# Askive — Sovereign Multi-Agent Document Platform

A private RAG chatbot for compliance and policy archives. Citations on every answer. No data leaves the corporate firewall.

## Key facts

- **Domain:** KNOWLEDGE & COMPLIANCE
- **Status:** Delivered as a sovereign-deployment RAG platform inside a public-sector engagement
- **Use when:** Legal, compliance, or regulated-industry clients need conversational query over their archive without using public LLM endpoints.
- **Reference engagement:** A national tourism authority in the GCC

## The problem

Public-sector and regulated organisations cannot ship document content to public LLM endpoints. The default chatbot architecture violates that constraint on day one. Internal staff still need answers in minutes, not days.

## The approach

Retrieval-augmented generation with a private vector store and an LLM that respects data residency. Every response surfaces the source PDFs and page numbers it drew from. Operations staff get a familiar chat interface.

## The architecture

ChromaDB and HuggingFace embeddings inside the customer environment. Generation against Gemini Pro with retrieval-augmented prompts. A Next.js 16 frontend with streaming responses, document upload and management, and chat history. FastAPI service ties it together. Source citations stitched into every response.

## The outcome

A working internal Q&A surface for the corporate archive, with source citations on every answer. A pattern that ports directly to other regulated archives: legal, compliance, internal policy, regulator filings.

## ATI shape

- Predictive intelligence — Retrieval scoring as the predictive layer over the archive
- Agentic execution — LLM generation grounded in retrieved citations, not free invention
- Secure data — Vector store and embeddings stay on-prem; no data leaves the perimeter
- Actionable outcomes — Cited answers staff can hand to a regulator without rework

## Tech stack

- Next.js 16
- FastAPI
- LangChain
- ChromaDB
- HuggingFace Embeddings
- Google Gemini Pro

## Impact

- Local — Vector store and embeddings stay on-prem
- Cited — Every answer points to its source PDF and page
- Streaming — Real-time response generation

## Related

- [All accelerators](https://levent.ai/accelerators/)
- [Next: Smarequ](https://levent.ai/accelerators/smarequ/)

Metrics on this page are estimated and expected improvements describing the design intent of the accelerator. Real-client delivered metrics stay in private decks; see https://levent.ai/ai-content-policy/ for the abstraction policy.

---

**Canonical URL:** https://levent.ai/accelerators/askive/