Data Science & Machine Learning Solutions
From predictive insights to intelligent automation
Transform your business with custom machine learning models and AI solutions that actually reach production. We help you move beyond experimental models to build, deploy, and manage scalable, high-impact AI systems that drive measurable commercial value.
The problem:
Your models are stuck in the lab
Many businesses invest heavily in data science but fail to see a return. Promising models developed in notebooks often never make it into production, a phenomenon known as the "model graveyard."
High Failure Rate
You're struggling to deploy models, with high decline rates on new applicants or rising defaults despite conservative strategies.
Slow & Inconsistent Decisions
Manual overrides are common because the business doesn't trust the models, leading to slow, inconsistent, and biased decisions.
Fear of the Unknown (AI)
There's immense pressure to adopt Generative AI, but fears of hallucinations, data leakage, and a lack of clear ROI are causing innovation paralysis.
Skills Gap
Your team is skilled at building models but lacks the MLOps and software engineering expertise to deploy, monitor, and maintain them in production.
Our Solution
Production-grade AI, governed and scaled
We build and deploy complete, end-to-end data science solutions — from feature engineering to production monitoring — that are designed for impact, governance, and long-term value.
What we build for you
Custom ML Models
We develop a range of models, from interpretable scorecards (Logistic Regression) for regulatory compliance to high-performance models (XGBoost, Neural Networks) for competitive advantage.
Generative AI & LLM Applications:
We implement secure, enterprise-grade GenAI solutions, including RAG systems for accurate responses, AI agents for intelligent automation, and robust safety frameworks to prevent hallucinations and ensure data privacy.
End-to-End MLOps
We build the infrastructure for success, including feature stores, real-time scoring APIs, model registries with robust versioning, and A/B testing frameworks.
Comprehensive AI Governance
We ensure your AI is transparent and trustworthy by delivering detailed model documentation for regulators, fairness and bias testing, and real-time performance monitoring dashboards.
Unlock New Revenue: Approve 10-15% more good customers at the same risk level.
Increase Efficiency: Achieve up to 75% automation of complex analytical tasks.
Reduce Risk & Cost: See a 20% reduction in default rates and a 50% decrease in manual overrides.
Accelerate Decisions: Reduce decision times from days to minutes, or even seconds.
Production-ready ML models and GenAI applications with scoring/inference APIs.
A complete feature engineering pipeline and scalable feature store.
Real-time monitoring dashboards for performance, drift, and data quality.
Comprehensive documentation for regulatory compliance and internal teams.
The ability to serve new customer segments that competitors decline.
A consistent, explainable, and auditable decisioning framework.
A continuous improvement engine that keeps your models performing optimally.
What you get: Measurable impact from your AI investment
How we deliver production-ready AI
Our agile, phased approach ensures we prove value quickly and build solutions that are ready for the complexities of a live production environment.
Discovery and data foundation
(Weeks 1-2)
We start by analysing your current decisioning processes, identifying high-impact opportunities, and defining clear success metrics. We then build the foundational data and feature pipelines required for robust modelling. Deliverable: Use case definition with success metrics and feature engineering pipeline.
Model development and validation (Weeks 3-6)
We train and rigorously validate multiple algorithms, integrating business rules and ensuring all regulatory and fairness requirements are met. For GenAI, this includes implementing strict safety and testing protocols. Deliverable: Validated models with explainability framework and regulatory documentation.
Production deployment and integration (Weeks 7-8)
We deploy the validated model as a scalable API, integrate it with your existing systems, and establish a comprehensive monitoring framework. This is where MLOps best practices become critical. Deliverable: Production-grade ML system with monitoring, alerting, and rollback capabilities.
Optimisation and continuous learning (Ongoing)
Once live, we continuously monitor for performance degradation and concept drift, using A/B testing and challenger frameworks to ensure the solution delivers ongoing value. Deliverable: Optimised models with continuous improvement and knowledge transfer.