Challenge:
This multinational chemical company sought to leverage emerging AI technologies to transform information extraction and knowledge access across its global operations. The challenge lay in building scalable, secure, and compliant LLM-based products that could integrate seamlessly with existing Azure infrastructure.

Solution:

• Large Language Model-based solutions using RAG and advanced prompt engineering.
• Centralised data management pipelines.
• Full end-to-end lifecycle of LLM products from proof-of-concept to deployment.
• Developed API endpoints for scalable, cloud-native integration.

Outcome:
Delivered enterprise-ready AI products that transformed unstructured data into actionable knowledge. The initiative standardized LLM deployment practices at the client, enhanced cross-functional collaboration, and established a scalable framework for future AI-driven solutions across the organisation.

Challenge:
A leading audiobook publisher needed a model to identify which books to publish in audio format to maximise return on investment.

Solution:

• Automated pipelines for data ingestion, preprocessing and cleaning
• Centralised data warehouse for modeling and reporting
• Machine learning based modeling solution to achieve accurate, stable and unbiased predictions
• Flexible reporting system with customisation capabilities.

Outcome:
Highly accurate, sales prediction model for audiobooks.
Increased efficiency of staff through concentrating time and effort on the most important targets.

Challenge:
Leading Agritech company required a solution that automatically detected fruits on the trees from an image.

Solution:
Our team achieved this task through the development of a deep learning object detection.

Outcome:
The 2-month PoC developed and validated a concept that outperformed human in accuracy, especially on detection of occluded fruits.

Challenge:
The multi-national shipping company’s global procurement process was manual, slow, and inefficient. Relying on spreadsheets to manage a $15B spend caused fragmented data and missed cost-saving opportunities.

Solution:
The team supported the shipping company to develop a bespoke AI agent. The platform automated the analysis of thousands of supplier quotes and centralised data, transforming a manual process into an intelligent, strategic function.

Outcome:
The "Quest" agent slashed quote analysis time from weeks to mere hours, delivering millions in savings. It enabled faster, data-driven decisions and became a successful blueprint for AI adoption across the company.

Challenge:
Global reinsurance company aimed to modernise its data analytics capabilities by creating a knowledge-driven architecture to unlock relationships hidden in complex datasets. The company needed a robust, scalable knowledge graph and an MLOps backbone for production-level AI adoption.

Solution:

• Designed and deployed a knowledge graph architecture within a microservices environment.
• Developed APIs and integrated knowledge graph algorithms for advanced reasoning and data discovery.
• Built an MLOps infrastructure to enable reproducible model development and deployment.
• End-to-end project management, from stakeholder alignment to inference platform delivery.

Outcome:
We empowered the reinsurer with a unified, intelligent data fabric that enabled faster insights and data-driven decision-making. The new MLOps and knowledge graph infrastructure accelerated the company’s AI strategy, improved collaboration between data science and engineering teams, and strengthened model governance.