/ AI
Artificial intelligence, wired into your system — not bolted onto it.
We embed LLM assistants, MCP servers and agent automation directly into your existing processes. No separate “AI tool” on the side — the assistant sees your data and can take actions.
AI integration
A large language model (LLM) can talk, but on its own it can't act inside your system. We connect it to your data and functions: the assistant reads from your database, calls your APIs and returns verifiable results — it doesn't guess.
Typically that means an in-app assistant that answers from your documents (RAG), performs tasks via function calling, and remembers context across sessions. We pick the right model for the task and the token budget.
- LLM assistants wired into the data model from day one
- RAG — answers from your documents and database
- Function calling — the assistant acts, not just advises
- Model routing and cross-session memory
LLM · RAG · function calling · MCP · model routing
MCP and agent automation
The Model Context Protocol (MCP) is an open standard through which AI agents call tools and read data in a uniform way. We build you an MCP server that exposes your system as a set of safe, controlled tools.
For bigger jobs we orchestrate several agents at once — with conflict resolution, task ordering and a full log. The same approach drives autonomous dev, test and ops loops. Productized through AIIOtalk.
- Custom MCP servers for your systems
- Multi-agent orchestration (DAG, conflict resolution, federation)
- Autonomous dev / test / ops loops
- Bearer-protected admin agents for content management
MCP · multi-agent · AIIOtalk · Claude Code CLI · SQLite
AI-ready websites (WebMCP)
AI agents increasingly browse the web on behalf of people. We equip your website with a WebMCP server so agents read your services and products and submit an inquiry in a structured way — instead of scraping the HTML.
We also add an llms.txt file and clean structured markup (JSON-LD) so agentic search, AI Overviews and social previews understand you. It's a visible lever, not a technical detail.
- WebMCP server on the page (navigator.modelContext)
- Public read tools + inquiry submission
- llms.txt and JSON-LD for agentic search and AI Overviews
- No impact on regular visitors or load speed
WebMCP · navigator.modelContext · llms.txt · JSON-LD · /.well-known/mcp
AI testing & QA
Regression that would take days by hand, agents do in hours. We expose browser testing over MCP and let AI agents run through flows, forms, accessibility, SEO and responsiveness.
The result is a list of concrete bugs with steps to reproduce — not a vague “something is broken.” In practice we found 130 bugs in a single app this way.
- AI browser testing over MCP (WebTesterAI)
- Regression in hours instead of days
- Coverage: interaction, accessibility, SEO, responsiveness
- Concrete bugs with steps to reproduce
WebTesterAI · Playwright · Chromium · MCP · 75 tools
FAQ
Frequently asked
What is MCP and why does it matter?
The Model Context Protocol is an open standard through which AI agents call tools and read data uniformly. It means the assistant actually performs tasks in your system instead of just advising — safely and under control.
Do I need my own AI model?
No. We use existing models (e.g. Claude or others) and pick the right one for your task based on capability and token budget. You only pay for usage.
Does it work with my existing system?
Yes — that's the point. We wire AI into your existing databases, APIs and processes (including WordPress/WooCommerce); we don't build a separate island.
What about privacy and data?
Data is processed in the EU. Servers (Cloudflare, Resend) are in the EU, GDPR-compliant. Sensitive operations sit behind a bearer token or login; an agent gets only as much access as it needs.
How much does it cost and how does it work?
Per project — first a short call and an estimate. We prepare an integration plan, model selection and budget, then build in phases. One person owns development, testing and deployment.