Service / AI
Forecasts your team can act on.
Forecasting, anomaly detection, churn, fraud and pricing models on your structured data, deployed into the systems where decisions are made.
[ 01 ]The short answer
What is Predictive ML and Forecasting?
Predictive machine learning uses your historical data to estimate what happens next: demand, churn, fraud, failure or price. It is often the highest-return AI a business can deploy because it acts on structured data you already collect. We build gradient-boosted and deep learning models with honest backtests, explain the drivers behind each prediction, and deploy them into the tools where people decide, with monitoring for when the world changes.
What you get
- Model with backtest report
- Feature pipeline
- Real-time or batch scoring service
- Explainability views
- Drift monitoring and retraining job
[ 02 ]What we build
The pieces, built to last.
- 01
Demand forecasting
Hierarchical forecasts by product, location and day, with uncertainty ranges.
- 02
Anomaly detection
Unusual transactions, readings or shipments flagged as they happen.
- 03
Churn and propensity
Which customers are likely to leave or buy, and what drives it.
- 04
Fraud models
Real-time scoring with features engineered from behaviour and networks.
- 05
Explainability
Feature attributions per prediction, so a person can see why.
- 06
Monitoring and retraining
Drift detection and scheduled retraining against fresh data.
[ 03 ]Where it pays off
Where it earns its keep.
- 01
Supply chain
Stock and capacity planned against forecast demand, with disruptions spotted early.
- 02
Subscription businesses
Retention offers sent to the right customers before they cancel.
- 03
Payments and lending
Fraud stopped in milliseconds without blocking good customers.
- 04
Investing and research
Signals ranked by their predictive power, with honest backtests.
[ 04 ]How we deliver
Four steps, one team, start to finish.
The same people scope it, build it and stay after launch. Timings are typical for a first release; we agree yours after the deep dive.
- 01Week 1
Find the decision
We pick the decision or task worth automating, measure how it is done today and agree what better means in numbers.
- 02Week 1 to 2
Build the eval first
A labelled test set from your own data comes before any prompt or training run, so every change is scored, not argued.
- 03Weeks 2 to 10
Build with guardrails
Models suggest, code decides. Permissions, injection checks, human override and audit logs are part of the first version.
- 04Ongoing
Ship and improve
We launch to real users, watch cost, latency and quality per request, and feed failures back into the eval set.
[ 05 ]Stack
Tools we reach for.
Chosen per project, and we work in yours. Nothing here is a lock-in: you own the code and the accounts.
- LightGBM
- XGBoost
- PyTorch
- Prophet
- Darts
- SHAP
- dbt
- Snowflake
- BigQuery
- FastAPI
- Streamlit
[ 07 ]Further reading
Problems we found, and how we would solve them.
- Cost3 min read
Your AI bill is a routing problem
ProblemAI applications send every request to a frontier model with a long prompt, so cost and latency grow with usage while most requests never needed that capability.ProposalRoute each request to the cheapest model that passes your evaluation for that kind of request, cache shared prompt prefixes, batch what can wait, and distil a small model for the highest-volume task.Read the proposal - Models3 min read
Fine-tune, retrieve or prompt? A decision guide
ProblemTeams pick fine-tuning, RAG or custom training by reputation rather than by what the problem needs, and spend months on the wrong one.ProposalDecide by what must change: prompts for instructions, retrieval for knowledge that changes or must be cited, fine-tuning for behaviour, format and cost, and custom models only where the task or data is truly unusual; always prove it on an evaluation set.Read the proposal
[ 08 ]FAQ
Questions buyers ask.
For seasonal forecasts, ideally two or more years. For fraud or churn, enough labelled outcomes in each class. We assess the data in the first week before promising anything.
Language models are poor at numeric forecasting on tabular data. Gradient-boosted trees and time-series models remain the right tools, and an LLM can explain their output in words.
We backtest both on the same historical periods and run the model in shadow mode before it drives any decision.
[ 09 ]More AI services
- Custom AI Model DevelopmentA model trained on your data, owned by you.
- LLM Fine-Tuning and DistillationFrontier quality at a fraction of the cost.
- AI Agent DevelopmentAgents that finish the job, inside the rules.
- AI Workflow AutomationHours of back-office work, done in minutes.
- RAG DevelopmentAnswers from your documents, with citations.
- AI Memory and Knowledge GraphsAI that remembers what your organisation learned.
- MCP Server DevelopmentYour product, usable by every AI assistant.
- Voice AI AgentsEvery call answered, qualified and logged.
- Document AI and Intelligent Document ProcessingPaperwork turned into clean, checked data.
- Private LLM DeploymentAI on your infrastructure, data never leaves.
- LLMOps, Evaluation and GuardrailsKnow your AI works before customers find out.
One company at a time