AI, MCP Server & Intelligent Integrations

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

• 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.

• 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.

• 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.

• 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.

• 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.

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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