Case Studies

Proof, not promises.

Anonymised accounts of real systems built for real operational environments. The details that matter to a procurement officer: what the problem was, how we approached it, and what happened in production.

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Nuclear & Energy
UK Nuclear Licensed Site — Predictive Maintenance
Reducing unplanned shutdowns at a UK nuclear generating station by 34%
A UK nuclear operator was experiencing a pattern of unplanned equipment failures that were not being detected by existing condition monitoring. Gorp Labs designed and deployed a predictive maintenance system that integrated vibration, thermal, and process data to surface early indicators of developing faults — three to fourteen days before traditional alerting would trigger.
34%
Reduction in unplanned events
11d
Average early warning lead time
18mo
In production without incident
ML Engineering Deployment ONR Compliant
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Healthcare
NHS Trust — Elective Care
Emergency department demand forecasting for an NHS Trust
A busy NHS Trust needed to anticipate ED demand 48–72 hours ahead to optimise staffing and bed management. Gorp Labs built a multi-variate forecasting system integrating historical attendance, seasonal patterns, and local population data.
89%
Forecast accuracy at 48h
£2.1M
Annual agency cost reduction
ML EngineeringNHS DTAC
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National Security
UK Government Programme — Intelligence
Large-scale document intelligence for a classified government programme
A government programme needed to process and cross-reference tens of thousands of documents in a classified environment. Gorp Labs built an air-gapped NLP pipeline for entity extraction, relationship mapping, and semantic search with full audit logging.
40k+
Documents processed
6wk
Analyst time saved per quarter
NLPAir-gappedSC Cleared
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Infrastructure
Distribution Network Operator
Fault prediction and prioritisation for a UK electricity distribution network
A DNO needed to prioritise maintenance spend across an ageing network of substations and overhead lines. Gorp Labs built a risk-based asset health model integrating inspection records, fault history, and environmental data to produce a prioritised maintenance schedule.
28%
Reduction in fault frequency
£4.7M
Maintenance cost optimised
ML EngineeringData Architecture
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Healthcare
NHS Foundation Trust — Clinical Informatics
Structured data extraction from unstructured clinical correspondence
Clinical letters and discharge summaries contained critical structured information — diagnoses, medications, follow-up actions — buried in free text. Gorp Labs built an NLP extraction pipeline achieving 94% accuracy across 47 clinical entity types.
94%
Extraction accuracy
3.2h
Clinician time saved daily
Clinical NLPMHRA
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Financial Services
UK Retail Bank — Financial Crime
Explainable fraud detection replacing a legacy rule-based system
A retail bank's rule-based fraud detection system generated high false-positive rates that were degrading customer experience and analyst capacity. Gorp Labs replaced it with an explainable ML system that reduced false positives by 61% while improving fraud capture rate.
61%
False positive reduction
+19%
Fraud capture improvement
ML EngineeringFCA Compliant
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Nuclear & Energy
Nuclear Decommissioning Programme
Safety case document analysis across a nuclear decommissioning programme
A decommissioning programme needed to cross-reference safety case documentation spanning decades and multiple document management systems. Gorp Labs built a semantic search and cross-reference system designed to operate on-site under strict data governance constraints.
180k
Documents indexed
72%
Search time reduction
NLPOn-premiseONR
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Infrastructure
Water & Wastewater Utility
Leakage detection and pipe failure prediction for a water distribution network
A water utility needed to prioritise pipe replacement across a network of over 12,000km. Gorp Labs built a failure probability model integrating pipe age, material, soil conditions, and historical burst data to guide capital investment planning.
3.1×
Improvement in burst prediction
£8M
CapEx optimised annually
ML EngineeringData Architecture
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Healthcare
NHS ICU — Early Warning
Early sepsis detection in ICU patients using multi-variate vital sign modelling
An ICU team wanted to detect sepsis onset earlier than existing NEWS2 scoring. Gorp Labs built a multi-variate time-series model trained on the Trust's own historical data, with explainability features that allowed clinical review of each alert.
4.2h
Earlier average detection
91%
Sensitivity at 0.8 specificity
Clinical AIMHRA AIaMD
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Our approach to evidence

Why we anonymise. Why it still matters.

Our clients operate in sensitive environments. Details that seem innocuous — the specific asset type, the data architecture, the performance envelope — can constitute sensitive information in nuclear, defence, or classified contexts.

We anonymise all case studies by default. If you need more detail for a procurement decision, we can arrange reference conversations under NDA with the relevant client teams where permission has been granted.

01

Real engagements only. Every case study on this page represents a system in production. We do not publish proofs of concept or internal research as client work.

02

Metrics verified. All performance figures have been confirmed with the client team. We do not publish projected or estimated outcomes as results.

03

Production longevity. We track system performance after handover. The metrics shown reflect sustained production performance, not launch-day numbers.

04

Reference available on request. For most case studies, we can arrange a reference conversation with the client team under NDA for serious procurement enquiries.

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