Better decisions, made the same way every time.
Some of the most valuable decisions a company makes are made thousands of times a year: which overdue account to escalate, which customer to extend credit to, what price to quote. Each is made by capable people under time pressure, and small inconsistencies add up. We build models that weigh everything the business already knows and recommend the decision with the best expected result.
Where the money goes.
Decisions vary by person and by day
Two similar cases receive different treatment, and the cost of the worse decision is rarely visible.
Data exists but is not used at the point of decision
History, customer profiles and outcomes sit in systems no one consults in the moment.
Results are never compared
Without a baseline, there is no way to know whether a different decision would have done better.
What we build.
Credit and collections prioritisation
Deciding which accounts to pursue, how and when, for the best recovery after cost.
Pricing
Price and discount recommendations based on what customers have actually accepted.
Risk scoring
Consistent scoring of customers, suppliers or cases, with the reasons shown.
Search and recommendation
Systems that show customers the products that match what they mean, not just what they type.
Results in money, against business as usual.
Every engagement starts with a risk-free diagnostic on your own data, and the system then runs side by side with your current process so the difference is measured. Read our approach.
- Expected value per decision, after cost
- Recovery rate and legal or collection cost
- Margin and win rate
- Consistency against a historical baseline
Selected work
All case studies →A debt-collection decision model for a global credit insurer
A production model that decides which overdue commercial accounts to pursue, and how, saving an estimated €3M+ a year in legal costs.
AI product search for a fashion e-commerce business
An AI search engine that understands style and intent, built into production. The client acquired the intellectual property.
Does automated follow-up get overdue invoices paid? The evidence
What independent data shows about late payment in the UK and US, what automated invoice follow-up changes, what it does not, and how to measure it.
Guide · ApproachHow to run a risk-free AI pilot that proves ROI before you commit
Most AI pilots never show a return. A six-stage method to de-risk one: a money metric, a backtest, pre-agreed thresholds, a control group and stop rules.
Frequently asked questions
Does the model make the decision?
Usually it recommends and your team decides. Where decisions are routine and well understood, it can act automatically within limits you set.
How do you know the model is better than our people?
We test it on past cases it has never seen and compare its recommendations with what actually happened, before it informs any live decision.
Can we see why the model recommends something?
Yes. Each recommendation shows the main factors behind it, so your team can check and challenge it.
Start with a diagnostic.
Share one report. Within seven days, we show you in money what AI would have delivered on your own past numbers. If it does not deliver, you do not pay.
Book a diagnostic →