AI engineering
AI that earns its place in your operations
Sound familiar?
The problems we are usually called in to fix
- Problem 01
Rules that cannot keep up
Static rule sets miss new fraud patterns and bury analysts in false positives that someone has to clear by hand.
- Problem 02
Models that never left the notebook
A promising proof of concept is worth nothing until it runs reliably against live data inside your core systems.
- Problem 03
Black-box decisions
Regulators, auditors and customers want to know why a loan was declined or a payment was held. "The model said so" is not an answer.
- Problem 04
Manual checks at the front door
Identity checks, document reading and data capture still depend on people comparing photos and retyping forms.
What we deliver
What we build and run for you
Machine learning that scores risk, flags fraud, reads documents and recognises faces, deployed as secure APIs your systems can call in real time.
Risk scoring & fraud detection
Gradient-boosted models (CatBoost, XGBoost) that score transactions and applications in milliseconds, tuned to cut false positives.
Credit decisioning APIs
Approval predictions and recommended amounts served through documented FastAPI endpoints your lending workflow can call directly.
Face & ID verification
Deep-learning face matching, liveness checks and OCR on identity documents for remote onboarding and attendance.
NLP & document intelligence
Extract, classify and route the information buried in forms, statements, emails and free-text notes.
Computer vision
Detection models such as YOLOv9 for inspection, counting and diagnosis, from crop disease to equipment condition.
Forecasting
Time-series models for demand, prices, cash flow and capacity, with confidence ranges rather than single guesses.
How it works
A clear path from idea to production
Working software every two weeks, demonstrated to you, with security and documentation built in from the first sprint.
- 01
Data & feasibility audit
We check what data you have, its quality and whether it can support the decision you want to automate, before anyone writes a model.
- 02
Baseline & prototype
A simple, honest baseline first, then a working prototype on your real data, so improvement is measured, not assumed.
- 03
Explainability built in
Feature attribution (for example SHAP) gives every prediction a human-readable reason your team and auditors can review.
- 04
Production deployment
Models are packaged as secure, documented APIs, integrated with your systems and load-tested before go-live.
- 05
Monitor & retrain
We watch for data drift and accuracy decay, and retrain on fresh data so performance holds long after launch.
Use cases by industry
Where AI engineering pays off
Banking & fintech
AML and fraud flagging, credit scoring, face-verified digital onboarding.
Agriculture
Image-based pest and disease detection, yield and market price forecasting.
Retail & e-commerce
Scam and seller-credibility scoring, demand forecasting.
Public sector
Document classification, attendance verification, case triage.
Related work
Proof, not promises
BankingAI fraud & anti-money-laundering detection
Machine learning that scores transactions for fraud and money-laundering risk and flags suspicious activity with actionable intelligence.
- Python
- Django
- FastAPI
BankingAI loan approval & amount recommendation API
Production-ready CatBoost and NLP models that predict loan approvals and recommend suitable amounts in real time.
- Python
- FastAPI
- CatBoost
- Banking
AI face-verified digital onboarding
A QR-launched onboarding platform that verifies customers with deep-learning face matching and ID document recognition, now running in production.
- Python
- Django
- OpenCV
Tools and technologies behind our AI engineering work
- Python
- CatBoost
- XGBoost
- scikit-learn
- PyTorch
- Transformers
- OpenCV
- DeepFace / FaceNet
- YOLOv9
- Prophet
- FastAPI
- Swagger / OpenAPI
FAQ
AI engineering questions, answered
Do we need a lot of clean data before we start?
Not necessarily. The first step is an audit of what you already hold. Many valuable models start with a few years of transaction or application history. Where data is thin, we start with rules plus a simpler model, and improve as data accumulates.
Can you explain why the model made a decision?
Yes. We favour interpretable models and attach feature attributions to each prediction, so a declined application or a flagged payment comes with the factors that drove it.
Can the AI run on our own servers?
Yes. We regularly deploy models on-premise or in your own cloud tenancy, including offline configurations, so sensitive data never has to leave your environment.
What happens when the model gets less accurate over time?
We monitor live performance and data drift, and agree a retraining schedule as part of support, so the model keeps up with how your customers and markets change.
Free assessment
Have a process that’s slow, manual or risky? Let’s engineer a smarter one.
An engineer, not a salesperson, will reply within one business day. The first assessment is free and there is no obligation.