Generative AI Development

LLM application development

A large language model on its own is raw capability. Turning it into a product your customers or staff rely on takes real engineering: a clear interface, grounded knowledge, structured outputs, error handling and integration into your systems. We build full LLM applications, not prompt wrappers, designed around the job they need to do.

We cover the whole stack: the model layer, the retrieval and data layer, the prompt and orchestration logic, and the application and API around it. The result is a product that behaves predictably, fails gracefully and fits into how your business already works.

• End-to-end LLM application builds
• Structured outputs and validation
• Orchestration and prompt logic
• System and API integration
• Error handling and fallbacks
• Scalable, production-ready architecture

Related: AI agent developmentAPI development & integrations

Custom chatbots and virtual assistants

Generic chatbots frustrate customers because they do not know your business. We build custom chatbots and assistants that answer from your own content, follow your rules and tone, and hand over to a human cleanly when needed, across your website, support channels and internal tools.

Because they are grounded in your knowledge and evaluated for accuracy, these assistants deflect routine enquiries, answer out of hours, and give consistent responses at any volume, while escalating the cases that genuinely need a person.

• Website and support chatbots
• Internal knowledge assistants
• Grounded in your own content
• On-brand tone and rules
• Clean handover to humans
• Multi-channel deployment

Related: AI agent developmentUI/UX design

Retrieval-augmented generation (RAG)

RAG is what makes generative AI accurate for your business. Instead of relying on what a model learned in training, a RAG system retrieves the relevant passages from your documents and data at query time and answers from those, with citations. This dramatically reduces hallucination and keeps answers current as your content changes.

We build the whole pipeline: ingesting and chunking your content, embedding and indexing it, tuning retrieval so the right material surfaces, and composing grounded answers. It is the single most important technique for trustworthy generative AI, and we treat it as core, not an add-on.

• Content ingestion and chunking
• Embeddings and vector indexing
• Retrieval tuning for relevance
• Grounded answers with citations
• Freshness and re-indexing
• Hallucination reduction

Content and image generation tools

Generative AI can scale your content without scaling your team: first drafts, summaries, variations, product descriptions, replies and images produced on demand and shaped to your brand. We build the tools and workflows that let your people generate quickly and then review and refine, keeping humans in control of quality.

We focus these tools on real workflows rather than novelty, so they save time where it counts. Everything is built with guardrails and brand rules so output stays consistent, appropriate and on message.

• Drafting and copy generation
• Summarisation at scale
• Product and description generation
• Image generation workflows
• Brand-consistent output
• Human review and refinement

Model selection, fine-tuning and prompt engineering

The right model depends on the job. We choose between OpenAI, Anthropic Claude and open models based on your accuracy, cost, latency and privacy needs, and we are honest when a smaller or self-hosted model is the better call. Often the biggest gains come from strong prompt engineering and retrieval rather than a bigger model.

Where fine-tuning genuinely helps, for tone, format or a specialised task, we do it on the right data and measure the lift. We never fine-tune for the sake of it, because for most use cases good prompting plus RAG delivers more, faster and cheaper.

• Model selection (OpenAI, Claude, open models)
• Prompt engineering and templates
• Fine-tuning where it earns its place
• Cost and latency optimisation
• Open and self-hosted model options
• Privacy-driven model choices

Related: AI consulting & strategyMachine learning development

Evaluation, guardrails and safe deployment

Generative AI in front of customers has to be trustworthy. We build evaluation suites that test outputs against real queries for accuracy, tone and safety before launch, then monitor quality, cost and latency in production. We add guardrails that catch unsafe, off-brand or low-confidence responses before they reach a user.

This discipline is what separates a reliable product from a liability. You get a system that is measured, observable and improved over time, with plain-English reporting on how it is performing.

• Output evaluation against real queries
• Safety and content guardrails
• Confidence and fallback handling
• Quality, cost and latency monitoring
• Data privacy and compliance by design
• Ongoing refinement and reporting