Generative AI & LLMs

Cutting Through the Hype

Most generative AI vendor pitches conflate three different things — chat interfaces, retrieval systems, and autonomous agents. Each has different risks, costs, and use cases. We work with you to identify which pattern fits your problem. Sometimes the answer is a simple ChatGPT-style chat interface over your documentation. Sometimes it is a retrieval system grounding answers in your knowledge base. Sometimes it is full multi-step agentic AI workflows that take action. Often the answer is a much smaller model than the conversation suggests. We never recommend frontier models when smaller ones deliver the same business outcome at 1/20th the cost.

RAG Systems and Knowledge Bases

Retrieval-augmented generation is the highest-ROI starting point for most businesses adopting generative AI. We build RAG systems that ground model responses in your actual content — policies, procedures, technical documentation, customer history, product catalogues. Citations let users verify answers. Permission-aware retrieval respects access controls. Continuous evaluation surfaces hallucinations and topic drift. We deploy RAG systems on Pinecone, Weaviate, Qdrant, pgvector, or whatever vector store fits your existing stack. The conversational AI capability uses RAG as its foundation, and the data AI work ensures the underlying content is clean, current, and well-structured.

Content and Workflow Pipelines

Generative AI shines for content production at scale — when paired with proper quality controls. We build pipelines for marketing copy variants, technical documentation drafts, code generation, image synthesis, multilingual content, and automated reporting. Every pipeline includes prompt templates aligned to your brand voice, evaluation against quality rubrics, human approval gates for high-stakes content, and version control. Teams using our pipelines typically produce 5-10x more content per week with higher consistency and lower revision cycles. The AI automation framework handles the orchestration.

Cost Optimisation

Generative AI costs spiral fast if you let them. Frontier models cost 10-50x more per token than open-weights alternatives. Naive prompt engineering wastes 60-80% of spend on context that does not change the answer. Caching, batching, and smart routing between models cut bills dramatically. We build systems with cost observability from day one — you see spend per use case, per model, per business unit. Open-weights deployments on your own infrastructure (using vLLM, TGI, or Ollama) eliminate per-token cost for high-volume internal workloads while keeping data inside your environment. Our AI strategy work always considers total cost of ownership, not just initial deployment cost.

Common Generative AI Use Cases

Five clusters consistently deliver the strongest ROI for businesses adopting generative AI in production.

• Knowledge access: RAG over policies, procedures, technical documentation, prior client work, product catalogues — internal and external.
• Content production: marketing copy variants, technical documentation drafts, multilingual content, transcript summarisation, brief writing.
• Code and engineering: code review, test generation, documentation, debugging, technical specification drafting, SQL generation.
• Customer interaction: support chat, sales discovery conversations, appointment booking, post-purchase engagement, voice IVR.
• Decision support: document summarisation for legal and compliance review, contract clause comparison, policy alignment checks.

Most teams get the strongest first win from knowledge access or content production — both deliver measurable productivity gains in weeks. Our AI Readiness Assessment ranks these against your data maturity, content needs, and team capacity.

Why UK Businesses Need Local Expertise in GenAI

The generative AI landscape changes weekly, but the UK business context is stable. You operate under GDPR, FCA rules if you're in financial services, NHS requirements if you're in healthcare, SRA rules if you're legal. You compete with peers in your industry who are also experimenting with genAI. And your team is stretched — everyone's already juggling existing work, so a consultant who wastes time on false starts costs you real productivity.

Most UK businesses using genAI today are burning money because they haven't built the right guardrails. Overspend on API calls because nobody was tracking cost per use case. Hallucinations in customer-facing systems that damage brand trust. Data leakage because API-based models retain their inputs by default. RAG systems that cite incorrect sources. We help you avoid these pitfalls from day one — not through generic best practices, but through UK-specific implementation patterns we've tested with financial services, healthcare, legal, and retail teams.

GenAI adoption in UK businesses typically follows a three-phase pattern: first, a quick win that proves the business case (usually knowledge access or content production). Second, scaling that pattern to other teams. Third, building a composed stack where multiple genAI components work together. We help you plan all three phases in advance so you build cumulative advantage rather than isolated pilots that never reach production.