Service · Predictive analytics
Predictive analytics that people actually act on
We build forecasting and risk models together with the data platform underneath them — so predictions are accurate, explainable and delivered straight into the decisions they are meant to improve.
01 The challenge
Dashboards describe the past
Most companies have reporting that explains what happened last month. Far fewer can say with confidence what will happen next week — which customers are likely to leave, which machines are likely to fail, how much stock to order or which transactions look wrong.
Prediction projects often stall for reasons that have little to do with algorithms: scattered data, no reliable pipelines, models nobody trusts, and outputs that never reach the people making decisions. We address all of these together.
02 Capabilities
What we predict and build
- 01
Demand and sales forecasting
Forecasts by product, location and channel to guide purchasing, staffing and production.
- 02
Risk and credit scoring
Explainable models for credit, fraud, churn and operational risk with clear reason codes.
- 03
Failure and maintenance prediction
Equipment health models that schedule maintenance before breakdowns.
- 04
Data platforms
Modern data pipelines, warehouses or lakehouses that unify sources into trustworthy, documented datasets.
- 05
MLOps
Versioning, automated retraining, drift detection and monitoring so models stay accurate in production.
- 06
Decision dashboards
Predictions delivered as alerts, recommendations and simple views inside the tools your teams use.
03 How we deliver
From first conversation to systems in production
- 01
Discover
We audit your data sources and pick a prediction with a clear decision attached and a measurable benefit.
- 02
Prototype
A baseline model back-tested on your history, compared honestly with your current method.
- 03
Deploy
Production pipelines, scheduled scoring and integration into planning, CRM or operations systems.
- 04
Evolve
Monitoring for drift and accuracy, retraining, and new predictions built on the same platform.
Every engagement can run in the cloud, on-premise or fully air-gapped. Your data stays under your control, and humans stay in command of every significant decision.
04 Outcomes
What you can expect
- Earlier, better-informed decisions on stock, staffing, risk and maintenance
- One trusted data foundation instead of competing spreadsheets
- Explainable predictions your teams and regulators can understand
- Models that are monitored and maintained, not abandoned after launch
→ Two ways to work with us
Enhance what you have — or let us build and run it
Path A
Enhance what you have
Add AI and automation to the systems, products and operations you already run — no rip-and-replace, and your team stays in control.
Start with an enhancementPath B
Build & manage for you
We design, build and launch it, then host, monitor, secure and improve it — or hand it over to your team.
Have us build it05 Related
Industries we bring this to
- Finance & BankingKYC and AML, credit and risk scoring, fraud detection, agent automation, tokenisation and security.AI for Finance & Banking →
- EnergyPlant and grid automation, renewable and load forecasting, predictive maintenance and smart metering.AI for Energy →
- HealthcareClinical documentation, imaging triage, patient-flow forecasting and private LLMs for health data.AI for Healthcare →
- SMEsAI agents for sales and support, bookkeeping automation, demand forecasting and AI-first software for growing businesses.AI for SMEs →
- Energy AutomationPlant and grid automation, SCADA and PLC integration, solar, wind and load forecasting, smart metering and predictive maintenance.Related service →
- Factory & Industrial AutomationVision quality inspection, predictive maintenance, robotics and PLC line automation, digital twins and supply-chain automation.Related service →
- AI Agents & Workflow AutomationAgents that take over business processes end to end — back office to operations — and escalate to people when it matters.Related service →
06 FAQ
Predictive Analytics & Data Platforms: frequently asked questions
Our data is messy. Can we still start?
Yes — almost everyone's is. Part of every engagement is cleaning, joining and documenting the data so the first model and everything after it stand on solid ground.
How do you prove a model is better than what we do today?
We back-test on your historical data and compare against your existing method or a simple baseline, using metrics agreed with you in advance.
Can predictions be explained?
Yes. We favour interpretable models where possible and add explanation tools so each prediction comes with its main drivers.
Which tools and clouds do you work with?
We work with major clouds and open-source stacks, and can deploy entirely on-premise.
Who maintains the models after launch?
We can run them for you under a support agreement or hand over to your team with documentation and training.
What would you like to know in advance?
Tell us what you want AI to take over. A senior engineer will get back to you — under NDA if you need it.
Start a project