Results thatbecome referencesin execution.

Microsoft Fabric

ISVMicrosoft Fabric

Fabric Modernisation to support Customer Churn Modelling

Implemented a unified data platform on Microsoft Fabric to enable customer churn modelling and provide a scalable foundation for future AI and advanced analytics use cases.

Challenge: Data was fragmented across multiple silos, limiting data quality, governance, and the organisation’s ability to perform reliable analytics or develop predictive models and AI use cases.
Approach: Rationalised the existing data model to remove redundancy and improve consistency. Re-ingested trusted, validated data sources into Microsoft Fabric. Rebuilt data pipelines using a Medallion architecture (Bronze, Silver, Gold). Established clear data lineage, governance, and documentation throughout the platform.
Outcome: Delivered a Fabric-native, governed data architecture with a clean semantic model and OneLake connectivity, enabling reliable churn analysis today and providing a robust, AI-ready foundation for future advanced analytics initiatives.

ISV
Microsoft Fabric
Modern Data Architecture
AI Ready
Medallion Architecture
Governance
OneLake
Analytics

Azure AI

NonProfitAzure AI

AI-Driven Modernisation of Legacy Content Matching

Delivered a cloud-native solution using RAG and semantic search on Microsoft Azure to modernise a legacy matching engine and improve accuracy and scalability.

Challenge: A legacy, on-premises matching solution struggled with noisy, multilingual data, limited scalability, and heavy manual intervention, slowing operations and hindering cloud transformation.
Approach: Implemented an Azure-based RAG architecture with semantic indexing and vector search, and evaluated AI-driven matching against the existing search baseline.
Outcome: Demonstrated more accurate and explainable matching with reduced manual effort, validating a scalable, AI-ready architecture for future production use.

NonProfit
Azure AI
RAG
Semantic Search
Vector Search
Legacy Modernisation
AI Ready
Azure

FinOps

ISVFinOps

FinOps Optimisation for a SaaS ISV

Delivered a FinOps optimisation initiative to automatically reduce cloud spend and improve financial governance without impacting performance or engineering velocity.

Challenge: A growing ISV faced rising cloud costs driven by dynamic workloads and manual reservation management, with limited visibility into savings opportunities and inconsistent cost accountability across teams.
Approach: Implemented tooling to automate commitment-based discounts, continuously optimise Reserved Instances and Savings Plans, and provide real-time FinOps insights aligned to engineering and finance stakeholders.
Outcome: Achieved sustained cloud cost savings, improved forecast accuracy, and established a scalable FinOps operating model allowing teams to focus on innovation while the tooling handled optimisation in the background.

ISV
FinOps
Optimisation
Cost Optimisation
Savings Plans
Reserved Instances
Governance

Azure AI

ISVAzure AI

Azure AI–Powered Document Entity Extraction

Designed and delivered services on Microsoft Azure to validate automated extraction of structured data from unstructured business documents, establishing a scalable foundation for intelligent document processing and downstream automation.

Challenge: Critical business information was locked in unstructured documents, requiring manual processing that was time-consuming, error-prone, and difficult to scale. The organisation needed to validate whether AI-driven extraction could reliably identify a fixed set of predefined entities and integrate into existing cloud workflows.
Approach: Defined the target entities and document scope, then built an Azure-native extraction pipeline combining OCR, rules, and language models. Deployed and tested the end-to-end workflow within the client’s Azure environment, producing structured outputs and operational documentation.
Outcome: Successfully delivered and deployed, working environment that consistently extracted the required entities into structured outputs. Confirmed technical feasibility and architectural viability for scaling to production, while providing a clear roadmap and operational guidance to support future expansion of intelligent document processing and AI-driven automation use cases.

ISV
Azure AI
Document Intelligence
Governance

Azure AI

ISVAzure AI

Azure AI–Powered RFI Response Automation

Designed and delivered an Azure AI solution for an internal AI agent that accelerates and standardises responses to complex domain specific RFIs, establishing a secure and scalable foundation for AI-assisted knowledge workflows.

Challenge: The organisation faced increasing volumes and complexity of RFIs, requiring subject-matter experts to manually search distributed documentation and repeatedly re-author similar responses. This led to long turnaround times, inconsistent answers, and limited reuse of validated knowledge, while operating under strict governance and regulatory constraints.
Approach: Conducted a structured assessment of the existing data and knowledge landscape, then implemented an Azure-native RFI Management Agent using Azure AI Foundry, Azure OpenAI, and Azure AI Search. The solution enabled automated ingestion of RFI documents, AI-assisted answer suggestions using retrieval augmented generation, and a human-in-the-loop workflow for review, collaboration, approval, and export - all deployed within the customer’s Azure tenant and aligned to Microsoft’s Well-Architected Framework.
Outcome: Successfully demonstrated measurable productivity and quality improvements in RFI response workflows. Validated the technical feasibility, security, and scalability of Azure AI for regulated environments, and provided a clear architectural and operational roadmap to support future AI-enabled use cases.

Azure AI
ISV
RFI Automation
Governance

Azure AI

ISVAzure AI

AI Voicebot Platform for Contact Centre Training and Performance Analysis

Implemented an Azure-native AI voicebot platform to simulate customer interactions, automatically analyse agent conversations, and deliver actionable coaching insights for contact centre training and quality assurance.

Challenge: Contact centre training and quality monitoring relied heavily on manual review of recorded calls, limiting scalability and consistency in evaluating agent performance. Organisations needed a solution capable of simulating realistic customer scenarios, capturing voice interactions, and automatically assessing agent behaviour across key competencies such as empathy, compliance, communication, and emotional intelligence. At the same time, the platform needed to support secure integration with existing systems and provide a scalable architecture for future AI-powered capabilities.
Approach: Designed and implemented a cloud-based AI solution leveraging Microsoft Azure and modern AI services to enable voice interaction simulation, conversation analysis, and automated feedback generation. The platform captures and processes voice interactions, converts conversations into structured data, and applies advanced AI models to evaluate performance against defined training criteria. Results are securely stored and made available through APIs and reporting tools, enabling integration with existing applications and dashboards. The implementation followed Microsoft’s Well-Architected Framework, ensuring strong governance, security, and scalability.
Outcome: Delivered a scalable AI-powered platform capable of simulating customer interactions, automatically analysing conversations, and providing structured feedback for agent training and performance improvement. The solution enables organisations to standardise contact centre coaching, accelerate training cycles, and gain deeper insights into customer interactions. The architecture also provides a secure and extensible foundation for future AI-driven CX capabilities, including advanced conversation analytics and intelligent automation.

Azure AI
ISV
Voice AI
Contact Centre
Customer Experience

Azure AI

Digital MarketplaceAzure AI

AI-Powered Search and Enrichment Platform for Digital Marketplace Optimisation

Implemented an Azure-native AI search platform to improve product discovery through hybrid search, automated enrichment, and real-time indexing, enabling a more relevant user experience and scalable marketplace operations.

Challenge: The organisation relied on manual and batch-based processes to ingest and enrich marketplace listings, resulting in delays, inconsistent metadata quality, and limited search relevance. Existing search capabilities lacked real-time updates, scalable ranking, and the ability to support advanced filtering and faceting, making it harder for users to find relevant results efficiently. There was also a need for a governed, production-ready architecture that could integrate securely with the website, automate enrichment workflows, and provide operational visibility across performance, cost, and system health.
Approach: Designed and implemented a cloud-native AI search solution using Microsoft Azure and modern AI services to combine hybrid retrieval, automated enrichment, and event-driven indexing. The platform processes listing and image updates through triggers and queues, applies AI models to enrich and validate metadata, and uses confidence scoring, fallback logic, and conflict resolution to improve consistency and control. The solution was integrated with the website through search APIs and included support for filtering, faceting, pagination, and analytics. CI/CD pipelines, Infrastructure as Code, monitoring, alerting, and governance controls were also established to enable secure, repeatable, and scalable production operations.
Outcome: Delivered a production-ready AI-powered search platform that improved search relevance, data freshness, and operational efficiency across a digital marketplace environment. Automated ingestion and enrichment reduced manual effort while increasing consistency and responsiveness as listings changed. The solution enabled better product discoverability, stronger operational governance, and a secure foundation for future AI-driven capabilities such as intelligent recommendations, advanced analytics, and further search optimisation.

Azure AI
Search
Digital Marketplace
AI Enrichment
Customer Experience

Azure AI

Digital Sports MarketplaceAzure AI

AI-Powered Event Recommendation Platform for Personalised Experiences

Implementation of an Azure-native AI recommendation platform to improve event discovery by leveraging user interaction data, machine learning models, and data pipelines, enabling personalised athlete experiences and increased event participation.

Challenge: The organisation operated a large-scale digital sports marketplace serving over a million users and thousands of events annually, but lacked an effective recommendation capability. User interaction data such as searches, clicks, and views was not systematically captured or centralised, limiting the ability to deliver behaviour-driven recommendations. Data was fragmented across operational systems and not structured for analytics or AI use cases. Existing recommendation experiments were not production-ready, and there was no scalable or governed architecture to support personalisation. Additional constraints around cost efficiency, limited in-house data science capacity, and GDPR compliance increased the complexity of delivering a robust AI-driven solution.
Approach: Designed and validated a cloud-native recommendation architecture on Microsoft Azure, focused on combining interaction data with operational event data. A dedicated analytics layer was introduced using Azure SQL to model event-interaction relationships, supported by ingestion and transformation pipelines through Azure Data Factory and event-stream processing. Azure Machine Learning was used to build an initial ranking model alongside heuristic scoring to generate ranked event recommendations. Azure OpenAI capabilities were incorporated to support enrichment and future explainability use cases. The solution also included a containerised recommendation API, CI/CD deployment workflows, secure secret management, and an architecture aligned to Azure Well-Architected principles for scalability, security, reliability, and operational governance.
Outcome: Delivered a validated blueprint for an AI-powered recommendation platform that improves event discoverability and personalisation across a digital sports environment. The solution established a scalable and cost-efficient foundation for using real user behaviour to drive more relevant recommendations and improve engagement. It also created a governed architecture for future expansion across web, email, and mobile channels, while improving data readiness, operational visibility, and the organisation’s ability to evolve its AI-driven personalisation strategy.

Azure AI
Recommendations
Personalisation
Digital Marketplace
Customer Experience

Azure AI

ManufacturingAzure AI

Azure AI Multi-Agent for Industrial IoT Operations

Designed and implemented an Azure-native multi-agent platform to enable intelligent interaction with industrial IoT systems, supporting real-time insights, operational decision-making, and scalable AI-driven automation.

Challenge: The organisation’s existing IIoT platform and AI implementation faced significant architectural and integration challenges, including undocumented and inconsistent APIs, lack of standardised payloads, and unclear endpoint behaviours. The existing application did not align with Azure-native patterns, lacked containerisation, and did not support critical capabilities such as token persistence, tenant context management, or multi-tenant readiness. These limitations made it difficult to reliably integrate AI-driven agents, scale the solution, and ensure secure, maintainable operations across environments.
Approach: Designed and delivered a cloud-native solution leveraging Microsoft Azure and Azure AI Foundry to modernise and refactor the existing agent into a scalable, containerised multi-agent platform. The approach included a structured API discovery phase to analyse and normalise backend services, development of MCP-based connector prototypes, and implementation of agent orchestration capable of reasoning, invoking tools, and handling tenant-specific context. The solution incorporated Azure-native services for identity, observability, and DevOps, with CI/CD pipelines, monitoring, and telemetry. The architecture followed Microsoft’s Well-Architected Framework, ensuring security, governance, and scalability while enabling future extensibility.
Outcome: Delivered a validated Azure-native multi-agent platform capable of interacting with industrial systems through structured APIs, executing intelligent workflows, and maintaining secure, context-aware interactions. The solution established a scalable and maintainable foundation aligned with Azure AI Foundry standards, enabling improved operational visibility, automation of decision processes, and readiness for multi-tenant expansion. The solution also provided documented architecture, reusable connectors, and a clear roadmap for future functionality deployment and advanced AI-driven capabilities.

Azure AI
Manufacturing
IIoT
Multi-Agent Systems

Azure AI

HealthcareAzure AI

AI-Powered Medical Document Processing and Workflow Automation

Implemented an Azure-native AI platform to automate medical document processing, reducing examination wait times, improving operational efficiency, and enabling scalable healthcare data management.

Challenge: The organisation relied heavily on manual data entry for processing medical orders, resulting in long wait times, operational inefficiencies, and increased risk of human error. With thousands of medical professionals generating high volumes of documentation, the system struggled to scale. A significant portion of data remained on-premises, limiting accessibility and integration with modern digital services. The organisation needed a solution to automate document processing, reduce latency, and modernise its data platform while maintaining accuracy and compliance.
Approach: Designed and implemented a cloud-based solution leveraging Microsoft Azure and AI services, including OCR and computer vision capabilities to automatically extract and process medical data from documents. The platform introduced high-performance data processing pipelines, reducing processing times from minutes to seconds. Azure-native services were used to enable scalable infrastructure, cost optimisation, and secure data governance. CI/CD pipelines and DevOps practices were implemented to support continuous delivery, while automated workflows streamlined document handling and reduced manual intervention.
Outcome: Delivered a highly efficient AI-powered platform that significantly reduced document processing times and improved patient throughput. The solution enabled the organisation to move from manual, time-intensive processes to near real-time data processing, drastically reducing wait times. Staff productivity improved by minimising administrative workload, allowing greater focus on patient care. The platform also established a scalable and governed data foundation, supporting future growth, advanced analytics, and continued digital transformation in healthcare operations.

Azure AI
Healthcare
Document AI
Computer Vision
Operational Efficiency

Azure AI

HealthcareAzure AI

Real-Time AI Monitoring Platform for Neonatal Care

Implemented an Azure-native real-time monitoring and analytics platform to process IoT healthcare data, enabling faster clinical insights, improved patient outcomes, and scalable data-driven decision-making.

Challenge: The organisation relied on an on-premises application to monitor high-risk newborns, which introduced delays in data processing and limited real-time visibility. Monitoring data from IoT devices was not processed quickly enough, leading to delays in identifying critical events. The system also faced scalability constraints, infrastructure limitations, and high operational overhead, making it difficult to expand across multiple hospitals and support growing data volumes.
Approach: Designed and implemented a cloud-native solution leveraging Microsoft Azure and AI services to modernise the existing monitoring platform. The approach included migration from on-premises infrastructure to Azure, enabling real-time data ingestion and processing from IoT devices. AI and analytics capabilities were applied to process and analyse monitoring data, while Azure data services provided scalable, secure, and highly available infrastructure. Automated pipelines and modern DevOps practices ensured efficient deployment, monitoring, and continuous improvement of the platform.
Outcome: Delivered a real-time monitoring platform capable of significantly reducing delays in detecting critical patient events, improving responsiveness in neonatal care. The solution enabled near real-time data processing, enhanced clinical decision-making, and reduced manual workload for healthcare staff. The platform now supports multiple hospitals at scale, provides governed datasets for analytics, and establishes a robust foundation for future AI-driven healthcare innovation and expansion.

Azure AI
Healthcare
IoT
Real-Time Analytics
Patient Monitoring

Azure AI

HealthcareAzure AI

AI-Powered Fraud Detection for Medical Claims Processing

Implemented an Azure-native AI solution to detect fraudulent reimbursement claims by identifying duplicate and manipulated medical images, improving accuracy, efficiency, and trust in healthcare operations.

Challenge: The organisation faced increasing levels of fraud in reimbursement claims, particularly through the submission of duplicate or modified radiological images. With high volumes of claims and manual data entry processes, it was difficult to accurately detect similarities across documents. This resulted in financial losses, operational inefficiencies, and limited ability to scale fraud detection efforts effectively.
Approach: Designed and developed an AI-driven solution leveraging Microsoft Azure to analyse and compare medical images for similarity detection. The platform used advanced image processing and machine learning techniques to identify duplicate and altered images, including cropping, colour changes, and contrast adjustments. A centralized system was implemented to process large volumes of claims data, supported by scalable Azure infrastructure, enabling automated detection and streamlined workflows for fraud analysis.
Outcome: Delivered a highly effective fraud detection platform capable of identifying duplicate and manipulated medical images with high accuracy. The solution significantly reduced fraudulent claims, improved operational efficiency, and strengthened trust across stakeholders. With a scalable architecture, the platform supports increasing data volumes and evolving fraud patterns, providing a robust foundation for ongoing innovation in healthcare fraud prevention.

Azure AI
Healthcare
Fraud Detection
Computer Vision
Machine Learning

Azure AI

HealthcareAzure AI

AI-Driven Hand Hygiene Monitoring for Infection Prevention

Implemented an Azure-native AI solution to monitor and improve hand hygiene compliance in healthcare settings, reducing infection risks and enhancing patient safety through real-time feedback and analytics.

Challenge: Healthcare providers faced low adherence to hand hygiene protocols, with compliance rates below 40%, contributing to high rates of hospital-acquired infections and increased patient risk. Traditional monitoring methods were manual, inconsistent, and unable to provide real-time feedback. This resulted in limited visibility into compliance levels and difficulty in enforcing hygiene standards across clinical environments.
Approach: Designed and implemented an AI-powered solution leveraging Microsoft Azure and machine learning to monitor hand hygiene practices in real time. The system used computer vision and high-resolution imaging to detect and analyse hygiene actions against established standards. Real-time feedback mechanisms were introduced to guide correct behaviour, while Azure Machine Learning enabled continuous model improvement. The platform was built with scalable cloud infrastructure, ensuring adaptability across different healthcare environments and conditions.
Outcome: Delivered a significant improvement in hand hygiene compliance, increasing adherence rates by over 60% and reducing hospital-acquired infections. The solution enhanced patient safety and improved overall service quality by providing consistent, real-time monitoring and feedback. With a scalable and intelligent architecture, the platform enables ongoing optimisation and supports broader infection prevention strategies across healthcare organisations.

Azure AI
Healthcare
Computer Vision
Patient Safety
Infection Prevention

Azure AI

EducationAzure AI

AI-Powered Learning Assistant for Digital Education Platforms

Implemented an Azure-native AI chatbot platform to enhance student learning experiences through personalised guidance, natural language interaction, and intelligent content discovery.

Challenge: The organisation managed a large-scale digital education platform serving millions of users and offering hundreds of courses. Educational content was distributed across multiple formats, including PDFs and videos, making it difficult for students to quickly find relevant information. Student questions often required instructor support, limiting scalability and slowing down response times. Additionally, content needed to be organised and delivered dynamically based on each learner’s course and context.
Approach: Designed and implemented an AI-powered chatbot solution integrated into the existing platform using Microsoft Azure. The solution leveraged Azure AI Search and OpenAI capabilities to index and retrieve educational content, combined with Azure Speech Services for enhanced interaction. The platform enabled natural language querying, personalised tutoring, and contextual responses based on course-specific data. A scalable API layer and static web application architecture ensured performance, reliability, and ease of integration, while enabling continuous testing and improvement of response accuracy.
Outcome: Delivered an intelligent learning assistant that significantly improved content accessibility and student engagement. The platform enabled natural language interaction, allowing students to quickly find answers and better understand course material. Automated content indexing and multi-format support, including transcription of audio and video, enhanced discoverability. The solution reduced reliance on manual support, improved learning efficiency, and provided a scalable foundation for future AI-driven education capabilities.

Azure AI
Education
Chatbot
AI Search
Personalisation

Azure AI

EducationAzure AI

AI-Powered Video Processing and Search Platform for Education

Implemented an Azure-native AI platform to process, transcribe, and analyse large volumes of educational video content, enabling intelligent search, improved accessibility, and scalable digital learning experiences.

Challenge: The organisation needed to support thousands of schools, educators, and millions of students while managing a rapidly growing library of video content exceeding terabytes in scale. Educational materials were not easily searchable, limiting accessibility and reuse. Manual processes for transcription and indexing were inefficient, making it difficult to deliver content in a timely and structured way across diverse learning environments.
Approach: Designed and implemented a cloud-based AI platform leveraging Microsoft Azure, combining computer vision, machine learning, and speech-to-text capabilities to automate video ingestion, transcription, and analysis. The solution enabled large-scale storage and processing of audiovisual content, while data visualisation layers provided insights into content usage and engagement. Azure-native services ensured scalability, performance, and efficient management of large datasets, supporting multilingual access and intelligent content discovery.
Outcome: Delivered a scalable video analytics platform that significantly improved content accessibility and engagement across the education ecosystem. The solution enabled intelligent search through transcribed and indexed content, enhanced inclusivity with multilingual support, and optimised storage and operational costs. It also provided actionable insights into student behaviour and content usage, supporting digital transformation across the public education network.

Azure AI
Education
Video Analytics
Speech to Text
Digital Transformation

Azure AI

Financial ServicesAzure AI

AI-Powered Chargeback Processing and Document Automation

Implemented an Azure-native AI platform to automate chargeback document processing, improving accuracy, reducing operational effort, and enabling faster, compliant financial operations.

Challenge: The organisation faced low operational efficiency due to manual document analysis and fragmented systems across multiple tools and platforms. Human errors in processing chargeback documents led to inconsistencies and compliance risks with complex card scheme and issuer rules. Limited data traceability and disconnected integrations made it difficult to track transactions and decisions, while strict processing deadlines created significant operational pressure.
Approach: Designed and implemented an AI-driven solution leveraging Microsoft Azure to automate chargeback processing using OCR and intelligent document understanding. The platform extracts and interprets data based on issuer and card scheme rules, integrating seamlessly with existing enterprise systems and external APIs. It connects to multiple data sources, including cloud storage and databases, and centralises workflows into a unified interface. Automated version control and document management ensure consistency, while providing analysts with a streamlined environment for decision-making.
Outcome: Delivered a highly efficient and scalable chargeback processing platform that increased processing speed by over 90% and significantly reduced human error. The solution improved accuracy, ensured compliance with strict deadlines, and reduced operational costs. Centralised data and workflows enhanced traceability and control, while providing a better experience for both internal teams and customers. The platform establishes a strong foundation for continued automation and optimisation of financial operations.

Azure AI
Financial Services
Document AI
Automation
Operational Efficiency

Azure AI

LogisticsAzure AI

AI-Powered Predictive Forecasting for Container Logistics

Implemented an AI-driven platform leveraging Dell infrastructure and NVIDIA AI capabilities to forecast container arrivals and departures, improving operational predictability and enabling data-driven decision-making across logistics operations.

Challenge: The organisation relied on manual analysis to forecast container movements, limiting accuracy and responsiveness in a highly complex logistics environment. Predicting arrivals and departures within short timeframes required processing multiple operational indicators, which traditional methods struggled to handle efficiently. This resulted in limited visibility, reduced ability to anticipate bottlenecks, and inefficiencies in managing physical space and resources.
Approach: Designed and implemented a machine learning solution built on Dell AI Factory infrastructure with NVIDIA AI Enterprise, enabling high-performance data processing and model training. The platform applied advanced algorithms such as regression models, decision trees, and neural networks to identify movement patterns and generate accurate forecasts. By combining scalable AI infrastructure with robust data modelling, the solution automated processing of key logistics indicators and enabled integration with additional data sources over time.
Outcome: Delivered a predictive analytics platform that significantly improved operational predictability, allowing the organisation to anticipate bottlenecks and optimise terminal operations. The solution enhanced forecasting accuracy, improved resource planning, and automated critical data workflows. A scalable and extensible architecture was established, enabling future integration of external variables such as weather conditions and shipping routes, while ensuring long-term sustainability through knowledge transfer and documentation.

Azure AI
Logistics
Predictive Analytics
Machine Learning
NVIDIA
Dell

Azure AI

LogisticsAzure AI

AI-Powered Container Inspection and Anomaly Detection

Implemented an AI-driven computer vision platform leveraging Dell infrastructure and NVIDIA AI to automate container inspection, improving security, operational efficiency, and decision-making in maritime logistics.

Challenge: The organisation needed to automate the detection of illicit objects and anomalies within maritime containers using high-resolution X-ray imagery. Manual inspection processes were time-consuming, error-prone, and difficult to scale. Identifying empty, full, or suspicious containers required analysing complex image data under operational pressure, with the added need to detect patterns that could indicate the presence of prohibited items such as drugs or weapons.
Approach: Designed and implemented a specialised AI model built on Dell AI Factory infrastructure with NVIDIA AI Enterprise, enabling high-performance processing of large-scale X-ray image datasets. The solution applied advanced computer vision techniques to analyse high-resolution images and classify containers using a multi-state logic model (e.g., empty, full, requires inspection). Training leveraged labelled datasets to improve accuracy across container types, while batch processing pipelines generated detailed inspection insights to support operational teams.
Outcome: Delivered an intelligent inspection platform that significantly enhanced security by automatically identifying anomalies and irregularities in container scans. The solution improved operational agility through automated reporting and streamlined workflows, enabling faster and more informed decision-making. Full knowledge transfer and delivery of source code ensured long-term autonomy, giving the organisation complete control over the solution and its future evolution.

Azure AI
Logistics
Computer Vision
Security
NVIDIA
Dell

Azure AI

Public SectorAzure AI

AI-Powered Document Analysis and Case Processing for Regulatory Agencies

Implemented an Azure-native AI platform to automate large-scale document analysis and case processing, significantly reducing processing time and improving regulatory efficiency.

Challenge: The organisation faced a massive backlog of hundreds of thousands of cases, representing hundreds of millions of pages requiring manual review. Traditional processes required full reading of case files, manual identification of key elements, and individual drafting of opinions and referrals. This approach was highly time- and resource-intensive, creating operational bottlenecks and making it impractical to process the backlog within a reasonable timeframe.
Approach: Designed and implemented a cloud-native solution leveraging Microsoft Azure, including Azure OpenAI, Azure AI Search, and Azure Document Intelligence. The platform utilised large language models, vector search, and computer vision to automate the analysis of complex documents, including technical reports and structured data. Azure Functions orchestrated workflows to process documents, interpret content, apply regulatory logic, and generate structured outputs such as referrals and recommendations. A governed Azure Landing Zone ensured scalability, security, and compliance.
Outcome: Delivered a highly efficient AI-driven platform that processed large volumes of cases with high accuracy, significantly reducing processing time from years to months. The solution improved operational efficiency, reduced bottlenecks, and enabled faster regulatory decision-making. It also increased internal satisfaction by reducing manual workload, while establishing a scalable and governed foundation for future AI-driven public sector innovation.

Azure AI
Public Sector
Document AI
Automation
Regulatory Compliance

Azure AI

ManufacturingAzure AI

AI-Powered Quality Control for High-Volume Manufacturing

Implemented an AI-driven quality control platform using IoT and machine learning to detect defects in real time, improving production efficiency and product quality at scale.

Challenge: The organisation needed to implement real-time defect detection within a high-volume manufacturing environment, specifically identifying issues in raw materials used in production. Achieving a minimum accuracy threshold while operating with low-cost hardware and on-premises constraints added complexity. Manual inspection processes were inefficient and unable to scale with production volumes, impacting overall product quality and operational performance.
Approach: Designed and deployed an AI-powered solution integrating IoT sensors and machine learning models to monitor production lines in real time. The platform enabled automated data collection and processing, transforming raw production data into actionable insights. Machine learning models were trained to detect defects with high accuracy, while the architecture supported efficient data pipelines and on-premises deployment requirements. The solution combined edge processing with scalable analytics to ensure responsiveness and reliability.
Outcome: Delivered a high-performance quality control solution that significantly improved production efficiency and final product quality. The platform achieved over 94% accuracy and 98% precision in defect detection, enabling more consistent manufacturing outcomes. Real-time monitoring reduced reliance on manual inspection, optimised production processes, and provided a scalable foundation for further industrial AI adoption.

Azure AI
Manufacturing
IoT
Computer Vision
Quality Control

Azure AI

ManufacturingAzure AI

AI-Driven Production Monitoring and Maintenance Optimisation

Implemented an Azure-native IoT and AI platform to monitor high-volume production in real time, optimise maintenance processes, and improve operational efficiency across continuous manufacturing environments.

Challenge: The organisation operated a large-scale manufacturing environment with continuous 24/7 production across multiple shifts and thousands of employees. High throughput on production lines required real-time monitoring to detect failures and inefficiencies. Existing processes lacked visibility into operations, making it difficult to analyse performance, predict issues, and optimise maintenance activities, while handling large volumes of streaming production data.
Approach: Designed and implemented an AI-powered solution integrating IoT sensors and real-time data processing on Microsoft Azure. The platform enabled continuous data collection from production lines, feeding into machine learning models for analysis and optimisation. Data pipelines transformed and processed high-volume telemetry, while dashboards provided real-time visibility into operations. The solution supported predictive insights, enabling proactive maintenance and improved decision-making across shifts.
Outcome: Delivered a scalable monitoring and analytics platform that improved operational efficiency and reduced maintenance costs by over 35%. The solution provided full visibility across all production shifts, enabling faster identification of issues and optimisation of processes. Enhanced data-driven insights improved product quality and reduced downtime, while significant cost savings were achieved through more efficient maintenance and operational practices.

Azure AI
Manufacturing
IoT
Predictive Maintenance
Operational Efficiency

Azure AI

ResearchAzure AI

AI-Powered Research Data Management and Analytics Platform

Implemented an Azure-native data platform to organise, index, and analyse millions of research documents, enabling faster discovery, improved insights, and scalable data-driven innovation.

Challenge: The organisation managed tens of millions of research documents across multiple systems, formats, and languages. Data was fragmented across several interfaces, making it difficult to access, search, and analyse information efficiently. Manual processes and lack of integration limited productivity, while the growing volume of digitised documents created additional complexity in managing and extracting value from the data.
Approach: Designed and implemented a scalable data platform leveraging Microsoft Azure, incorporating ETL pipelines and advanced indexing techniques to organise large volumes of structured and unstructured data. A web-based application was developed to enable efficient access, search, and analysis of research content. The solution integrated multiple systems into a unified architecture, while advanced analytics capabilities provided deeper insights and reporting.
Outcome: Delivered a unified and scalable platform that significantly improved access to millions of research documents and reduced the time required to find relevant information. The solution enhanced search accuracy and enabled more effective analysis, supporting the development of new insights and data-driven solutions. Improved efficiency and data quality empowered users to make faster, more informed decisions across research activities.

Azure AI
Research
Data Platform
Analytics
Search

Azure AI

EnergyAzure AI

AI-Powered Aerial Inspection and Video Analytics Platform

Implemented an Azure-native AI platform to automate aerial inspections using video analytics, reducing manual effort, accelerating fault detection, and improving operational efficiency.

Challenge: The organisation relied on complex aerial inspections using helicopters, generating terabytes of video data per cycle. Manual analysis of hundreds of hours of footage was time-consuming, resource-intensive, and prone to delays in identifying faults. The growing volume of inspection data made it increasingly difficult to scale operations and maintain timely and accurate analysis.
Approach: Designed and implemented a cloud-based solution leveraging Microsoft Azure, incorporating a Data Lake for large-scale data management and computer vision for automated video analysis. The platform enabled automated ingestion and processing of video data, combined with audio-to-text capabilities and machine learning models for enhanced interpretation. Interactive dashboards provided real-time insights and visualisation, enabling faster decision-making and improved operational oversight.
Outcome: Delivered a highly efficient inspection platform that reduced manual analysis effort by hundreds of hours per inspector and decreased total inspection time by over 95%. The solution enabled rapid fault identification through advanced analytics, improving responsiveness and operational reliability. A scalable architecture was established to support future expansion, ensuring long-term sustainability and continuous improvement in inspection processes.

Azure AI
Energy
Computer Vision
Video Analytics
Operational Efficiency

Azure AI

Professional ServicesAzure AI

AI-Powered PPE Compliance Monitoring for Industrial Operations

Implemented an Azure-native AI solution using computer vision and IoT to monitor PPE compliance in real time, improving safety, operational visibility, and regulatory adherence across large-scale operations.

Challenge: The organisation operated thousands of sites with tens of thousands of employees, requiring strict adherence to PPE (Personal Protective Equipment) standards. Ensuring compliance across diverse environments and geographies was difficult to manage manually. Limited visibility into real-time behaviour created risks related to safety, regulatory compliance, and operational consistency.
Approach: Designed and implemented a cloud-based solution leveraging Microsoft Azure, combining Data Lake architecture for large-scale data management with computer vision to monitor PPE usage in real time. Machine learning models were applied to analyse compliance patterns, while dashboards and alerting systems provided real-time visibility and notifications. The platform enabled scalable monitoring and standardised reporting across all locations.
Outcome: Delivered a comprehensive monitoring solution that achieved full visibility into PPE compliance across operations. Real-time alerts enabled immediate corrective actions, improving safety outcomes and operational control. Advanced analytics supported process optimisation, while the scalable architecture allows replication across new environments and continued expansion.

Azure AI
Computer Vision
Workplace Safety
IoT
Compliance

Azure AI

Real EstateAzure AI

AI-Powered Data Submission and Analytics Platform for Real Estate Investment

Implemented an Azure-native data platform to streamline data submission, validation, and analysis for real estate investment reporting, improving data quality, efficiency, and user experience.

Challenge: The organisation managed large volumes of real estate investment data submitted periodically by institutional members. The existing process relied heavily on manual data submission, validation, and analysis, creating inefficiencies, delays, and inconsistencies. Lack of standardisation and fragmented communication channels made it difficult to ensure data quality, while increasing operational workload and limiting scalability.
Approach: Designed and implemented a centralized web-based platform leveraging Microsoft Azure to automate data submission and validation processes. The solution introduced structured upload interfaces with validation rules applied in real time, ensuring data consistency and accuracy. Automated data consolidation enabled generation of reports, dashboards, and analytics. A chatbot powered by AI was integrated to assist users with queries related to processes, templates, and data submission, improving usability and reducing support overhead.
Outcome: Delivered a scalable and efficient data management platform that significantly improved data quality and reduced manual effort. Real-time validation and feedback enhanced user experience and reduced submission errors, while automated consolidation streamlined reporting and analytics. The solution enabled faster access to insights, improved operational efficiency, and provided a strong foundation for advanced analytics and future AI-driven capabilities.

Azure AI
Real Estate
Data Platform
Analytics
Automation

Microsoft Fabric

EnergyMicrosoft Fabric

Unified Data Warehouse for Energy Operations

Implemented a Microsoft Fabric-powered data platform to unify operational data and deliver real-time insights across energy operations.

Challenge: Siloed systems and inconsistent integrations made it difficult to track workforce performance and operational metrics in real time, limiting visibility and slowing decision-making.
Approach: Designed a modern data platform using Microsoft Fabric, integrating multiple data sources into a governed lakehouse architecture. Automated pipelines and Power BI dashboards enabled real-time analytics and self-service reporting.
Outcome: Enabled real-time operational visibility, improved data quality and trust, accelerated reporting, and established a scalable foundation for future AI and analytics initiatives.

Microsoft Fabric
Data Platform
Energy
Analytics

Azure AI

ConstructionAzure AI

AI-Powered Multilingual Communication Platform

Delivered an AI-driven translation platform to enable seamless communication across global construction teams.

Challenge: Language barriers across geographically distributed teams created inefficiencies, miscommunication, and delays in project delivery.
Approach: Developed a centralised web application using Azure AI translation services and Copilot integration to provide context-aware multilingual document handling and secure collaboration.
Outcome: Improved communication accuracy, reduced manual translation effort, and enabled faster, more effective collaboration across global teams.

Azure AI
Translation
Copilot
Construction

AI Optimisation

RetailAzure AI

AI-Driven Pricing and Inventory Optimisation

Implemented AI models to optimise pricing strategies and inventory forecasting across retail operations.

Challenge: High stock variability, frequent stockouts, and static pricing strategies negatively impacted sales, efficiency, and customer satisfaction.
Approach: Built machine learning models for demand forecasting, price elasticity, and dynamic pricing, integrated with real-time data pipelines for continuous optimisation.
Outcome: Reduced stockouts, improved inventory stability, increased pricing agility, and enhanced overall operational efficiency.

Retail
AI
Forecasting
Pricing

Microsoft Fabric

Retail & DistributionMicrosoft Fabric

Modern Analytics Platform for Reporting

Modernised legacy reporting systems with a cloud-native analytics platform using Microsoft Fabric.

Challenge: Legacy systems caused slow reporting, fragmented data, and limited flexibility for evolving business needs.
Approach: Implemented a Fabric-based lakehouse with automated data pipelines, unified analytics workspace, and integrated Power BI dashboards.
Outcome: Accelerated report generation, unified data access, enabled self-service analytics, and delivered a scalable, future-ready platform.

Microsoft Fabric
Analytics
BI
Modernisation

Azure

Financial ServicesAzure

Cloud Data Platform Modernisation

Transformed legacy financial data systems into a scalable, cloud-native platform.

Security AI

Financial ServicesAzure AI

AI-Driven Fraud and Security Detection

Unified fraud detection and physical security monitoring using AI and real-time analytics.

Challenge: Traditional rule-based systems struggled to detect complex fraud patterns and unify digital and physical security insights.
Approach: Implemented machine learning anomaly detection, facial recognition, and real-time data pipelines to monitor transactions and security events.
Outcome: Reduced fraud detection time by 50%, achieved high accuracy, and improved overall security operations.

Security
Fraud Detection
AI
Computer Vision

Healthcare AI

HealthcareAzure AI

AI-Powered Medical Order Processing

Automated the processing of medical examination orders using AI and OCR technologies.

AI Search

Manufacturing / R&DAzure AI

AI-Powered Research Knowledge Platform

Developed a searchable AI-driven knowledge base for millions of research documents.

IoT + AI

ManufacturingAzure IoT

Real-Time Quality Monitoring with IoT and AI

Implemented IoT and AI to monitor production quality in real time and reduce defects.

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