Skip to content

Summary

Status

Technical PoC / working prototype, June 2026

Role

System Designer, AI-assisted Prototype Engineer

Stack

Type: Enterprise AI / controlled LLM execution prototype / evidence-backed analytics prototype

Python, FastAPI, LangGraph, Open WebUI, PostgreSQL, Qdrant, MinIO, Redis, Docker Compose

Project value

A technical PoC of controlled LLM execution for evidence-backed analytics. The prototype explores how an executive analytics assistant could be built: the LLM does not answer freely from memory and does not get direct access to data. It operates inside a backend-mediated tool environment over prepared synthetic scenarios.

The prototype validated the architectural idea on single-turn analytical requests. It is not a production platform, not a complete decision-support product, and not a multi-turn conversational agent.

What was implemented

  • chat-like UI based on Open WebUI;
  • experimental LangGraph-based execution flow;
  • backend tools for accessing prepared synthetic data;
  • initial tool registry / tool description concept;
  • synthetic financial and cross-functional management scenarios;
  • single-turn analytical requests;
  • evidence-backed response pattern;
  • basic execution trace / run details;
  • architectural documentation and future direction.

Current limitations

The most important limitation: each new message in Open WebUI was effectively processed as a new independent request rather than continuation of the same analytical session.

What this demonstrates

  • understanding of enterprise AI risks;
  • controlled LLM execution instead of free chat;
  • separation of chat UI and execution layer;
  • tool-mediated analytics;
  • evidence-backed response design;
  • execution trace as a trust/debugging mechanism;
  • ability to build a working prototype quickly;
  • ability to honestly document limitations.