, a monitoring dashboard and a 90-day support window. Your existing staff learn to retrain the model when new data arrives, so you stop depending on outside help for routine updates.
Our pricing is project-based with a fixed cap. You approve the scope, we quote a ceiling, and if we finish under budget you keep the difference. That aligns our incentive with yours: ship something useful, fast.
Four steps from raw data to working model
1
Audit your data
We connect to your existing databases, CRMs and file stores. Within five business days you receive a data-quality report that flags gaps, duplicates and formatting issues.
2
Define the question
Together we write a single sentence describing what the model should predict or classify. "Which customers are likely to churn next quarter?" is a good example. Vague goals get sharpened here.
3
Build and validate
Our engineers train candidate models, compare accuracy on held-out data and select the best performer. You see interim results every two weeks in a shared dashboard.
4
Deploy and hand over
The final model runs inside your infrastructure or on a managed cloud instance we set up. We train two members of your team to monitor performance and trigger retraining when accuracy drifts.
What we offer
Each engagement is scoped to a concrete business outcome, not a technology wish-list.
Document processing
Extract structured fields from invoices, contracts or application forms. Our pipeline handles PDFs, scanned images and handwritten notes with accuracy above 94 percent on typical business documents.
Conversational assistants
Customer-facing chatbots trained on your product catalogue and support history. They answer in your brand voice, escalate tricky questions to a human and log every interaction for review.
Anomaly detection
Spot unusual patterns in transactions, network traffic or sensor readings within seconds. Alerts go to Slack, email or any webhook endpoint your ops team already monitors.
Common questions
Most engagements run between six and twelve weeks from kick-off to deployment. A simple predictive model on clean data can ship in four weeks. Document-processing pipelines with custom training data tend to sit closer to the twelve-week mark because annotation takes time.
Not necessarily. We can work with anonymised or synthetic datasets during development. If the model requires real identifiers at inference time, we deploy it inside your own infrastructure so the data never leaves your network. We sign a Data Processing Agreement before any data transfer.
All models drift over time as the underlying data changes. We set up automated monitoring that flags accuracy drops. Your team can trigger a retraining run with one command, using the handbook we provide. If you prefer, we offer a maintenance retainer that covers quarterly health checks and retraining.
Yes. We have delivered integrations with Salesforce, HubSpot, SAP, Shopify and several bespoke ERP systems. Our models expose a REST API, so anything that can make an HTTP request can consume predictions.
We quote a fixed ceiling after the scoping call. Small projects (a single predictive model on structured data) typically fall between £8,000 and £18,000. Larger multi-model programmes with custom infrastructure can reach £50,000 or more. You only pay for delivered milestones.
Let's figure out what your data can do
Describe your situation in a few sentences. We will reply within one business day with an honest assessment of whether AI is the right tool for the problem, and if so, a rough timeline and budget range.
397 Kellie Lea, Castle Rogahning, TZ06 1VK, England, United Kingdom