Service
AI that's wired into what you already run, with limits that are actually enforced.
Workflow automation with n8n, retrieval over your own documents (RAG), and AI features built directly into your product via the Claude API — each scoped to a specific task, with rate limits and spend caps decided up front, not discovered in a bill.
Scoped to one task, not a general chatbot.
Every integration is wired to a specific workflow or document set, with rate limits and spend caps decided before it ships — not discovered in a bill.
What's included
Workflow automation
n8n flows connecting the tools you already use — forms, spreadsheets, email, your CRM — without a developer on call for every change.
Retrieval over your documents
A RAG pipeline that answers from your own material, with sources, instead of a model guessing from general knowledge.
API-layer AI features
AI capability built into your product through the Claude API, scoped to one real task rather than a general-purpose chatbot bolted onto everything.
Agent and MCP integration
Expose specific internal tools or data to AI assistants through the Model Context Protocol — read-only by default, authenticated, and limited to what the task needs.
Guardrails and governance
Per-IP rate limits, a hard monthly spend cap, logged tool calls and no unscoped tool or data access — decided and documented before anything ships.
Human review where it matters
Workflows that draft or recommend, with a person approving anything that sends, spends or changes a record.
How it's built
Stack
- n8n
- Claude API
- Python
- FastAPI
- Model Context Protocol
Questions people actually ask
Is this a chatbot?+
Usually not. Each integration is scoped to one task or document set — answering from your files, triaging a form, drafting a reply for review. A general-purpose chatbot is rarely the right first step.
What stops an AI feature from running up a bill or being abused?+
Rate limits, a hard monthly spend cap and narrowly scoped tool access, agreed and written down before launch.
What is MCP and do I need it?+
The Model Context Protocol is an open standard for letting AI assistants call specific tools and read specific data. You only need it if you want assistants to work with your internal systems; many projects are fine with a plain API integration.
Will it replace people?+
The aim is removing repetitive steps, not removing judgement. Anything consequential keeps a human approval step.