AI, MCP Server & Intelligent Integrations
We connect artificial intelligence to your business systems in a secure, governed, production-ready way — leveraging our deep experience in systems integration and back-end development.
AI is powerful, but on its own it's not enough — it needs to talk to your systems
Everyone is talking about artificial intelligence, but the real value comes when AI can work with the data and tools your company uses every day: your ERP, CRM, documents, internal APIs, databases.
The problem is that connecting an AI model to business systems reliably is far from trivial. Improvised solutions work in demos but break in production: inconsistent responses, sensitive data exposed, no control over who does what, costs spiralling out of control.
We start from a concrete advantage: years of experience in systems integration and enterprise back-end development. We know how APIs, databases, message brokers, and multi-tenant architectures work. This expertise allows us to build AI integrations that are not experiments — they are production-ready solutions, with the same attention to security, quality, and maintainability that we bring to everything else we do.
What we can do for you
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Develop custom MCP Servers. The MCP (Model Context Protocol) standard lets you expose the capabilities of your systems — queries, actions, data — so that any AI model can use them in a standard and controlled way. We design and develop these servers, connecting them to your APIs, databases, and internal services.
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Connect AI to your systems securely. Do you have an ERP, a document management system, a CRM, or custom services? We build the connectors that allow AI models to read, search, and act on your data — with authentication, role-based access control, audit logs, and secrets management.
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Build and integrate AI agents. Want an agent that doesn't just answer, but acts — queries your systems, performs operations and orchestrates multiple steps toward a goal? We build AI agents connected to your data and tools through MCP, with granular permissions, action limits, logging and human supervision where needed. No "magic", uncontrollable agents: reliable, traceable agents aligned with your processes.
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Automate processes with AI and workflows. Extracting information from documents, classifying requests, enriching data, generating reports: we build intelligent workflows with controlled steps, validation, confidence thresholds, and human approval where needed.
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Build RAG systems for your documentation. Documentation scattered across wikis, PDFs, and runbooks? We index your content and build a semantic search system that returns reliable answers with sources, respecting access permissions.
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Take prototypes to production. Already have a proof of concept that works on someone's desk? We take it, make it robust — containerisation, CI/CD, monitoring, cost management — and turn it into something that can run in production every day.
When it makes sense to involve us
- You want to connect AI models to your business systems without fragile or ungoverned solutions
- You need an MCP Server to expose internal capabilities in a standard way, reusable by multiple applications or agents
- You need control over security, costs, and quality of your AI integrations
- You want to automate operational processes (back-office, support, document management) leveraging AI
- You have a prototype that works but isn't production-ready
- You operate in an enterprise context with audit, access, and environment segregation requirements
Real-world cases
CV management and candidate matching with AI — Brainy Labs
We received CVs in different formats (PDF, Word, email) and the process of reading, evaluating, and matching them against open positions was entirely manual and time-consuming.
We built an internal tool that, using n8n and AI, automatically extracts relevant information from CVs — skills, experience, technologies, seniority — and compares them against our job requirements, returning an automatic matching with a compatibility score. The recruiter receives a pre-sorted shortlist and can focus on the evaluations that truly matter, instead of spending hours reading documents.
Did you know?
The MCP standard is changing the way AI interacts with business systems. Once a tool is exposed via MCP, any AI client or agent can reuse it as-is — no duplicate integrations, no rewriting code, no "prompt sprawl". It's the same principle as REST APIs, applied to the world of artificial intelligence: a standard interface that reduces complexity and increases control.
In detail: our technical expertise
- MCP Server — Design and development of MCP servers to expose tools, capabilities, and business data in a standard, secure, and reusable way across multiple AI clients and agents.
- Systems and API integration — Connectors to ERPs, CRMs, custom services, microservices, databases, and message brokers. This has been our bread and butter for years.
- RAG and knowledge access — Document indexing pipelines, semantic search, retrieval with sources and citations, access permission management.
- AI workflow orchestration — Agents and automations with controlled steps (validation, confidence threshold, human approval, retry, fallback), integrated with existing processes.
Technologies
AI & MCP: MCP SDK, OpenAI API, open-source models
Back-end: Spring Boot, Node.js (NestJS), Python
Orchestration & Workflow: n8n, AWS Step Functions
Message broker: RabbitMQ, Kafka
Vector search: OpenSearch, ElasticSearch, vector DB
Infrastructure: Docker, AWS (Lambda, S3, IAM, ECS), GitHub Actions, Jenkins
Observability: OpenTelemetry, log/metrics/tracing
Frequently asked questions
What is an MCP Server and what is it for?+
An MCP Server (Model Context Protocol) is a component that exposes your systems' capabilities — queries, actions, data — in a standard way, so any AI model or agent can use them under control. It's essentially a secure adapter between AI and your management systems, CRM, databases and APIs: you build it once and reuse it across multiple applications and assistants, without duplicated integrations.
What's the difference between an AI agent and a simple chatbot?+
A chatbot answers questions; an AI agent acts. An agent can query your systems, perform operations, chain multiple steps and make decisions toward a goal — for example retrieving an order, checking its status and updating the management system. We build agents connected to your data and tools through MCP, with permissions, action limits, logging and human supervision where needed.
Is our business data kept secure?+
Yes, it's at the core of our approach. Integrations include authentication, role-based access control, secret management and audit logs on who does what. We define with you which data the AI can read and which actions it can take, with the same security attention we bring to enterprise systems. We can also work with self-hosted models when data must not leave your boundaries.
Do we have to use OpenAI, or can we use other models?+
You're free to choose. We work with commercial provider APIs (such as OpenAI or Claude) and with self-hosted open-source models, based on privacy, cost and performance requirements. Thanks to the MCP standard, the integration with your systems stays the same even if you change model later.
We only have an idea or a prototype: where do we start?+
That's perfectly fine. We start from an analysis of the processes and systems involved to identify the highest-value use case. If you already have a proof of concept that works on someone's desk, we make it robust and production-ready — containerization, security, monitoring and cost management included.
Do we need clean, structured data to get started?+
Not necessarily. Part of our job is making data accessible to AI: document indexing, preparation pipelines, RAG systems with sources and citations. We assess the current state of your data together and define the necessary steps, without requiring a perfect starting point.
Want to learn more? Let's talk over a coffee ☕
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