AI Engineering.

AI Strategy and Discovery

Most AI initiatives stall before they start because the business case, the data readiness, and the integration paths were never properly aligned. We work with your business and engineering leaders to identify the highest-value AI use cases, assess feasibility, and build a roadmap that aligns to your enterprise architecture.

Our discovery process is short and structured. Typical timelines are four to eight weeks for a focused use case discovery, longer for enterprise-wide AI strategy. We come out of discovery with a prioritised roadmap, a recommended engagement model, a build-versus-buy view on the platform layer, and the AI governance framework you will need to keep regulators comfortable.

• AI use case identification and prioritisation
• Feasibility and ROI modelling against business outcomes
• Data readiness and infrastructure assessment
• Model selection (closed-source frontier models versus open-source self-hosted)
• AI governance and model risk framework design
• Platform selection across AWS, Azure, GCP, Databricks and Snowflake

AI Platform and Product Engineering

We design, build and ship the platforms and products that put AI into production. Agent platforms, ML platforms, RAG systems, custom model fine-tuning, and the enterprise integration that connects AI to your CRMs, ERPs, data platforms and customer experiences.

Our platform engineering work spans foundation model integration, model serving infrastructure, vector store engineering, prompt management and orchestration, retrieval pipelines, and the observability layer production AI requires. Built on production-grade frameworks like LangChain, LangGraph, LlamaIndex and Semantic Kernel, with custom orchestration where the off-the-shelf frameworks fall short. Product engineering covers AI features built into your existing applications, new AI-native products built greenfield, and the integration work that makes AI useful inside your business processes.

AI Agent Development and Agentic Platforms

Production AI agents that automate workflows, support decision-making and integrate into your enterprise architecture. Custom single-purpose agents, multi-agent systems where specialised agents collaborate, and the full agentic platform infrastructure that scales agent deployment across your organisation.

This work is detailed on our Agentic AI page. The short version: we build with LangGraph, custom MCP servers, agentic RAG, and the human-in-the-loop and governance frameworks regulators expect. We are aligned to UBS's 2030 AI strategy on agentic AI for financial advisors and have transferable IP across agent orchestration, decision engines and explainability.

AI-Augmented Developer Enablement

AI is changing how software gets built, not just what software does. The teams that move fastest are deploying AI-assisted coding tools across their engineering organisations, building maturity models, and measuring productivity uplift.

We work with the major AI coding platforms, including Cursor, GitHub Copilot, Claude Code and Anthropic's Claude IDE integrations. The work covers tool selection and deployment, custom rulesets for your codebase and architecture standards, developer training programmes, productivity measurement and benchmarking, and the internal AI tooling and developer experience platforms that make adoption durable past the initial enthusiasm. This is one of the fastest-changing areas of enterprise AI; the vendors that landed in the market in 2024 are being repriced and resorted in 2026. We track the space actively and bring opinionated recommendations.

AI Governance, Validation and Model Risk

The regulatory bar for production AI has hardened. The FDA's January 2025 draft guidance on AI for regulatory decision-making, the joint FDA-EMA guiding principles released in January 2026, the EU AI Act, and SR 11-7 model risk management for banking all set higher standards for model validation, biological credibility, explainability and human oversight.

We bring the explainability frameworks, validated training pipelines, model registries and audit trail tooling we have built for tier-1 banks under SR 11-7. The same patterns apply directly to FDA, EMA and EU AI Act regimes, and to internal model risk programmes at any large enterprise.

• Explainable AI (XAI) and decision-engine instrumentation
• Model validation frameworks aligned to your regulatory regime
• Audit-ready model registries with full lineage
• Bias detection and human-oversight workflow design
• FAIR data implementation for proprietary data assets

AI Run, Monitor and Optimise

AI systems need continuous monitoring, retraining and adaptation. The model that worked at launch will degrade. The cost profile that looked reasonable at pilot scale will surprise you at production volume.

We provide ongoing AI lifecycle management as a managed service or as embedded support inside your team. Coverage includes AI observability and behaviour monitoring, model drift detection and triggered retraining, performance optimisation and cost management (FinOps for AI), compliance review and ongoing governance, and expansion of AI capabilities into adjacent use cases as the original deployment proves out.