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8 Industries — Deep AI Expertise — 200+ Projects

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Generic AI doesn’t solve industry-specific problems. We bring deep sector expertise across 8 verticals — understanding the data, the regulations, the workflows, and the outcomes that actually matter in your world.

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Expertise in:
✓ Regulated sectors ✓ Data-heavy ops ✓ Legacy systems
Industries Served
8
Deep vertical expertise — not generalist AI consulting
200+
Projects
285%
Avg ROI
20+
Countries
98%
Satisfaction

Trusted by industry leaders worldwide

Healthcare Systems Global Banks Retail Giants Manufacturers Law Firms Logistics Operators Energy Companies SaaS Platforms
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Industry 01 / 08

Healthcare & Life Sciences

AI that improves patient outcomes, reduces clinician burden, and navigates the regulatory complexity that makes healthcare one of the hardest — and highest-impact — domains for AI.

Key Challenges
  • Clinicians spending 30–40% of their time on documentation rather than patient care
  • Diagnostic errors costing an estimated $100B+ annually in the US alone
  • Fragmented EHR systems making patient data nearly impossible to aggregate
  • Regulatory requirements (FDA, CE Mark, HIPAA) adding 12–24 months to AI deployment
  • Clinical validation requirements often absent or inadequate before deployment
  • Staff resistance to AI tools perceived as threatening clinical judgment
How We Solve Them
  • Ambient clinical documentation AI that auto-generates notes from consultations
  • Diagnostic imaging AI for radiology, pathology, and dermatology with CE/FDA pathway
  • Predictive readmission and deterioration models integrated into existing EHR workflows
  • Regulatory-first design: we build the clinical validation plan before the model
  • NLP pipelines that extract structured data from unstructured clinical notes at scale
  • Change management programmes designed specifically for clinical adoption
AI Use Cases & Outcomes
Medical Image Diagnosis
94% detection accuracy
Readmission Prediction
32% reduction in readmissions
Clinical Documentation AI
70% time saved on notes
Drug Interaction Prediction
45% fewer adverse events
Appointment Optimisation
28% fewer no-shows
EHR Data Extraction
10× faster than manual
Client Result
Regional Hospital Network — Diagnostic Imaging AI
“Radiologists using the system are reviewing 40% more scans per day with higher accuracy than before. The AI flags what needs urgent attention — and it’s right 94% of the time.”
Chief Medical Officer, 12-hospital network (anonymised) — 14-week deployment
300%
ROI Year 1
60%
Faster diagnosis
14wk
To deployment
Technology Stack
PyTorch MONAI (Medical Imaging) HL7 FHIR Integration AWS HealthLake Hugging Face (Clinical NLP) Azure Health APIs DICOM / PACS Integration MLflow + HIPAA-compliant logging
Book Healthcare AI Consultation → View All Healthcare Use Cases
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Industry 02 / 08

Financial Services

From fraud detection to automated underwriting — AI that operates at the speed and scale financial institutions demand, within the regulatory constraints they can’t avoid.

Key Challenges
  • Fraud losses exceeding $40B annually — real-time detection is an operational necessity
  • KYC and AML processes costing global banks $274B per year in compliance spend
  • Model Risk Management (SR 11-7) adding 6–18 months to AI deployment timelines
  • Legacy core banking systems that cannot easily expose data to ML pipelines
  • Explainability requirements — regulators demand models that justify every decision
  • Customer churn driven by poor personalisation in an era of challenger banks
How We Solve Them
  • Real-time fraud detection with sub-100ms inference and <0.1% false positive rate
  • Automated KYC/AML document processing reducing onboarding from days to minutes
  • Interpretable credit scoring models with full SHAP-based explanations for regulators
  • Legacy system integration via API layers — no core system migration required
  • Model risk management documentation built into every deployment
  • Customer churn prediction with personalised retention intervention targeting
AI Use Cases & Outcomes
Real-time Fraud Detection
95% detection, 60% fewer false positives
Credit Risk Scoring
22% better default prediction
KYC Document Processing
85% faster onboarding
Churn Prediction
35% reduction in churn rate
Regulatory Doc Analysis
90% reduction in review time
Personalised Financial Advice
42% increase in cross-sell revenue
Client Result
Tier-2 European Bank — Real-time Fraud Detection
“We went from catching 71% of fraud with a 4% false positive rate to catching 95% with under 0.8% false positives. The model pays for itself every month in prevented losses alone.”
Head of Financial Crime, European commercial bank (anonymised) — 10-week deployment
$4.2M
Annual fraud prevented
95%
Detection rate
10wk
To production
Technology Stack
XGBoost / LightGBM SHAP (Explainability) Apache Kafka (Real-time) Databricks Azure OpenAI (Doc Processing) Feast (Feature Store) MLflow + Model Registry ISO 27001 compliant infra
Book Financial Services Consultation → View All Finance Use Cases
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Industry 03 / 08

Retail & E-commerce

AI that drives revenue per visit, reduces inventory waste, and creates the personalised experience that turns one-time buyers into lifetime customers — at any catalogue scale.

Key Challenges
  • Average e-commerce conversion rate still only 2–3% — 97% of visitors leave empty-handed
  • Inventory holding costs averaging 25–30% of product value annually
  • Stockouts costing global retail an estimated $1.1T in lost sales per year
  • Customer acquisition costs rising 60% over 5 years while retention rates stagnate
  • Manual product content creation unable to keep pace with catalogue growth
  • Returns rates exceeding 30% in fashion — largely from poor product discovery
How We Solve Them
  • Personalised recommendation engines that drive 40–60% of total revenue
  • Demand forecasting with 35% reduction in stockouts and overstock simultaneously
  • Dynamic pricing that responds to competitor pricing, demand signals, and margin targets
  • AI-powered customer service handling 80%+ of enquiries without human intervention
  • Automated product description and content generation at catalogue scale
  • Visual search and size recommendation to reduce return rates and improve conversion
AI Use Cases & Outcomes
Product Recommendation
45% revenue uplift per session
Demand Forecasting
35% fewer stockouts
Dynamic Pricing
12% margin improvement
AI Customer Service
80% self-service resolution
Visual Search
23% conversion uplift
Content Generation
90% time saved on copy
Client Result
Omnichannel Retailer — Personalisation & Demand Forecasting
“Within 90 days of deploying the recommendation engine we saw a 38% increase in revenue per session. The demand forecasting model saved us $4.2M in the first year by nearly eliminating end-of-season overstock.”
Chief Digital Officer, 300-store omnichannel retailer (anonymised) — 12-week deployment
$4.2M
Inventory savings yr 1
38%
Revenue per session
320%
ROI Year 1
Technology Stack
Collaborative Filtering (PyTorch) Prophet / NeuralProphet Apache Spark (Real-time events) GPT-4 / Claude (Content Gen) Pinecone (Vector Search) Shopify / Magento integration CLIP (Visual Search)
Book Retail AI Consultation → View All Retail Use Cases
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Industry 04 / 08

Manufacturing & Industry 4.0

AI that connects the factory floor to the intelligence layer — reducing unplanned downtime, eliminating defects before they reach customers, and optimising production schedules in real time.

Key Challenges
  • Unplanned downtime costing manufacturers an average of $260,000 per hour
  • Quality defect detection still predominantly manual — slow, inconsistent, and fatigue-prone
  • OT/IT integration gap: factory sensors and SCADA systems locked away from cloud ML
  • Supply chain disruptions exposing brittle just-in-time inventory strategies
  • Energy costs representing 20–40% of operational expenditure with no optimisation
  • Skilled operator knowledge undocumented and lost when people retire
How We Solve Them
  • Predictive maintenance models trained on vibration, temperature, and current sensor data
  • Computer vision quality control at line speed — 99%+ defect detection accuracy
  • OT/IT bridge architecture that connects factory sensors to cloud ML without rearchitecting SCADA
  • Supply chain disruption prediction with 60% of disruptions flagged 14+ days in advance
  • Production schedule optimisation increasing OEE by 15–22% in first deployment
  • Energy consumption optimisation using load forecasting and process parameter ML
AI Use Cases & Outcomes
Predictive Maintenance
50% less unplanned downtime
Visual Quality Control
99.2% defect detection
Production Optimisation
18% OEE improvement
Supply Chain Prediction
60% of disruptions flagged early
Energy Optimisation
20% reduction in energy spend
Quality Documentation
95% less reporting time
Client Result
Tier-1 Auto Parts Manufacturer — Predictive Maintenance & Visual QC
“In the first 6 months we avoided 11 unplanned shutdowns — that’s an estimated $8M in avoided downtime costs. The visual QC system caught defects our line operators were missing at high line speeds.”
VP Operations, Tier-1 automotive supplier, 4 plants (anonymised) — 16-week deployment
$8M
Avoided downtime costs
99.2%
Defect detection
18%
OEE improvement
Technology Stack
YOLOv8 (Visual QC) Time-series LSTM / Transformer AWS IoT Greengrass (Edge) OSIsoft PI / Historian integration OPC-UA (SCADA bridge) NVIDIA Jetson (Edge inference) Grafana (OEE dashboards)
Book Manufacturing AI Consultation → View All Manufacturing Use Cases
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Industry 06 / 08

Logistics & Supply Chain

AI that compresses delivery timelines, predicts disruptions before they happen, and extracts every point of efficiency from networks that move millions of shipments per day.

Key Challenges
  • Last-mile delivery costs consuming 53% of total shipping costs with no clear optimisation
  • Freight document processing still largely manual — bills of lading, customs forms, PODs
  • Customer service teams overwhelmed by shipment tracking enquiries (60%+ of contact volume)
  • Fleet maintenance done on fixed schedules rather than condition — wasteful and still unreliable
  • Demand volatility making warehouse slotting and staffing decisions increasingly inaccurate
  • Port and carrier delays blindsiding planning teams with no advance warning system
How We Solve Them
  • Last-mile route optimisation reducing fuel costs by 22% and adding 18% more deliveries per driver
  • Automated freight document processing with 90% reduction in manual handling time
  • Conversational AI handling 70%+ of tracking enquiries — live, 24/7, in any language
  • Fleet predictive maintenance reducing unplanned breakdowns by 45%
  • Warehouse slotting and pick path optimisation cutting travel distance by 35%
  • Shipment delay prediction giving planners 4× earlier visibility into disruptions
AI Use Cases & Outcomes
Route Optimisation
22% fuel cost reduction
Delay Prediction
4× earlier warning
Document Automation
90% less processing time
Warehouse Optimisation
35% less travel distance
Fleet Maintenance AI
45% less unplanned downtime
Customer Service AI
70% enquiry automation
Client Result
3PL Operator — Route Optimisation & Delay Prediction
“The route optimisation model saved us $2.8M in fuel in year one. But the delay prediction system was the real game-changer — we’re now managing customer expectations proactively instead of reacting to complaints.”
COO, mid-size 3PL operator, 800 vehicles (anonymised) — 10-week deployment
$2.8M
Fuel savings year 1
22%
Fuel cost reduction
290%
ROI Year 1
Technology Stack
OR-Tools (Route Optimisation) LSTM (Time-series prediction) GPT-4 (Document processing) SAP / Oracle TMS integration AWS Location Services IoT telematics integration Twilio (Customer notifications)
Book Logistics AI Consultation → View All Logistics Use Cases
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Industry 07 / 08

Energy & Utilities

AI for the energy transition — from grid load balancing and renewable output forecasting to equipment failure prediction and ESG reporting automation, across conventional and renewable assets.

Key Challenges
  • Grid balancing costs rising as intermittent renewables increase grid complexity
  • Offshore and remote asset inspection requiring expensive, slow manual processes
  • Renewable energy output curtailment costing operators billions in lost generation revenue
  • Equipment failures in oil & gas causing $38B in unplanned shutdown costs annually
  • ESG and carbon reporting becoming mandatory with no scalable collection mechanism
  • Demand forecasting accuracy insufficient for modern grid balancing requirements
How We Solve Them
  • Energy demand forecasting with 30% improvement in accuracy versus statistical baselines
  • Drone + computer vision asset inspection — 80% faster and 90% cheaper than manual
  • Renewable output prediction reducing curtailment losses by 25%
  • Oil & gas equipment failure prediction reducing unplanned shutdowns by 55%
  • Grid load balancing optimisation reducing balancing costs by 15% at scale
  • Automated ESG and carbon reporting reducing report preparation time by 95%
AI Use Cases & Outcomes
Demand Forecasting
30% accuracy improvement
Drone Asset Inspection
80% faster inspections
Renewable Prediction
25% less curtailment
Equipment Failure AI
55% fewer shutdowns
Grid Optimisation
15% lower balancing costs
ESG Reporting AI
95% less reporting time
Client Result
Renewable Energy Operator — Output Prediction & Asset Inspection
“The curtailment reduction alone was worth $3.1M in the first year. Replacing manual tower inspections with the drone AI system cut our inspection costs by 88% and found defects our engineers had been walking past for two years.”
Head of Operations, 2GW renewable energy operator (anonymised) — 12-week deployment
$3.1M
Curtailment savings yr 1
88%
Inspection cost cut
340%
ROI Year 1
Technology Stack
NeuralProphet (Forecasting) YOLOv8 (Drone Inspection CV) SCADA / Historian integration AWS IoT (Sensor ingestion) DroneDeploy / custom UAV integration OpenMeteo / ERA5 weather APIs Grafana (Operations dashboard)
Book Energy AI Consultation → View All Energy Use Cases
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Industry 08 / 08

Tech & SaaS

AI that accelerates product development, reduces churn, compresses support costs, and embeds intelligent capability directly into your product — making it better every time a user interacts with it.

Key Challenges
  • SaaS churn rates averaging 5–7% monthly — AI can predict and prevent 40% of departures
  • Engineering teams spending 30–40% of time on repetitive boilerplate code and review
  • Support ticket volumes scaling linearly with user growth — unsustainable unit economics
  • Product search and discovery failing users — most users never find features they’d love
  • Expansion revenue underperforming because usage patterns predicting upgrade intent go unread
  • Time-to-value for new users too long — AI-driven onboarding can compress this dramatically
How We Solve Them
  • Customer churn prediction identifying at-risk accounts 30+ days before cancellation
  • AI code review and bug detection integrated into CI/CD pipeline — 25% fewer production bugs
  • Intelligent support ticket triage with 60% faster response and automatic routing
  • Semantic search across product and documentation — 50% reduction in support volume
  • In-product AI writing assistant driving 35% increase in daily active usage
  • Expansion revenue prediction identifying upgrade opportunities 4 weeks in advance
AI Use Cases & Outcomes
Churn Prediction
40% reduction in churn
AI Code Review
25% fewer production bugs
Support Triage AI
60% faster response times
Semantic Search
50% fewer support tickets
In-product AI Assistant
35% DAU increase
Expansion Prediction
30% more expansion revenue
Client Result
Series B SaaS Platform — Churn Prediction & In-product AI
“The churn model gave our customer success team a 35-day head start on at-risk accounts. Combined with the in-product AI assistant, we reduced monthly churn from 4.2% to 2.6% in six months — that’s $3.8M in ARR recovered.”
CEO, Series B B2B SaaS platform, 800 enterprise customers (anonymised) — 8-week deployment
$3.8M
ARR recovered
38%
Churn reduction
410%
ROI Year 1
Technology Stack
XGBoost (Churn Prediction) GPT-4 / Claude (In-product AI) LangChain + RAG (Semantic Search) Segment / Mixpanel (Behavioural data) Pinecone (Knowledge base vectors) dbt + Snowflake (Feature pipeline) Stripe / HubSpot integration
Book Tech & SaaS Consultation → View All SaaS Use Cases
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