Agentic AI Development & LLM Integration

What we mean by agentic AI — and what we don't

An agent, as we build them, is software that uses a language model to work through a defined job — reading context, calling tools, producing structured results — inside boundaries designed in advance. It is not a chatbot bolted onto a homepage, and it is not a promise that AI will run your company. Scoping the job precisely is most of the engineering; we would rather automate one workflow completely than gesture at ten.

• Defined triggers, tools, and outputs — the agent’s job description is written before its code
• Structured outputs your systems can consume, not free text someone has to re-read
• Boundaries and escalation designed in advance, so failure modes are chosen, never discovered
• A working definition of “done” per task, measured, not vibes

AI features inside your product

Search that understands intent, summaries that save a reading, assistance that drafts the tedious parts — language models make features possible that were not, but only when they are designed like features rather than demos. We integrate LLMs into mobile and web products with the same UX discipline as everything else we ship: latency budgets, loading and failure states designed, and the model invisible behind an interaction that simply works.

• LLM-powered search, summarisation, extraction, and drafting inside your product
• Latency, cost, and quality budgets set per feature and measured in production
• Failure and fallback states designed — the feature degrades gracefully, never mysteriously
• Streaming, caching, and prompt design treated as engineering, not incantation

Agents that carry a workflow end to end

The useful agents in a business are rarely glamorous: triage the inbox and draft the response, reconcile the report, chase the missing document, prepare the brief a human then approves. We build agents around your real workflow — connected to your systems through tools and APIs, producing auditable output, and handing off to a person exactly where the stakes say they should.

• Tool use against your actual systems — CRMs, databases, documents, internal APIs
• Multi-step workflows with checkpoints, retries, and full audit trails
• Human approval gates placed where risk and judgement live, not sprinkled for show
• Escalation paths designed for the cases the agent should not decide

Evaluation, guardrails, and human oversight

A model that behaves in a demo has proven nothing. Before an agent touches real work we build an evaluation suite from real cases — the routine ones and the awkward ones — and measure behaviour against it; after launch, the evals run on, so regressions surface in a dashboard rather than in a customer conversation. AI accelerates the work; it does not replace review, security judgement, or release responsibility. That rule governs what we build for clients because it is the rule we run ourselves.

• Evaluation suites built from real cases before launch, run continuously after
• Guardrails on inputs, outputs, and tool access — least privilege applies to agents too
• Observability: every agent action logged, attributable, and reviewable
• Humans review and decide where the cost of a wrong answer is real

Built on models and tooling that will still exist next year

The stack is deliberately mainstream: Claude and GPT-class models behind well-supported APIs, standard patterns for tool use and retrieval, and integration code in the languages your team already runs. No exotic frameworks that will be abandoned by their maintainers before your project ships — the same "built to be kept" rule every app here is held to.