AI Development

Generative AI development services

Generative AI is a new way of building software. It produces text, code or images rather than following instructions exactly, and that changes how you design, test and run it.

Discuss Your Project

Most business generative AI comes down to a handful of patterns. Retrieval-augmented generation (RAG) gives the model relevant passages from your documents to answer from, rather than from its general training. Structured extraction turns text into data. Drafting tools produce first versions for people to edit. Agents use tools to carry out multi-step tasks. The art is in choosing the simplest pattern that works.

In RAG, most of the quality comes from the retrieval, not the model. If the right passage isn’t found, the answer will be wrong however clever the model is. So we spend time on how documents are split up, hybrid search combining keywords with embeddings, and reranking results. Showing the sources alongside each answer means people can check it, which also builds trust.

Guardrails and security

LLM applications have their own security risks. Prompt injection, where text in a document or a user’s message tries to hijack the model’s instructions, is the best known, and there isn’t yet a complete fix. The OWASP Top 10 for LLM Applications is a useful reference. We limit what a model is allowed to do, keep permission checks in ordinary code rather than the prompt, and treat model output as untrusted input. If you’re adding this to an existing system, see AI integration.

What makes generative AI hard in production

Hallucinated answers

Plausible but wrong output, which is hard to spot without grounding and evaluation.

Prompt injection

Instructions hidden in user input or documents that can manipulate what the model does.

Changing models

Providers update and retire models, and quality can shift, so you need tests that catch this.

What we build

RAG systems

Search and answer tools over your documents, with citations and access controls.

Content tools

Drafting and rewriting tools that follow your style and facts, with a person approving the result.

Structured output pipelines

Reliable conversion of unstructured text into validated data.

Agents

Task-focused agents with limited tool access and approval steps.

Evaluation suites

Automated tests for answer quality, safety and regressions when anything changes.

Where it fits

  • Internal knowledge search for policy and technical documents
  • Proposal and report drafting
  • Summarising calls, cases and long documents

Technology

  • Claude
  • OpenAI
  • Gemini
  • Llama
  • pgvector
  • Pinecone
  • Python
  • LangChain
  • Promptfoo
  • AWS Bedrock

Sectors we think about here

Generative AI questions

What is RAG?
Retrieval-augmented generation finds relevant passages from your own content and gives them to the model, so that its answers are based on your information and can show where they came from.
Which model should we use?
It depends on the task, budget and data requirements. We test several on your real examples, because benchmarks don’t tell you how they’ll handle your work, and we keep the design flexible enough to switch.
Who owns the generated content?
Ownership of AI-generated content is still a developing legal area. Check your provider’s terms, and take legal advice if this matters to your business.

Related services

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