We index documents (RAG on BGE-M3), integrate permissions, and team context. Instead of generic responses, you get fact-based answers from your materials—with a link to the source. This forms the foundation for agents and internal search.
cashcrown // ai.agents
Company GPT
assistant on your company knowledge, on-prem.
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Problem
You need “Company GPT”, but building it in-house gets stuck on integrations, maintenance and lack of time — and the result tends to be fragile and hard to scale.
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What we build
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Process
- Process map
We break the real flow down into steps, data and decision points.
- Agent + tools
We define the scope, tools and gates; we wire in the LLM router.
- Loop with proof
Plan → execution → verification (log/test), with rollback.
- Deployment and oversight
Observability, alerts, a gradual widening of autonomy.
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Stack
AI gateway / routerLangGraphMCPn8nFastAPIkolejki zadań
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Related
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FAQ
How much does this service cost and when does it pay off?#
We work in ranges that depend on scope — we start with a fixed, low-cost pilot so you see the value before a larger investment. When the service takes back a dozen to several dozen hours of work a month, it usually pays off in 2–4 months. Calculate your concrete return in the ROI calculator; we don't quote fixed prices upfront because they depend on the actual scope.
Does it integrate with our systems and is it GDPR-compliant?#
Yes. We connect to your systems (CRM, email, databases, n8n) through controlled, allow-listed tools, and irreversible actions require confirmation (a human-gate). We mask PII before anything leaves for the cloud and handle sensitive paths locally — GDPR and AI Act compliance designed in from the start.
How do we start?#
With an audit and a pilot on one narrow scope with a measurable result. We show a working service before we ask for your trust — then expand the scope step by step. More on the approach: how to choose the first process.