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Quelford Technologies

Service 01

AI systems built for real business use.

We design and ship AI applications, assistants, agents and retrieval systems that plug into how your business actually runs — grounded in your data, monitored in production, and reviewed by engineers, not left to run unsupervised.

Most "AI features" never leave the demo stage. They work once, on a curated example, and fall apart against real inputs, real edge cases and real users. We build the layer around the model — retrieval, evaluation, guardrails, monitoring — that turns a promising prototype into something you can put in front of customers.

Every AI system we ship is designed around a specific business outcome: fewer support tickets, faster internal workflows, a better product experience. The model is one component in that system, not the whole strategy.

What this covers

Capabilities

AI applications

Purpose-built products with AI at the core, designed around a specific workflow rather than a generic chat window.

LLM integrations

Connecting your product to OpenAI, Anthropic, or open-weight models with proper prompt management, evaluation and fallback handling.

AI assistants

Assistants grounded in your documentation, policies and internal knowledge — not the open internet.

RAG systems

Retrieval-augmented pipelines over your own documents and data, with citation, freshness and access control handled properly.

AI agents

Multi-step, tool-using agents that can look things up, call APIs and complete real tasks — with logging and human checkpoints where it matters.

Workflow automation

Using AI to remove manual steps from operational processes: triage, categorisation, drafting, extraction, summarisation.

How we approach it

AI work starts with a short discovery phase to define what "correct" means for your use case, followed by a working prototype against real data before we commit to a production build.

Technology

OpenAI Anthropic Claude Vector databases RAG pipelines LangChain / custom orchestration Python & Node.js services

FAQ

Common questions

No. Part of discovery is identifying where your knowledge already lives — documents, databases, tickets, wikis — and designing the retrieval layer around that, rather than asking you to restructure everything up front.
Yes. We build against OpenAI, Anthropic and open-weight models depending on cost, latency and data-residency requirements, and design integrations so the underlying model can be swapped later without a rebuild.
Through grounding (retrieval over your actual data instead of relying on model memory), evaluation against real questions before launch, and monitoring after launch so drift gets caught early.

Related

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Ready to talk about ai development?

Tell us what you're trying to build. We'll respond with next steps, not a sales script.