How We Help

Real problems,
real solutions

See how organizations across manufacturing, banking, healthcare, retail, and logistics transform data challenges into competitive advantages. Real businesses, real results, real growth.

Manufacturing01 / 05

Your best engineers are fighting fires. They should be preventing them.

60% Reactive Maintenance

Every unplanned stoppage costs £10k–£50k/hr in lost production.

OEM-Locked Tools

Siemens, GE, ABB tools see one machine — your multi-vendor plant has no unified layer.

Siloed Data

Quality and uptime data don't connect. Root causes stay invisible.

30-50%

Downtime Reduction

McKinsey

20-40%

Extended Machine Life

McKinsey

6-12 mo

Payback Period

Industry avg

"Digitized maintenance drove an average 20% reduction in downtime — starting with existing machine and historian data."

McKinsey Operations Practice

10–14 weeks · $180k–$350k
Manufacturing intelligence
0%
downtime reduced
UNIT 05 — CRITICAL
Bearing temp: 127°C
Predicted failure: 4.2 hrs

The Problem

Manufacturing plants operate with 60% reactive maintenance, where OEM-locked tools can't see cross-vendor equipment and siloed quality data hides root causes. Every unplanned stoppage costs £10k–£50k/hr in lost production.

The Solution

Unified predictive maintenance layer that ingests PLC/SCADA, historian, and MES data from all vendors, detects anomalies before failures, and estimates remaining useful life with 95% accuracy.

Key Benefits

  • Reduce downtime by 30–50% through early detection
  • Extend machine life by 20–40%
  • ROI in 6–12 months
  • Enable shift handovers based on predicted failures

Technical Architecture

Your Data

PLC/SCADA
Historian
MES
CMMS/EAM
QMS + Vision

Intelligence

Anomaly Detection
RUL Estimation
Downtime Classification
Quality Models

Action

Maintenance Copilot
CMMS Write-back
Shift Handovers
Ops Dashboard
Banking & Fintech02 / 05

Your analysts are drowning in alerts that aren't real fraud.

95% False Positives

Real fraud slips through while the team chases shadows.

2026 APP Scam Liability

Real-time decisioning is now regulatory requirement — batch monitoring won't pass scrutiny.

Explainability Gaps

Black-box models don't pass SR 11-7 model-risk review.

$4.4T

Illicit Activity 2025

Nasdaq

31%

False-Positive Reduction

NICE Actimize

$30M

3-Year Savings

Feedzai

"Analytical and agentic AI are especially relevant for false-positive reduction, transaction monitoring, and fraud prevention."

McKinsey FS Practice

12–16 weeks · $200k–$450k
Financial crime intelligence
False positive rate
0%
of alerts waste analyst time
AI identified
TXN-441206 · £14,200
Score: 0.94 · APP scam

The Problem

95% of fraud alerts are false positives, real fraud slips through, and black-box models can't pass SR 11-7 regulatory review. Each analyst spends thousands of hours chasing shadows instead of catching real crimes.

The Solution

Explainable AI that combines ML fraud scoring with graph-based entity risk and AML typologies to reduce false positives while maintaining 100% recall and meeting regulatory requirements.

Key Benefits

  • Reduce false-positive rate by 31%
  • Save 1,800+ investigation hours per quarter
  • Maintain 100% fraud recall
  • Pass SR 11-7 model risk reviews

Technical Architecture

Your Data

Payments/Cards
Digital Sessions
Device/IP
KYC/CRM
Watchlists

Intelligence

ML Fraud Scoring
Graph Entity Risk
AML Typologies
Decision Orchestration

Action

Investigator Copilot
SAR Drafting
Case Mgmt
Model Monitoring
Healthcare03 / 05

Hospitals are spending $43 billion collecting money they already earned.

15% Claims Denied

For a $1B system, that's $150M delayed — 60% preventable with better data.

Prior Auth Denials +56%

Each manual appeal takes 45+ minutes — payers rely on giving up.

2026 CMS FHIR Mandate

API-based prior auth required. Most RCM teams unprepared technically.

$43B

Chasing Owed Payments

AHA 2025

46%

Auth Denial Reduction

Waystar

30-60%

Cost-to-Collect Reduction

McKinsey 2025

"AI-enabled provider revenue-cycle operations could reduce cost-to-collect by 30 to 60 percent."

McKinsey 2025 US Care Delivery Survey

8–12 weeks · $120k–$280k
Healthcare RCM Intelligence
Industry cost of denial
$0B
annual · owed but uncollected
Denial rate
0%
first submission · private payer avg

The Problem

Hospitals are chasing $43 billion in owed payments. 15% of claims denied, prior auth denials up 56%, and the 2026 CMS FHIR mandate requires API-based automation—most RCM teams unprepared.

The Solution

AI-powered revenue cycle platform that detects denial propensity before submission, automates prior auth through payer APIs, detects underpayments, and queues high-impact appeals first.

Key Benefits

  • Reduce denial rate by 46%
  • Cut cost-to-collect by 30–60%
  • Meet 2026 FHIR API mandate
  • Recover $150M+ for a $1B health system

Technical Architecture

Your Data

EHR/EMR
ADT + Scheduling
Eligibility
ERA/Remittance
Documents

Intelligence

Denial Propensity
Coding Checks
Underpayment Detection
Queue Prioritization

Action

Appeals Copilot
Prior-Auth Copilot
RCM Workbench
Payer Analytics
Retail & CPG04 / 05

Your inventory is either too much or too little — never just right.

Excess & Stockout

Markdown pressure on overstock, stockout penalties on understock—both kill margin.

Promo ROI Blind

You run promotions, but don't know which ones drive profit vs. just theft of future sales.

Vendor Lock-in

Legacy category management tools can't ingest external data or run real-time scenarios.

5-15%

Inventory Cost Reduction

Gartner

20-30%

Forecast Accuracy Improvement

McKinsey Retail

2-3x

Faster Scenario Planning

Industry avg

"AI-enabled demand planning can reduce forecast errors by 20–30% while cutting markdown exposure by 2–4 points of COGS."

McKinsey Consumer Excellence Practice

10–14 weeks · $150k–$300k
Retail intelligence
Demand surface · live
Margin at risk
£0K
this week · this category
Stock-outs
0
SKUs · right now

The Problem

Inventory is either too much (markdown pressure) or too little (stockout penalties)—both kill margin. Promo ROI is blind, legacy tools can't ingest external data or run real-time scenarios.

The Solution

AI demand forecasting that reduces forecast errors by 20–30%, optimizes markdowns, measures promo effectiveness, and runs real-time scenario planning across all channels.

Key Benefits

  • Reduce inventory costs by 5–15%
  • Improve forecast accuracy by 20–30%
  • Cut markdown exposure by 2–4 COGS points
  • Increase inventory turns by 12–18%

Technical Architecture

Your Data

POS
Inventory
Promotions
Weather
Events/Social

Intelligence

Demand ML
Markdown Optimizer
Promo Effectiveness
Assortment Rules

Action

Buying Copilot
Scenario Planning
Retail Dashboards
Supplier Alerts
Logistics05 / 05

Every delivery is a black box. You can't see efficiency or sustainability.

Visibility Blind Spots

Third-party logistics hide operational detail. You have no visibility to act on inefficiency.

Carbon Liability

ESG reporting is manual. Scope 3 emissions tracking is incomplete or wrong.

Legacy TMS Limits

Your system can't route on carbon, can't handle multi-modal, can't learn from data.

10-20%

Cost Reduction

Gartner

30%

Carbon Reduction

McKinsey Logistics

48hr

Visibility Lag (avg)

Industry

"Real-time supply chain analytics can reduce logistics costs by 10–20% and cut carbon by 30% through better route and mode optimization."

McKinsey Supply Chain Practice

12–16 weeks · $200k–$400k
Control tower live
Expedite cost MTD
£0K
avoidable with AI visibility
Scope 3 today
0t
CO₂e · target: 720t

The Problem

Third-party logistics hide operational detail. ESG reporting is manual. Legacy TMS can't route on carbon, can't handle multi-modal, can't learn from data. Average 48-hour visibility lag.

The Solution

Real-time supply chain analytics that optimizes routes for cost AND carbon, tracks Scope 3 emissions automatically, and provides visibility-to-action across all modes.

Key Benefits

  • Reduce logistics costs by 10–20%
  • Cut carbon emissions by 30%
  • Close visibility lag from 48hr to real-time
  • Meet ESG reporting requirements automatically

Technical Architecture

Your Data

TMS/WMS
GPS/Telematics
Fuel Data
Carbon Intensity
Order Stream

Intelligence

Route Optimization
Carbon Scoring
Demand Prediction
Carrier Analytics

Action

Driver Copilot
Carbon Dashboard
Shipper Portal
Cost Allocation

The Next Step

Your KPI baseline.
Two weeks. Fixed fee.
No commitment.

We quantify your opportunity before you commit to a program. Most clients see the case for a full proof within the first baseline audit.

Stack-neutral
Human-in-the-loop
KPI instrumented