AI Agent Development & MCP Server Integration: Connecting Language Models to the Systems You Already Run

The demonstrations all work. The pilot that summarised your documentation was impressive, the prototype that answered support questions looked ready, and then it met the actual CRM, the actual permissions model and the actual data, and stopped being ready.

That gap is the work. Not the model (the model is the easy part now), but everything between a language model and a system that has opinions about authentication, rate limits, tenancy and who is allowed to see what.

What we build

MCP servers for your internal systems

The Model Context Protocol is an open standard for exposing tools and data to AI assistants in a way that is consistent across clients. It has become the practical answer to "how do we let an assistant use our systems without writing a bespoke integration for every assistant".

We build production MCP servers over the things your business actually runs: your CRM, your ticketing system, your data warehouse, your internal APIs, your document store, your ERP. Not a demo server with a hardcoded token: one with proper authentication, per-user authorisation that respects the permissions your systems already enforce, multi-tenant isolation, rate limiting, structured logging and an error surface that tells the model something useful when a call fails.

Custom AI agents and assistants

Agents that do a job end to end: triage an inbound queue, reconcile two systems that disagree, draft the first version of a document a human then approves, run a research task across internal and external sources. We build them with the boring parts included: retries, timeouts, cost ceilings, idempotency on anything that writes, and a human approval step wherever an action is hard to undo.

Retrieval over your own content

RAG, done properly: document ingestion and chunking that respects structure rather than character counts, embeddings and vector search, hybrid keyword and semantic retrieval because pure vector search misses exact matches, reranking, and citations back to the source so an answer can be checked. Plus the unglamorous half, keeping the index current as the underlying documents change, and honouring access control so retrieval never surfaces a document the user could not otherwise open.

Workflow and back-office automation

Not everything needs an agent. A large share of what companies want from AI is classification, extraction and routing, reading an inbound email and deciding what it is, pulling structured data out of an unstructured document, matching a description to a catalogue entry. These are cheaper, more reliable and easier to evaluate than an agent, and when that is the right answer we build that instead.

Evaluation, observability and guardrails

An AI feature without evaluation is a feature nobody can improve. We build test sets from your real cases, measure against them on every change, trace every call with its inputs, tool use, latency and cost, and put limits in place before a runaway loop finds your API bill. Prompt injection is a live risk the moment an agent reads untrusted content and can also act. We design the tool boundary so a malicious document cannot turn into a destructive action.

How we work with model providers

We are not tied to one. We build against Anthropic's Claude models, OpenAI, Google, and open-weight models running on your own infrastructure, and we design the integration so the model is a replaceable component. Which one you should use depends on the task, the latency you need, the cost per call at your volume, and (increasingly the deciding factor in Europe), where the data is allowed to go.

For clients with data residency constraints, we deploy into EU regions or self-hosted inference on your own hardware, and we will tell you honestly where that costs you capability.

What this is not

We are not an AI strategy consultancy. We do not run workshops about the transformative potential of anything.

We also will not build an agent for a problem that does not need one. A meaningful share of the AI projects we are asked about are better solved by a well-specified API integration, a search index, or fixing the data quality issue underneath. Recommending that costs us a bigger engagement and saves you a system you would have to maintain.

Where this tends to pay off

Internal knowledge that is technically available and practically unreachable: thousands of documents across a wiki, a shared drive and a ticketing system, where the person who knows which one is current is on holiday.

High-volume triage: support queues, claims intake, inbound sales, application review. Anything where a human currently reads something, decides what it is, and passes it on.

Systems that never got an integration: two tools that should talk, do not, and the workaround is a person with a spreadsheet. An agent with MCP access to both is often a shorter path than a formal integration project.

Document-heavy operations: contracts, invoices, specifications, regulatory filings. Extraction with a human review step is reliable enough to change the economics of these processes today.

Developer productivity inside your own codebase: internal tooling, code review assistance, test generation, and MCP servers that give your team's assistants access to your build system, your issue tracker and your logs.

How engagements run

Discovery, one to two weeks. We look at the process you want to change, the systems involved, and what the data actually looks like. The output is a written assessment of what is feasible, what it will cost to run per month at your volume, and what the failure modes are. Sometimes it says do not build this.

Proof of concept, three to six weeks, against real data in a controlled environment. The point is not a demo. It is an evaluation result you can trust, with a measured accuracy figure against cases you chose.

Production build, typically eight to sixteen weeks, including authentication, authorisation, monitoring, evaluation harness, cost controls and documentation. Deployed into your infrastructure, in your repository, with your team involved throughout.

Operation or handover. Either your team takes it, with the runbook and the evaluation suite, or we keep running it under support. Models change, prompts drift, and the evaluation suite is what tells you when something has quietly got worse.

Technology

Python and TypeScript for agent and MCP work; PostgreSQL with pgvector, Qdrant, Weaviate or OpenSearch for retrieval; AWS Bedrock, Azure OpenAI, direct provider APIs, and self-hosted inference with vLLM or Ollama where the data cannot leave; OpenTelemetry-based tracing; and whatever your existing stack is on the integration side, because that is where the work actually happens.

Frequently asked questions

What is an MCP server, in plain terms?

A small service that exposes some capability (reading from a database, creating a ticket, searching your documents), in a standard format that AI assistants understand. Build one, and any MCP-compatible assistant can use that capability without a custom integration for each.

Should we build an MCP server or a normal API integration?

If one AI assistant needs one system, a direct integration is simpler. MCP earns its keep when several assistants or several teams need the same systems, when you want the capability available to tools you have not chosen yet, or when you want one place where the permissions and audit logging live.

How do you stop an agent doing something destructive?

Layered. Tools that write are separated from tools that read, and write tools are scoped as narrowly as the task allows. Anything irreversible goes through a human approval step. Every call is logged with its arguments. And the agent runs with a service identity that has only the permissions the job needs, so the blast radius is bounded by your own authorisation model rather than by the prompt.

What does this cost to run?

It depends almost entirely on volume and on how much context each call carries. We model this during discovery with real numbers, because the difference between a design that sends a whole document on every turn and one that retrieves the relevant part can be an order of magnitude in monthly cost.

Can this run without our data leaving the EU?

Yes. Major providers offer EU regions, and open-weight models can run on infrastructure you control. There is usually a capability trade-off, and we will be specific about what it is rather than pretending there is none.

Our last AI pilot did not make it to production. Why would this?

Usually because the pilot proved the model could do the task and never touched authentication, permissions, error handling, evaluation or cost, which is where the remaining ninety per cent of the work is. We start from the assumption that the model works and spend the engagement on everything else.

Get started

Describe the process you want to change and the systems it touches. We will tell you whether an agent is the right instrument, what a realistic first version looks like, and what it costs to build and to run.

Contact us to arrange a technical discovery session.

Related services

• Staff Augmentation & Outsourcing: AI engineers embedded in your team
• EU AI Act Compliance Engineering: for when what you build falls in scope
• AWS Cost Optimization: inference and retrieval workloads have a habit of surprising people