AI Integrations & MCP
Connect AI to the systems you already run — safely.
ERP, CRM, Magento, WooCommerce, databases and internal APIs, opened to AI models through secure APIs and Model Context Protocol servers — with scoped permissions, approvals and a log of every call.
AI CONNECTED TO YOUR BUSINESS
A model is only as useful as the systems it can reach.
Orders, stock, customers, invoices and documents already live in your ERP, CRM, shop and databases. We give AI a safe, controlled way in.
A general-purpose assistant can write and summarise, but it does not know what is in stock, which invoices are overdue or what a customer ordered last month. To be useful inside a company, an AI model needs access to live data and, sometimes, the ability to act: open a ticket, update an order status, prepare a quote. Done carelessly, that means a model holding an admin password. Done properly, it means a small set of narrow, documented tools with clear permissions, approvals for anything that writes, and a log of every call.
Model Context Protocol (MCP) is an open standard built for exactly this: a server exposes tools and data, and any compatible assistant or agent can use them — Claude, ChatGPT, IDE assistants or your own application. We build MCP servers and classic REST or GraphQL APIs on top of the systems you already run. E-commerce is our home ground: our team has been building Magento and WooCommerce stores and their integrations since 2013, so we know where the data lives and what should never change without a person in the loop.
Integrations are the foundation for most other AI work. An assistant that answers from manuals and internal wikis is a RAG knowledge assistant; when the AI has to run multi-step workflows on its own, it becomes one of our AI agents. Both rely on the connectors described here. And when the right answer is a new application rather than a connector, we build it as custom software.
Custom MCP servers
We design MCP servers around your systems: a small set of clearly described tools — search orders, check stock, read customer history, create a draft quote — instead of raw database access. Hosted on your infrastructure or on our cloud, with authentication and logging built in.
Secure APIs and connectors
Where an AI feature needs a regular API, we build it: REST or GraphQL endpoints, webhooks and sync jobs between ERP, CRM, e-commerce and internal tools, with OAuth 2.0, scoped tokens and rate limits. For larger projects, see our ERP integration service.
AI features in your apps
We add AI where your people already work: natural-language search in a catalogue or back office, summaries of tickets and orders, automatic classification of emails and documents, product descriptions and translations generated as drafts for review.- MCP servers for ERP, CRM, e-commerce and internal tools
- Magento and WooCommerce connectors for AI assistants and agents
- OAuth 2.0, scoped API tokens and per-tool permissions
- Read-only by default, write actions behind human approval
- Rate limits, quotas and cost controls per user and per tool
- Audit log of every tool call: who, what, when and with which data
- Secure REST and GraphQL APIs and webhooks for AI features
- AI search, summaries and classification in existing apps
- Works with OpenAI, Anthropic Claude and open-weight models
- Hosting on our own cloud and GPU cloud in Romania
CONTROL AND SECURITY
Connecting AI to production data is a security project first
Every integration starts from one question: what is the worst this tool could do, and who would notice?
Least privilege. Each tool gets its own scope. An assistant that answers stock questions cannot read customer emails, and one that drafts quotes cannot issue refunds. Tokens are scoped, short-lived and revocable, and the AI acts with the permissions of the person it serves, never through a shared admin account.
Read before write. We start read-only. Write actions — changing an order, sending an email, updating a price — are separate tools, validated on the server and, where it matters, held for human approval in a queue or a chat message before they run.
Prompt injection. Product reviews, emails and support tickets can contain text written to steer a model. We treat everything the model reads as untrusted, keep sensitive actions out of reach of untrusted input and test these paths on purpose, using the same methods as our AI security and red teaming work.
Limits and costs. Rate limits and quotas per user, per tool and per model protect both your systems and your API bill.
Audit trail. Every call is recorded with the user, the tool, the parameters and the result, so you can always tell who changed something and why. Logs stay on infrastructure you control.
Data location. If data must not leave your environment, both the integration and the model can run on your servers or on our own cloud and GPU cloud in Romania, on our own network (AS56951). Read more about private AI.
What you receive. A map of the systems and use cases, the MCP server or API with its source code in your repository, documentation for every tool and permission, an approval flow for write actions, logging and monitoring, automated tests and a short runbook for your team.
Tools we work with. MCP SDKs in TypeScript and Python, PHP for Magento and WooCommerce, PostgreSQL with pgvector, Redis, the OpenAI and Anthropic Claude APIs, open-weight models such as Llama, Mistral and Qwen served with vLLM, and LangGraph when a workflow needs several steps. Platforms such as Odoo, SAP, Microsoft Dynamics, HubSpot or Salesforce are systems we can connect to through their APIs.
HOW WE WORK
From first conversation to a connector in production
Small, safe steps. Read access first, write access when the logs say it is time.
Map systems and use cases
We list the systems involved, the questions people ask every day and the actions they repeat, then pick the first use cases with clear value and low risk.
Design the access model
Tools, scopes, roles, approval rules and the data that must never leave a system — agreed in writing before any code is written.
Build the connectors
MCP server or API, authentication, input validation, rate limits and logging, with the source code in your repository from day one.
Test with real scenarios
Realistic data, edge cases and deliberate attempts to misuse the tools, including prompt injection. The checks are repeatable, so they run again after every change.
Roll out gradually
Read-only first, for a small group of users. Write actions are switched on one at a time, once the logs show the tools are used as intended.
Operate and extend
Monitoring, model and API updates, new tools as your needs grow. We can maintain the integration or hand it over to your team, fully documented.FAQ
Questions we hear before an AI integration
What is MCP and do we need it?
Model Context Protocol (MCP) is an open standard that lets AI assistants and agents use external tools and data through a common interface. It makes sense when you want the same connectors to work across several AI clients, for example Claude, ChatGPT, IDE assistants or your own agent. For a single feature inside one application, a regular API is often simpler.
Will the AI be able to change data in our ERP or online shop?
Only if you decide so. We start with read-only tools. Write actions are separate tools with a narrow scope, validated on the server, and they can require a human approval before they run. Every call is logged with the user, the parameters and the result.
Is our data sent to OpenAI or other AI providers?
It depends on the model you choose. With a commercial API, only the data needed for each request is sent, and we keep that set as small as possible. If data must stay in-house, we run open-weight models on your servers or on our own cloud and GPU cloud in Romania.
We run Magento or WooCommerce. What can AI do with it?
Typical uses: answering staff questions about orders, stock and customers; drafting product descriptions and translations for review; classifying and routing customer emails; natural-language search in the catalogue; summaries of sales and returns. We build the connectors on top of the platform APIs and extensions, without modifying the core.
How is an integration project priced?
After a short discovery we propose either a fixed scope for the first integration or a value-based arrangement, depending on the project. Our AI engagement models page explains the options.
Have a system AI should talk to? Let us look at it.
Tell us which systems you run and what you would like AI to do with them. We will tell you what is feasible, what is risky and where to start.