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8 Industries — Deep AI Expertise — 200+ Projects

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We build AI solutions tailored to your industry, combining advanced models with deep domain understanding across eight verticals. That means designing systems that align with how your sector actually works from data pipelines and compliance requirements to decision-making processes and measurable outcomes that matter in your business.

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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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