Project Scenarios

AI Projects That Deliver Results

Six detailed scenario examples showing the depth, breadth, and business impact of Cyber Merchant engagements across industries.

⚖️ Legal Generative AI · RAG

AI Contract Review & Risk Analysis Platform

A top-100 UK law firm was spending an average of 4.5 hours per contract review — creating a bottleneck that limited capacity, delayed client deals, and consumed disproportionate senior associate time.

Challenge
  • 10,000+ contracts reviewed monthly across five practice groups
  • Inconsistent clause extraction and risk flagging across reviewers
  • Senior lawyers spending 60% of time on routine review work
  • Client pressure to reduce turnaround from days to hours
Solution
  • Custom RAG system trained on firm's 50,000-document knowledge base
  • Fine-tuned LLM for jurisdiction-specific clause extraction
  • Risk scoring engine with configurable firm-specific playbooks
  • Integration with existing document management system
  • Explainable AI summaries with source citations
87%
Review time reduction
£1.2M
Annual cost savings
4.5h→35m
Per contract review
"An illustrative quote showing client satisfaction with the results. Replace with a real testimonial." — [Client Placeholder]
🏥 Healthcare Machine Learning · MLOps

Predictive Patient Deterioration Early Warning System

An NHS Trust needed earlier identification of patients at risk of deterioration to enable proactive clinical intervention — existing NEWS2 scores were too reactive, flagging deterioration too late for optimal intervention.

Challenge
  • NEWS2 scoring missing subtle early deterioration signals
  • High ICU transfer rates driven by late identification
  • Clinical staff alert fatigue from too many false positives
  • Need for explainable predictions trusted by clinicians
Solution
  • ML model combining 47 clinical variables for 4-hour prediction window
  • SHAP-based explainability showing which signals drove each alert
  • Calibrated threshold per ward type to manage alert volume
  • Integration with EPR and nursing handover workflows
  • DTAC and NHS DSP Toolkit compliant deployment
94%
Prediction accuracy
-31%
ICU transfer reduction
4h
Earlier warning window
"Replace with real clinician or NHS Trust testimonial after deployment." — [Clinical Lead Placeholder]
🏦 Financial Services ML · Real-Time Scoring

Real-Time Fraud Detection Engine

A challenger bank was experiencing rising fraud losses as its customer base scaled rapidly. Its legacy rules-based fraud detection was generating too many false positives (blocking legitimate customers) while missing sophisticated fraud patterns.

Challenge
  • Legacy rules generating 3,000+ false positive blocks per day
  • Sophisticated fraud patterns evolving faster than rules could be updated
  • Customer friction driving churn from false declines
  • Fraud losses growing 35% YoY despite rules investment
Solution
  • Ensemble ML model (XGBoost + neural network) scoring at <10ms latency
  • Graph neural network detecting fraud rings and account-to-account links
  • Unsupervised anomaly detection for novel fraud typologies
  • Automated model retraining on rolling 7-day fraud feedback
  • Explainable decision support for human review queue
-60%
False positive rate
98%
Fraud detection rate
£2.4M
Annual loss reduction
"Replace with real CFO or risk officer testimonial after client project." — [Risk Officer Placeholder]
🏭 Manufacturing Computer Vision · IoT ML

Visual Quality Inspection & Predictive Maintenance

A Tier-1 automotive components manufacturer was experiencing quality escapes reaching customers and frequent unplanned downtime from equipment failures — both causing significant reputational and financial damage.

Challenge
  • Human visual inspection missing subtle surface defects at line speed
  • 15–20 hours of unplanned downtime per month from equipment failures
  • Customer quality rejects increasing penalty payments
  • Inspection cost at 8% of total production cost
Solution
  • Computer vision system with 8 cameras per line achieving 99.7% defect detection
  • IoT sensor data ML model predicting CNC machine failure 72h in advance
  • Real-time OEE dashboard with AI-recommended maintenance windows
  • Edge-deployed inference for <50ms latency at line speed
99.7%
Defect detection rate
-45%
Unplanned downtime
£890K
Annual savings
"Replace with real operations director testimonial after client engagement." — [Operations Director Placeholder]
🛒 Retail Recommendation · Forecasting

Personalisation Engine & Demand Forecasting Platform

A 200-store UK fashion retailer with a growing e-commerce channel was losing revenue to Amazon and Zalando through inferior personalisation and carrying £12M in excess inventory from poor demand forecasting.

Challenge
  • Generic product recommendations not reflecting individual customer preferences
  • Demand forecasting accuracy of 67% causing chronic over/under-stock
  • £12M tied up in excess inventory across 50,000 SKUs
  • Email campaigns with 1.2% conversion rate vs. industry 3.4% benchmark
Solution
  • Two-tower neural network recommendation model with real-time personalisation
  • Hierarchical time series forecasting incorporating 23 external demand signals
  • Dynamic segmentation model for email personalisation at individual level
  • A/B testing framework with multi-armed bandit optimisation
+34%
Online revenue uplift
-28%
Inventory reduction
3.1%
Email conversion rate
"Replace with real e-commerce director or CMO testimonial." — [CMO Placeholder]
🎓 Education NLP · Adaptive Learning

AI-Powered Assessment & Adaptive Learning Platform

A national EdTech platform serving 250,000 students needed to scale personalised learning without proportionally scaling teacher headcount — and needed AI marking that could achieve the consistency and accuracy of expert human markers.

Challenge
  • Marking backlog averaging 8 days delaying student feedback loops
  • Inconsistency in marking between assessors on long-form answers
  • One-size-fits-all curriculum not adapting to individual knowledge gaps
  • High dropout rate of 23% attributed to disengagement
Solution
  • Fine-tuned LLM for automated marking with 94% inter-rater agreement
  • Knowledge graph-based adaptive learning path generator
  • Early warning model for dropout risk triggering automated interventions
  • Explainable feedback generator providing constructive, personalised comments
94%
Marking agreement rate
8d→4h
Feedback turnaround
-31%
Student dropout rate
"Replace with real CTO or academic director testimonial." — [Academic Director Placeholder]

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