Solutions — AI & Advanced Analytics

AI That Works in
Production. Not Just
in Presentations.

CALIGO builds machine learning models, predictive systems, and intelligent decisioning pipelines that are deployed, monitored, and maintained in live environments — generating measurable business outcomes across banking, insurance, and telecoms.

Machine Learning Predictive Analytics AI Use Case Development MLOps & Model Lifecycle Prescriptive Analytics Intelligent Decision Systems
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The Problem

Most AI Initiatives Produce
Pilots. Not Outcomes.

Every major bank, insurer, and telecom operator has launched AI initiatives. Most have produced proof-of-concepts, vendor demos, and strategy decks. Far fewer have produced models running in production, generating real value, maintained by a team that understands them.

The failure modes are consistent: AI projects that aren't connected to the right data. Models that can't be deployed into existing systems. Teams that can build a notebook but can't operate a model at scale. And business cases that measure accuracy instead of revenue impact.

CALIGO approaches AI differently. We start with the business problem, validate that the data exists to solve it, and engineer the model as a production system — not a research artefact. Every engagement ends with something running in production, not a recommendation to "explore further".

Pilots That Never Reach Production
Models are built in data science notebooks, validated in isolation, and never deployed — because no one designed for the integration, latency, and operational requirements from the start.
Models That Degrade Without Anyone Noticing
A model trained on last year's data, running in production without monitoring, gives increasingly wrong predictions. No one knows until the business impact surfaces — by which point it is too late.
AI on Data That Isn't Ready
Sophisticated models are built on top of unreliable, inconsistent data — producing confident predictions that are systematically wrong, without anyone realising the foundation is flawed.
Use Cases Selected for Interest, Not Impact
AI roadmaps are built around technically interesting problems rather than commercial value. Teams build impressive models for problems that don't move the needle.
No Path from Insight to Decision
A model produces a score. The score sits in a database. No one integrated it into the CRM, the campaign engine, or the underwriting workflow where it would actually change a decision.
Our Approach

Business Problem First.
Production System Always.

We apply a disciplined use case selection process before writing a single line of model code — validating that the data exists, the outcome is measurable, and the deployment path is clear. Every engagement is engineered for production from day one.

01
Use Case Discovery & Validation
We work with business and analytics leaders to identify, score, and prioritise AI use cases by commercial impact, data readiness, and delivery feasibility. Output: a ranked use case backlog with clear go/no-go criteria — not every idea makes it through.
Use Case Scoring MatrixData Readiness Assessment2–3 Weeks
02
Data & Feature Engineering
We build the feature store and training datasets that the model actually needs — sourced from your data platform, enriched with derived attributes, and version-controlled so model training is reproducible and auditable.
Feature Store DesignTraining Data PipelineData Versioning
03
Model Development & Validation
We develop, train, and validate models using rigorous experimental design — cross-validation, holdout testing, champion/challenger frameworks, and business KPI validation alongside statistical metrics. We optimise for business impact, not just accuracy scores.
Champion / ChallengerBusiness KPI ValidationModel Explainability
04
Production Deployment
We engineer the model as a production system — API endpoints, batch scoring pipelines, integration with CRM or decisioning platforms, and CI/CD for model updates. The model goes into the system that makes decisions, not a dashboard nobody checks.
Model Serving APIBatch / Real-Time ScoringSystem Integration
05
MLOps & Model Monitoring
We implement monitoring for data drift, model drift, and business KPI degradation — with automated alerts and retraining triggers. Every model in production has a health dashboard and an owner responsible for its performance.
Drift DetectionPerformance DashboardsRetraining Pipelines
06
Business Impact Measurement
We close the loop — measuring the actual commercial impact of each deployed model against the business case. Uplift testing, holdout group analysis, and attribution methodology built in from the start so the value is demonstrable, not assumed.
Uplift TestingHoldout Group AnalysisROI Measurement
Production ML System Architecture
Data & Feature Layer
Data Platform
Feature Store
Training Datasets
Real-Time Signals
↓ Training · Validation · Experimentation ↓
Model Registry & Serving
Model Registry
Batch Scoring
Real-Time API
A/B & Champion/Challenger
Drift Monitoring
↓ Scores & Decisions Delivered to Business Systems ↓
Decision & Action Layer
CRM & Campaign Engine
Underwriting Workflow
Fraud Decision Engine
Digital Channels
What We Deliver

Six AI Capabilities.
All Engineered for Production.

Each capability is a production system, not a prototype. Deployed, monitored, and maintained — generating business value from day one in live environments.

Predictive Analytics & Scoring Models
Production-grade predictive models for churn, credit risk, default probability, renewal likelihood, and demand forecasting — trained on your data, validated against your commercial outcomes, deployed to your systems.
Churn & retention propensity
Credit default & PD / LGD modelling
Demand & revenue forecasting
Renewal & conversion scoring
Fraud Detection & Financial Crime Models
Real-time and near-real-time fraud detection models for transaction fraud, insurance claims fraud, and AML typology detection — designed for high-volume, low-latency environments where speed and precision both matter.
Transaction fraud detection (real-time)
Insurance claims anomaly detection
AML network & typology models
False positive reduction framework
Next Best Action & Prescriptive Analytics
Prescriptive models that don't just predict what will happen — they recommend what to do about it. Offer selection, channel optimisation, pricing decisions, and intervention timing — personalised at scale and integrated with decisioning systems.
Offer eligibility & propensity ranking
Channel & timing optimisation
Dynamic pricing models
Real-time decision API
NLP & Unstructured Data Analytics
Machine learning applied to unstructured data — call centre transcripts, customer complaints, contract text, claims descriptions — extracting signals, classifying intent, and automating review processes that currently require human reading.
Complaint & sentiment classification
Document & contract entity extraction
Call centre transcript analytics
Claims narrative processing
MLOps & Model Lifecycle Management
The engineering infrastructure that keeps AI in production — model registries, automated retraining pipelines, drift detection, performance monitoring, and CI/CD for model updates. Production AI without MLOps is a liability, not an asset.
Model registry & versioning
Data & concept drift monitoring
Automated retraining triggers
CI/CD for model deployment
AI Use Case Development & Roadmap
For organisations earlier in their AI journey, we run structured use case discovery — identifying, sizing, and prioritising the AI opportunities with the highest value and the shortest path to production. Output: an AI roadmap grounded in your data reality.
AI opportunity mapping
Use case scoring & prioritisation
Data readiness assessment per use case
Build vs buy vs partner framework
Use Cases

AI in Production.
Business Impact Measured.

These are real AI systems running in live environments — not proof-of-concepts. Every outcome was measured against a control group or baseline before being reported.

Banking · Fraud
Real-Time Transaction Fraud Detection — 34% Improvement
A bank's batch fraud detection was catching fraud 6 hours too late. CALIGO built a real-time ML scoring system ingesting transaction events — enriched with customer behavioural context — serving decisions in under 100ms at full transaction volume.
Fraud detection rate +34% vs. batch baseline; false positives −28%
Telecommunications · Retention
Churn Prediction Model — 23% Churn Reduction
A telecom operator's churn model was built on demographic data and run monthly. CALIGO rebuilt it on real-time behavioural signals, deployed to the CRM as a daily score, and integrated with retention campaign targeting — changing which customers were contacted and with what offer.
23% reduction in subscriber churn across 18M base
Banking · Credit Risk
Alternative Credit Scoring — 18% More Approvals, Same Risk
A bank using only traditional bureau scores was declining creditworthy customers with thin files. CALIGO built an alternative credit scoring model using transactional behaviour and cash flow patterns — expanding approvals without increasing default rates.
18% increase in approvals with no change in realised default rate
Insurance · Claims
Claims Fraud Anomaly Detection — €2.1M Recovered
An insurer's claims fraud team was manually reviewing 0.3% of submissions. CALIGO built an anomaly detection model scoring all claims at submission — prioritising investigation queues and flagging claim networks human reviewers had missed.
€2.1M additional fraud recovered in first year of deployment
Banking · Commercial
Next Best Product Engine — Cross-sell Revenue +18%
A retail bank was applying blanket cross-sell campaigns across its entire customer base. CALIGO built propensity models for 12 product categories, integrated with the CRM, and deployed personalised recommendations to 2.3M customers.
Cross-sell revenue increased 18% in first 6 months
Telecom · Operations
Network Anomaly Detection — MTTR Reduced by 41%
A telecom operator was responding to network incidents reactively. CALIGO built a time-series anomaly detection system on network performance data — predicting degradation events 2–4 hours before service impact and routing them to the right engineering team automatically.
Mean time to resolve reduced 41%; proactive interventions up 3×
Related Accelerators

Data Foundations That
Accelerate AI Delivery

AI is only as good as the data beneath it. CALIGO's pre-built industry data models provide the feature-rich foundation that machine learning needs — reducing data engineering time by up to 60% on every AI engagement.

Banking
Banking Data Warehouse Model
Transactional, behavioural, and risk attributes pre-modelled at customer, account, and product level — providing a rich feature layer for credit scoring, churn, fraud, and next best action models without custom data engineering per project.
Learn more
Telecom
Telecom Data Model
Network, usage, billing, and subscriber behaviour data modelled as a unified analytical layer — pre-built for churn, CLV, network anomaly, and revenue assurance ML use cases in any MNO environment.
Learn more
Insurance
Insurance Data Warehouse Model
Policy, claims, and customer history modelled for ML — providing the training data foundation for claims fraud detection, renewal churn, pricing optimisation, and customer risk scoring models.
Learn more
Case Studies

AI Systems Running
in Production.

All Case Studies →
BankingAI / MLModel Validation
AI Model Validation Framework — Standardised, Scalable, End-to-End
A leading private bank lacked a standardised AI model validation framework. CALIGO designed an end-to-end validation workflow covering requirement definition, multi-source data collection, testing, and automated reporting — strengthening NPL risk management and model governance.
100%
Automated Reporting
↓ NPL
Risk Reduced
E2E
Validation Workflow
Read Case Study
BankingCustomer Analytics
Alternative Credit Scoring & Financial Inclusion — A Regional Bank
Alternative credit scoring model using transactional behaviour and cash flow patterns — expanding approvals to creditworthy customers with thin bureau files, validated over 24 months with no increase in default rate.
+18%
Approval Rate
0%
Default Change
24mo
Validation
Read Case Study
Ready to Start

The Best AI Project
Is the One That Ships to Production.

We don't build AI strategies. We build AI systems. If you have a business problem, data that might support an ML solution, and the appetite to move from pilot to production — we should talk.

Start with a Use Case Discovery See Customer & Commercial Analytics →