Brief
The problem
Cloud-hosted foundation models could not be used because of data residency, regulatory and sovereignty constraints. Existing internal automation was rule-based and could not handle the reasoning workloads the business required.
Case Studies
Fourteen recent engagements.
The case studies below cover fourteen engagements delivered between January 2025 and the present, across the firm's principal sectors. Each is presented under sector pseudonymity. Client identities, jurisdictional specifics, commercial terms and technical particulars are withheld under confidentiality, and are available only under non-disclosure on a case-by-case basis.
01
Banking · Sovereign AI
Sovereign LLM Deployment.
A regulated banking institution required a private, on-premise large language model capable of serving compliance, credit and customer-operations functions across multiple jurisdictions, with full data residency and zero external inference dependencies.
Engagement
What we delivered
End-to-end architecture, fine-tuning, deployment and operation of a private LLM environment running entirely within the client's infrastructure. Document intelligence, compliance triage, credit-memo drafting and customer-operations co-pilots, all delivered against a single governance framework.
Outcome
What it produced
Compliance review cycles compressed from 10 working days to under 36 hours on covered case types. Credit-memo first drafts produced in under 4 minutes against an evaluated factuality threshold of 98.2%. Zero outbound inference — all model calls, embeddings and logs retained inside the client’s regulated perimeter, with model-risk documentation and audit trails sufficient for regulatory inspection.
02
Logistics · Document Intelligence
Autonomous Document Intelligence.
A multi-jurisdictional logistics operator was processing high volumes of multi-language shipping, customs and commercial paperwork. The objective was to compress processing time and reduce manual handling while preserving the audit trails the business is required to maintain.
Brief
The problem
Document volume and language diversity exceeded the throughput of manual review. The existing OCR could not cope with the structural variation in commercial paperwork, and downstream business systems required structured, validated output.
Engagement
What we delivered
A document intelligence platform combining OCR, vision-language models and LLM extraction. Multi-language handling, validation against business rules, structured-output APIs into downstream systems, and complete audit logging.
Outcome
What it produced
Manual touch reduced by 78% across the highest-volume document classes. Straight-through processing achieved on 64% of inbound paperwork, with median per-document handling time falling from 11 minutes to 42 seconds. Every extraction carries a citation back to the source page and a confidence score, with audit trails retained for the inspection window required by each customs authority in scope.
03
Manufacturing · ERP Enhancement
AI-Powered ERP Enhancement.
A manufacturing enterprise wanted to extract more operational value from a mature ERP environment without undertaking a platform replacement. The target areas were demand forecasting, supplier-risk analysis and procurement support.
Brief
The problem
The existing ERP was stable, embedded and unsuited to replacement. The business required predictive analytics and conversational assistance for procurement and operations users that the platform did not natively provide.
Engagement
What we delivered
An AI layer integrated alongside the existing ERP: forecasting models for demand and inventory, supplier-risk scoring drawing on internal and external signals, and natural-language co-pilots embedded within procurement and operations workflows.
Outcome
What it produced
Demand-forecast error reduced by 34% (MAPE) against the incumbent baseline across 6,800 SKUs. Supplier-risk scores refreshed daily across the active vendor base of 1,400 suppliers, replacing a quarterly cycle. Procurement co-pilots cut time-to-PO on routine categories by 58%, with the existing ERP retained as the system of record throughout.
04
Critical Infrastructure · Cybersecurity
Cybersecurity Modernisation.
An operationally sensitive client in a critical-infrastructure sector required a comprehensive uplift to its security posture, combining AI-driven anomaly detection with a managed-services model, while preserving existing investments in controls.
Brief
The problem
The existing security tooling was generating signal volumes the in-house team could not triage at the required pace. The client needed both an architectural uplift and an external operational capability to respond at the speeds the threat environment demanded.
Engagement
What we delivered
A re-architected security posture combining AI anomaly detection with continuous compliance monitoring, layered over existing controls. A managed-services model wrapped around the new platform to provide round-the-clock triage and incident response.
Outcome
What it produced
Mean time to detect compressed from 4.2 hours to 7 minutes on the in-scope attack surface. False-positive volume on the previous SIEM rule set reduced by 92%, surfacing 230 high-confidence alerts per day in place of approximately 2,800. Existing investment in network controls preserved, with the AI layer operating above them rather than displacing them.
05
Retail Banking · Conversational AI
Conversational Banking Platform.
A retail bank required a unified conversational platform serving mobile, web and contact-centre channels, integrating a fine-tuned LLM, biometric authentication and a real-time fraud-detection layer under a single governance model.
Brief
The problem
Existing conversational tooling was channel-fragmented, lacked authentication continuity across surfaces, and could not absorb fraud signals in real time. The business required a single platform to consolidate the experience.
Engagement
What we delivered
An integrated conversational platform with a fine-tuned LLM for banking-domain interactions, biometric authentication integrated across mobile, web and IVR surfaces, and a real-time fraud-detection layer driving conversation control under unified governance.
Outcome
What it produced
Containment of routine customer enquiries lifted to 73% across covered intents, up from a 41% pre-deployment baseline. Average handling time on escalated cases reduced by 31% through agent-side co-piloting. Fraud-flag precision improved 2.4× against the prior rules-only baseline, with every model decision logged and explainable on demand.
06
E-Commerce · Vertical AI
Vertical AI Marketplace.
A cross-border e-commerce operator required an AI-enhanced marketplace covering personalised search, generative merchandising, dynamic pricing and seller co-pilots, deployable under multiple data-protection regimes.
Brief
The problem
A monolithic marketplace platform could not deliver the personalisation, merchandising and pricing intelligence the business required, and the client's multi-jurisdictional footprint imposed varying data-protection constraints that off-the-shelf AI services could not satisfy.
Engagement
What we delivered
An AI-enhanced marketplace architecture: personalised semantic search, generative product merchandising, dynamic pricing models, and natural-language seller co-pilots. Designed for cross-border deployment, with locality-aware data handling and configurable governance per jurisdiction.
Outcome
What it produced
Search-to-purchase conversion improved 22% on the catalogue tier where the new ranking stack was deployed. Seller onboarding time reduced from 12 days to under 4 through co-pilot-assisted listing generation. Deployment passed pre-launch data-protection review in both the EU and GCC jurisdictions in scope, with regional data residency enforced at the storage and inference tiers.
07
Healthcare · Document Intelligence ·
Clinical Document Intelligence.
A multi-hospital healthcare group required structured extraction from 1.2 million handwritten, scanned and hybrid medical records spanning fifteen years of clinical history, without disturbing existing EMR systems.
Brief
The problem
Patient records existed across paper archives, scanned PDFs and partially digitised EMR fields. Manual extraction was the bottleneck for both quality audits and population-health analytics.
Engagement
What we delivered
A multi-modal OCR pipeline combining vision-language models with clinical NER, ICD-10 and UMLS code mapping, and a human-in-the-loop validation console for low-confidence extractions. The whole thing runs inside the client's existing data perimeter.
Outcome
What it produced
Median extraction accuracy of 89% validated against clinician-reviewed gold standard, processing approximately eighteen thousand records per working day with full provenance for each extracted field.
08
Insurance · Claims Automation ·
Motor Claims Automation.
A regional general insurer required automation of first-notification-of-loss triage and damage assessment for motor claims, without removing the adjuster from genuinely complex losses.
Brief
The problem
Manual FNOL handling was slow and inconsistent. Straight-through processing rates were running below industry benchmarks, and adjuster time was being consumed by low-severity claims that did not require human judgement.
Engagement
What we delivered
Vision-based damage classification trained on a sector-licensed image corpus, an LLM-driven liability narrative drafter, and routing logic that preserves adjuster ownership of disputed and high-severity losses.
Outcome
What it produced
41% of motor claims processed end-to-end without adjuster review, average cycle time reduced by 2.8 working days, and adjuster capacity reallocated to the complex losses that drive loss-ratio outcomes.
09
Engineering · Knowledge Retrieval ·
Engineering Drawing Search.
A regional construction and engineering group held approximately 850,000 technical drawings, specifications and as-built documents accumulated over three decades, with no practical way to retrieve them by content.
Brief
The problem
Filename-and-folder retrieval forced engineers to consult colleagues with tenure rather than search a system. Median time-to-document was approximately eighteen minutes per query.
Engagement
What we delivered
Multimodal indexing combining text extraction with drawing-region recognition and engineering-domain embeddings. A search interface that accepts natural-language queries, drawing fragments and dimensional tolerances as input.
Outcome
What it produced
Median search-to-document time reduced from 18 minutes to 22 seconds across a controlled study of 240 engineer-initiated retrievals.
10
Energy · Predictive Maintenance ·
Offshore Turbine Forecasting.
An offshore energy operator required earlier warning of failure across a fleet of compression turbines on platforms where unplanned downtime is measured in millions per day.
Brief
The problem
Existing condition monitoring was reactive: alerts fired when thresholds were already exceeded, leaving the operations team to recover rather than prevent. Maintenance scheduling was time-based rather than condition-based.
Engagement
What we delivered
Time-series anomaly detection layered onto vibration-signature analysis, with LLM-summarised technician dispatch notes that translate model outputs into the operational language the maintenance team already uses.
Outcome
What it produced
73% of qualifying failures predicted more than seven days in advance during the first nine months of production operation, with documented Y1 downtime avoidance of approximately USD 3.8 million.
11
Government · Sovereign AI ·
Federal Policy Co-pilot.
A national government body required a working LLM environment to support internal policy drafting, regulatory analysis and inter-agency document workflows. The requirement was absolute data residency and no external inference of any kind.
Brief
The problem
Commercially-hosted foundation models were inadmissible because of data classification. Existing search across roughly 220,000 policy documents was effectively keyword-only, producing low-precision results.
Engagement
What we delivered
An air-gapped open-weight LLM deployment with retrieval-augmented generation over the entity's classified policy corpus. Document provenance, redaction-aware answer construction, and a complete audit trail for every generated response.
Outcome
What it produced
Sub-two-second response time for typical policy queries, with citation back to the source paragraph. 100% of inference, embedding and storage retained within country borders.
12
Telecommunications · Conversational AI ·
Bilingual Customer Service Agents.
A regional mobile network operator required tier-one customer service automation across voice and chat channels, in Arabic and English, with reliable handoff to human agents where the situation warranted.
Brief
The problem
Existing IVR and chatbot tooling could not handle the dialectal range of Gulf Arabic at acceptable accuracy, and offered no integration with the billing, network-status and entitlement systems support agents rely on.
Engagement
What we delivered
Bilingual conversational agents with native dialectal coverage, structured tool-use against billing, network and entitlement APIs, and explicit human handoff with full context transfer for issues outside agent capability.
Outcome
What it produced
38% of inbound contacts resolved by the agent without human intervention. Average handle time for human-handled contacts reduced by 47% due to context pre-loading.
13
Legal · Contract Intelligence ·
M&A Contract Review.
A tier-one regional law firm required automation of M&A due-diligence review so deal teams could focus partner time on judgement rather than on first-pass clause extraction across thousands of documents.
Brief
The problem
Due-diligence review consumed weeks of associate time per deal, and quality was variable across teams. Speed-to-red-flag was the binding constraint on deal velocity.
Engagement
What we delivered
Clause extraction with full explainability (every extraction is linked back to its source span), deviation detection against the firm's precedent library, and a partner-review interface that prioritises clauses by deal-impact severity.
Outcome
What it produced
Review throughput improved by a factor of 5.2 against the firm's prior baseline; 92% of clause classifications validated as correct on partner review.
14
Aviation · Operational Intelligence ·
Aircraft Maintenance Log Intelligence.
A regional carrier required analysis of approximately twelve years of mechanic notes, defect entries and rectification records across its narrow-body fleet to identify recurring fault patterns and improve mean time between failure.
Brief
The problem
Maintenance records were free-text, technical, abbreviation-heavy and not uniformly structured. Manual analysis could not scale across the fleet, and recurring snags were being treated as isolated incidents.
Engagement
What we delivered
An aviation-domain language model fine-tuned on MEL/CDL terminology and the carrier's own engineering nomenclature, paired with retrieval over manufacturer service bulletins and safety advisories for each recurring pattern identified.
Outcome
What it produced
31% reduction in repeat snag occurrences across covered ATA chapters during the first two operating quarters, with mean time between recurring faults extended by approximately five weeks.
The engagements summarised above are representative rather than exhaustive. Specific case detail, references and demonstrations are available under non-disclosure. Direct enquiries to [email protected].