AI That Moved the Needle
Four industries. Four problems. Measurable results in every deployment. Here's exactly what happened.
AI Clinical Documentation Engine
12-hospital regional network · 340 physicians · 14-week deployment
The Problem
Physicians at this regional network were spending 3+ hours per day on clinical notes — after patient hours. Burnout rates were climbing, a key driver of attrition. The EHR vendor's native voice tool had an 18% error rate and required heavy manual correction. Staff morale was low, and physician onboarding took 3× longer than the national average because new doctors dreaded the documentation burden.
Physicians dictated into a legacy voice tool, corrected errors manually, then copy-pasted into the EHR. Average note took 18 minutes. Weekend catch-up was standard practice across 80% of the physician pool.
AI listens to the physician-patient interaction, generates a structured draft note mapped to ICD-10 codes, and pushes it into the EHR for one-click review. Average note completion: 6 minutes. 94% accepted with zero edits.
What We Built
A HIPAA-compliant real-time audio pipeline using fine-tuned medical ASR (automatic speech recognition), a custom NLP model trained on 2.4M clinical notes, and a bidirectional FHIR R4 integration with the client's existing EHR. The system runs entirely in the client's private cloud — no PHI leaves their environment.
We also built a physician feedback loop: every rejection or edit improves the model. By week 8, the acceptance rate had climbed from 71% to 94%.
Fleet Operations Intelligence Platform
Mid-size freight carrier · 620 vehicles · 9-week deployment
The Problem
Operations were running on 14 separate spreadsheets maintained by different regional dispatch teams. Data was siloed, version-controlled by email, and 4–6 hours out of date by the time it reached decision-makers. Dispatch errors — wrong vehicle, wrong driver, wrong route — cost the business an average of $28,000/month in redelivery, penalty fees, and overtime. Route optimization was done manually using experience and intuition.
Manual spreadsheet updates, email threads for route changes, no real-time vehicle visibility. Dispatch decisions based on stale data. Regional teams working in isolation — no central source of truth.
Live GPS tracking, AI-optimized route suggestions, predictive ETA calculations, and automated driver assignments — all in one dashboard accessible to every dispatch team simultaneously. Exceptions surface automatically.
What We Built
A real-time data platform ingesting GPS telemetry, traffic feeds, and weather APIs. A route optimization engine using constraint-based algorithms that account for vehicle type, driver hours, weight limits, and client delivery windows. A React-based operations dashboard with role-based views for dispatchers, regional managers, and executives. Slack and SMS alert integration for exceptions and delays.
Demand Forecasting & Inventory Automation
Omnichannel grocery chain · 38 locations · 11-week deployment
The Problem
The chain's buying team was working from weekly sales reports with no predictive capability. Seasonal demand spikes — bank holidays, school terms, local events — were handled reactively, leading to chronic stockouts on fast-moving lines and significant overstock on slow-moving ones. Perishable waste alone was running at £480,000 per year. A competitor had gained 7 points of local market share in 18 months partly on availability.
Weekly CSV exports from the POS system, analysed in Excel by the buying team. Orders placed based on last week's sales ± gut feel. No integration between online and in-store inventory. Holiday planning done 2 weeks out.
Live POS + e-commerce inventory sync. ML model forecasting demand at SKU + store level up to 8 weeks ahead, incorporating 14 external signals. Automated purchase orders drafted and sent for human approval. Perishable lines flagged for markdown 48h before expiry.
What We Built
A demand forecasting engine trained on 3 years of transactional history, enriched with external signals (weather, local events, school calendars, competitor promotions). An inventory management layer that syncs in real time across 38 stores and the e-commerce platform. Automated PO generation with buyer approval workflow. A margin dashboard showing the financial impact of stockouts vs. overstock in real time.
Real-Time Fraud Detection Engine
Digital lending platform · 1.2M active users · 8-week deployment
The Problem
The platform's rule-based fraud system was outdated: fraudsters had mapped its thresholds and were exploiting gaps systematically. Fraud losses were running at 1.8% of loan origination volume — nearly 3× the industry benchmark. The existing system also had a 4.2% false positive rate, meaning legitimate borrowers were being declined or flagged, causing churn and a surge in customer service contacts. The team needed a model that ran at transaction speed with fewer false alarms.
Static rule engine with 140 hard-coded thresholds. Rules reviewed quarterly, often after losses had already occurred. 4.2% false positive rate causing customer churn. No behavioural signals — decisions based only on application data at point-in-time.
Gradient boosting model trained on 18 months of labelled transaction data, incorporating 80+ behavioural and contextual features. Model scores every application in under 90ms. Continuous retraining pipeline updates the model weekly. False positives down to 0.3%.
What We Built
An ML scoring service deployed as a low-latency microservice integrated into the loan origination API. The model uses device fingerprinting, behavioural biometrics, network graph analysis, and application data to produce a fraud probability score. A continuous learning pipeline retrains the model on confirmed fraud outcomes weekly. A model explainability layer gives compliance teams plain-English rationale for every decline decision — essential for FCA/RBI reporting.
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