LLMs that do something useful. Not just impressive.
Fine-tuned models, retrieval-augmented systems, and multi-agent pipelines — built for specific business outcomes, not demos.
Generative AI is the category of AI systems that produce new content: text, code, images, audio, and video. Large language models (LLMs) are its most commercially deployed form. The gap between a compelling ChatGPT demo and a production system that reliably serves customers is large — it requires prompt engineering, retrieval architecture, fine-tuning, output validation, and fallback logic. We close that gap.
40%
average reduction in support ticket handle time with LLM triage
3–8×
ROI on RAG systems vs. manual knowledge base search
65%
of GenAI projects fail without grounding and hallucination controls
What's included
Services within Generative AI & LLMs
Each is a scoped engagement. Tell us which one fits your situation — or book a call and we'll scope it together.
LLM Fine-Tuning
Supervised fine-tuning, RLHF, and LoRA/QLoRA adaptation of open-source models (Llama 3, Mistral, Phi) on your proprietary data — for domain voice, instruction following, and task-specific accuracy.
Retrieval-Augmented Generation (RAG) Systems
Architecture and build of RAG pipelines: document chunking, embedding selection, vector store setup (Pinecone, Weaviate, pgvector), retrieval tuning, and citation-grounded answer generation.
AI Agents & Orchestration
Multi-step autonomous agents using LangChain, LlamaIndex, or custom orchestration — with tool use, memory, error recovery, and human-in-the-loop escalation for production reliability.
AI Image Generation
Stable Diffusion fine-tuning, ControlNet integration, and product image generation pipelines for e-commerce, media, and design workflows — with IP and brand safety filters.
AI Video Generation
Automated video production from scripts, product data feeds, or structured briefs — short-form content, explainer videos, and personalised video at scale.
Code Generation & AI Dev Tools
Custom code generation models, code review automation, and AI-assisted development tools trained on your internal codebase standards and architecture patterns.
Prompt Engineering & Optimisation
Systematic prompt design, few-shot construction, chain-of-thought structuring, and A/B testing to maximise output quality while reducing token cost for deployed LLM applications.
The problem
Why most LLM projects don't make it to production
These aren't edge cases — they're what we hear on almost every discovery call. If any of them sound familiar, this is likely the right place to start.
Hallucination: models confidently produce wrong answers — without retrieval grounding and output validation, this breaks trust immediately
Latency and cost: unconstrained LLM APIs are expensive at scale — prompt optimisation and model selection cut costs 60–80%
Context window limitations: most business documents exceed what a raw LLM can process without chunking and retrieval strategies
Brand and compliance risk: without output guardrails, LLMs produce off-policy, off-brand, or legally risky content
Integration complexity: connecting LLM output to databases, ticketing systems, and CRMs requires careful orchestration engineering
Who it's for
This is the right fit if…
These systems work best for organisations at a specific point — where the problem is real, the data exists, and generic tools have already proved insufficient.
SaaS companies embedding AI into their product — chat, search, code assist, or content features
Professional services firms with large document libraries that need to be queryable
Customer service operations wanting AI triage and draft-response generation
Content teams that need to produce high volume without losing brand voice
Operations teams running multi-step approval or research workflows that could be automated
Common questions
What people ask before they book
Not sure where to start?
Talk it through on a free call.
We'll help you figure out which of these fits your situation — no pressure, no obligation.
Book a Free 30-Min Call