Real-World Results from Data & AI

Challenge:

A global financial institution struggled with inefficiencies in retrieving critical information across policy, compliance, and operational documents. Employees relied on outdated portals and manual searches, leading to long turnaround times, inconsistent answers, and reduced productivity.

Impact:

Faster Knowledge Access: Document retrieval times reduced from hours to seconds. Improved Accuracy: Generated summaries validated through random sampling audits and offline evaluations. Reusable Architecture: A governed, extensible foundation now deployed across additional knowledge domains within the bank.

Challenge:

A leading energy operator faced recurring unplanned shutdowns and inefficiencies in gas compression operations. Their monitoring system relied on manual inspection of PI data and spreadsheet-based performance tracking, which made it difficult to identify early warning signs of component degradation. Operational parameters such as turbine pressure, lube oil pressure, discharge temperature, and seal gas differentials were logged inconsistently, leading to undetected anomalies and high maintenance costs.

Impact:

Reduced unplanned downtime through early detection, increased maintenance efficiency, and provided engineers with real-time anomaly dashboards for operational awareness and failure prevention.

Challenge:

A major energy operator needed an efficient way to process and evaluate thousands of daily operational and compliance documents. Manual verification led to inconsistent results and delayed decision-making in field operations.

Impact:

Reduced manual data validation time, improved report accuracy and traceability across compliance workflows, and enabled near real-time operational insight through automated document synthesis.

Challenge:

A major insurer needed a faster and more consistent way to review lengthy regulatory documents and compliance circulars. Manual summarization created knowledge silos and increased audit delays.

Impact:

Plan for reduced compliance review time, increased accuracy and regulatory adherence, and established automated knowledge reuse across departments.

Challenge:

A large-scale analytics environment required automated detection of data drift across production and UAT workspaces, with strict separation and audit control for model retraining.

Impact:

Delivered a cross-environment drift management solution exceeding industry best practices, automated retraining readiness with zero manual intervention, and enhanced compliance reporting for all production model events.

Challenge:

A leading retail enterprise required a centralized platform to streamline its rapidly expanding machine-learning initiatives. Multiple business units were independently developing predictive models, causing redundant workloads, inconsistent governance, and fragmented deployment pipelines. This created operational inefficiencies and made compliance with corporate AI-governance standards difficult.

Impact:

Unified ML operations across all business units, reduced model deployment time, established continuous compliance through governed pipelines, and enabled measurable ROI through re-usable AI components and workflows.

Challenge:

Enterprises required a consistent and reusable framework to implement domain-specific RAG systems capable of ingesting and retrieving knowledge across various file formats and departments.

Impact:

Delivered a production-ready GenAI framework that reduced new client onboarding from weeks to hours, provided enterprise auditability through integrated evaluation layers, and formed the baseline for all subsequent RAG deployments across industries.

Challenge:

An organisation needed a scalable framework to monitor people and vehicles using a camera network. The goal was to detect objects (people/vehicles) reliably, blur faces for privacy, store the data in cloud storage, and produce downstream analytics (counts, percentiles, time-based detection stats) via a dashboard.

Impact:

Enabled near-real-time monitoring of people/vehicle events with structured analytics, and provided a reusable framework that combines CV detection, automation, cloud storage, and dashboarding — offering operational transparency and actionable metrics.

Challenge:

A client had a database of data fields and required an automated mechanism to validate and update those records by cross-checking online sources (including public web sites and intranet sources). They needed the system to intelligently select which fields to validate or update, run flexible search strategies, adapt when blocks or connectivity issues occurred, and notify when updates were found.

Impact:

Delivered an automated cross-validation framework that significantly reduces manual fact-checking and improves data freshness and accuracy, and created an adaptive intelligence layer over web data extraction that handles search complexity, blocking conditions, and record updates.

Challenge:

The task involved preparing large corpora (books, articles, text contents) by cleaning and normalising the data so it could be used to train Generative Pretrained Transformers. The preprocessing needed to remove numbers, URLs, table of contents, symbols, convert first-person narratives to collective form, eliminate odd proper-nouns/product-names, and manually proofread where the automation couldn't resolve inconsistencies.

Impact:

Provided a high-quality text corpus prepared for generative-model training, enabling downstream generative tasks (descriptive paragraph generation, topic modelling) with clean and consistent input, and formed a backbone for enterprise-grade NLP modelling that ensures data readiness, governance, and consistency.