We design, fine-tune, ground and deploy AI systems that hold up under real enterprise data, real compliance requirements and real scale — the same standard our engineers applied building AI products inside Samsung's R&D organisation.
Central controller architectures that orchestrate multiple LLM agents across internal APIs and enterprise systems — for summarisation, content generation, sentiment analysis and real-time insight generation.
Retrieval-augmented generation pipelines backed by vector databases, purpose-built to reduce hallucination and keep answers traceable to source data.
QLoRA and supervised fine-tuning (SFT) to adapt open and commercial models — OpenAI, Claude, Hugging Face — to your domain and tone.
Structured evaluation pipelines that measure accuracy, safety and compliance before an AI feature reaches production — not after.
Model Context Protocol and API integrations that connect LLMs securely to your internal systems, databases and knowledge bases.
End-to-end platforms for lead discovery, qualification and automated outreach — built on the same data modelling discipline as our analytics work.
Models degrade, data drifts, and cloud bills creep up quietly. We build the operational layer that catches it.
Dockerised, version-controlled, zero-downtime rollouts.
Model drift detection and performance monitoring in production.
Automated, repeatable pipelines from experiment to deployment.
Secure deployments engineered for enterprise-grade data protection.
Built a multi-agent AI system using LLMs and RAG pipelines for summarisation, content generation and sentiment analysis — reducing customer support ticket escalation by 35% and improving agent productivity through a centralised knowledge base.
Built automated pipelines processing 1B+ records/day into BigQuery, and a KPI monitoring platform giving end-to-end observability across a large-scale voice response system — increasing data-driven decision-making adoption by 75%.
We'll give you a candid view — including when a simpler, non-AI solution is the right call.
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