AI Agents & LLM Integration

AI Agent Development & LLM Integration

AI agents that do real work inside your systems — calling your APIs through MCP tool calling, with guardrails, human approval where it matters and measurable results.

A chatbot answers questions; an agent takes action. We build LLM-powered agents that read live data, call your APIs and complete multi-step tasks — looking up an order, drafting a reply, updating a record or triaging a ticket — inside the tools your team already uses.

We connect models to your systems using the Model Context Protocol (MCP) and well-defined tool contracts, add guardrails and approval steps for anything sensitive, and log every action so you can see exactly what the agent did and why.

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What we deliver

LLM features embedded into your existing web or mobile product
MCP servers and tool definitions over your APIs and databases
Multi-step agents with planning, memory and error recovery
Human-in-the-loop approval for sensitive actions
Prompt, tool and model evaluation with regression tests
Usage, cost and latency monitoring in production

Use Cases

Where it fits

Customer service agents

Resolve order, refund and account questions by calling live systems, not just reading FAQs.

Back-office automation

Extract data from emails and documents, fill forms and update your CRM or ERP.

Internal copilots

Give teams an assistant that can query reports, create tickets and summarise activity.

AI features in your SaaS

Add summarisation, classification, drafting and natural-language search to your product.

How We Work

Spec-Driven Agile Development

AI projects fail when nobody defines what “good” looks like. We write it down first, then deliver against it in short, demo-driven sprints.

Specify

We start with a written spec: goals, user stories, acceptance criteria, data sources and the metrics an AI feature must hit.

Architect

Retrieval pipeline, model selection, API contracts and infrastructure are designed against the spec before code is written.

Build in Sprints

Short agile sprints with a working demo at the end of each one. The spec evolves with your feedback, so scope never drifts silently.

Evaluate

Every release is tested against the spec — including retrieval accuracy, answer grounding and hallucination checks for RAG features.

Launch & Improve

We ship to production, monitor real usage and answer quality, and feed what we learn into the next sprint.

FAQ

Frequently asked questions

What is the difference between an AI agent and a chatbot?

A chatbot generates replies; an agent can also use tools — calling APIs, querying databases and performing actions — and chain several steps together to complete a task.

What is MCP tool calling?

The Model Context Protocol (MCP) is an open standard for connecting LLMs to tools and data sources. We expose your systems as MCP tools so any compatible model can use them safely, through a defined contract rather than ad-hoc prompts.

How do you keep agents from doing the wrong thing?

Agents only get the tools and permissions they need, sensitive actions require human approval, inputs and outputs are validated, and every tool call is logged. Behaviour is tested against an evaluation set before each release.

Can you add AI to our existing product?

Yes — most of our LLM work is integration into existing web apps, mobile apps and back-office systems, delivered as features behind your current UI and APIs.

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Let's talk about AI Agents & LLM Integration

Book a free consultation — tell us what you want to build and we'll come back within one business day with an approach and next steps. No cost, no commitment.

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