AI that works in production — not just in a notebook
We build, deploy, and operationalise ML and AI models on the governed data foundations they need to stay reliable. From fraud detection to demand forecasting — end to end, in production.
Industry relevance
Banking
Critical
(fraud, risk)
Telco
Critical
(churn, NBO)
Retail
High
(demand, CX)
IT/Tech
Medium
The IDS approach — data first, model second
We start with the data
Every AI project starts with data readiness. We assess and build the governed data foundation your models need — clean, consistent, traceable inputs that stay reliable over time.
- Data quality and governance assessment
- Feature engineering on governed data
- Data pipeline design for ML workloads
- Connects to Domain 01 & Domain 02
We end with the model in production
We don’t stop at model development. We build the MLOps infrastructure, monitoring, and retraining pipelines that keep your models working — month after month.
- Model development, validation and deployment
- MLOps: monitoring, drift detection, retraining
- Business integration and user adoption
Services in this domain
ML/AI Model Development
End-to-end ML/AI lifecycle: data prep, feature engineering, model development, validation and deployment.
Predictive Analytics & Use Case Delivery
Churn, fraud, propensity, NBO, segmentation, credit risk, network optimisation — scoped, built and delivered.
GenAI Enablement & Integration
Integrating generative AI into data workflows and applications — built on governed, trusted data foundations.
MLOps & Model Operationalisation
Model monitoring, retraining pipelines, drift detection and production-grade deployment infrastructure.
Data Science Team Building
Building data science functions from scratch: hiring frameworks, tooling setup, methodology and knowledge transfer.
Advanced Analytics & Insights
Statistical analysis, forecasting, anomaly detection and pattern recognition applied to real business problems.
Why most AI projects fail to reach production
Models trained on ungoverned data produce unreliable outputs
Garbage in, garbage out. Without a trusted data foundation, even sophisticated models can't be trusted in production.
Pilots never make it past the notebook
Promising proof-of-concepts stall because there's no MLOps infrastructure, no monitoring, and no path to production deployment.
Data science teams are isolated from the business problem
Models that don't reflect real business logic — churn definitions, fraud rules, pricing constraints — don't get adopted even when they're accurate.
Technologies we use
GenAI is only as good as the data beneath it
Every GenAI project we've seen fail has the same root cause: ungoverned, inconsistent data feeding unreliable outputs. We help you build the foundation first — then integrate GenAI where it genuinely adds value.
- Assess your data readiness for GenAI
- Build governed data foundations
- Integrate GenAI into workflows safely
- Monitor outputs and iterate responsibly
Use cases by industry
The AI problems we solve most often — across the sectors we know best.
Banking
-
Fraud detection & prevention
-
Credit risk scoring
-
Customer churn prediction
-
AML transaction monitoring
-
Next Best Offer
Telco
-
Churn prediction models
-
Next Best Offer / NBO
-
Network anomaly detection
-
Customer segmentation
-
Propensity scoring
Retail
-
Demand forecasting
-
Customer lifetime value
-
Dynamic pricing models
-
Basket analysis & NBO
-
Inventory optimisation
Insurance
-
Claims fraud detection
-
Risk scoring models
-
Churn prediction
-
Customer segmentation
-
Underwriting automation
Case studies
Tell us your use case
The AI problems we solve most often — across the sectors we know best.
We'll tell you what data foundation it needs, what the model architecture looks like, and how long it takes to get from where you are to production — based on what we've already delivered.