AI Chatbot & Virtual Assistant

In one sentence

A private, on-prem customer assistant that answers from your own documents, streams replies in real time, and keeps data inside your infrastructure.

Who is it for?

Teams that need a secure, company-specific assistant (support, sales, HR, IT) without sending data to third-party clouds.

Business Challenge → Solution

Challenge:

Customers and employees can't find answers fast; docs live in PDFs, emails, and wikis; public chatbots hallucinate and raise data-privacy concerns.

Solution:

A document-grounded AI assistant (RAG) that indexes your content and serves reliable, sourced answers via a lightweight web widget or internal portal.

Outcome:

Faster resolution, lower ticket volume, consistent answers, and full control over data.

Core Value Propositions

📚

Grounded answers (RAG)

Pulls citations from your PDFs/pages before generating a response.

🏠

Local by design

Runs on your private server or VPC (Docker), no external data sharing.

⚡

Real-time streaming

Tokens appear instantly for a "typing" feel.

🌍

Multilingual

Auto-detects Hungarian/English (extendable).

🔒

Secure access

JWT-based auth, CORS allow-list, rate-limits, request validation.

📈

Scalable

FAISS vector index + Redis caching; add more models or shards as you grow.

How It Works (At a Glance)

Zero-trust posture: everything runs behind Traefik/Nginx with TLS; only your domain(s) can call the API.

1

Ask

User submits a question in the chat widget

2

Retrieve

System finds relevant chunks from knowledge base (FAISS)

3

Generate

LLaMA 3 drafts an answer using retrieved context

4

Stream

Reply is streamed to UI with source citations

5

Learn

Optional conversation memory and feedback loop

Key Features at a Glance

Document ingestion

PDFs, markdown, HTML pages, etc.

Citations & confidence

Show sources; tune relevance thresholds.

Conversation memory

Opt-in history for more natural dialogues.

Widget for WordPress/Divi

Simple embed, brandable UI.

Ops-ready

Health checks, logs, basic metrics, graceful timeouts.

Admin tools

Index refresh, model/version pinning, allow/deny lists.

Integrations & Deployment

Backend

FastAPI with LangChain (RAG pipeline)

Model runtime

Ollama (LLaMA 3 family; GPU optional for speed)

Vectors

FAISS (fast semantic search)

Cache

Redis (sessions, rate limits)

Infra

Docker/Compose, Traefik TLS reverse proxy, Linux server

Frontend

Lightweight JS widget; easy drop-in for WordPress/Divi

Security & Compliance

🔒 TLS/HTTPS everywhere
🔒 JWT authentication, CORS allow-list
🔒 Rate limiting and abuse protection
🔒 Input validation (Pydantic)
🔒 No PII exfiltration: data stays local; logs are yours
🔒 Auditability: request/response logging with timestamps (configurable)

Typical Results

✓ 30–60% fewer repetitive tickets in the first weeks
✓ 2–5s average time-to-first-token on standard hardware
✓ Higher answer consistency vs. human-only triage

(Exact numbers depend on content quality and volume.)

Technology Stack (Enterprise-Ready)

Python (FastAPI) · LangChain · Ollama (LLaMA 3) · FAISS · Redis · Docker/Compose · Traefik/Nginx · Linux (Debian)

What We Deliver in an Implementation

→ Content audit → what to index, what to exclude
→ Secure deployment (Docker, TLS, CORS, JWT, rate limits)
→ Indexing & evaluation (RAG quality checks, thresholds)
→ Branding & embed (widget styles, welcome prompts)
→ Pilot & training (admin + editors)
→ Support & SLAs (updates, monitoring, improvements)

Frequently Asked Questions

Can we keep all data on-prem?

Yes. The system is designed to run locally or in your private cloud.

Does it hallucinate?

The RAG pipeline reduces hallucinations by requiring retrieved sources. Thresholds are tunable.

Can we use a GPU?

Recommended for lower latency; CPU-only works for lighter loads.

How do we add content?

Upload/point to approved sources; the indexer updates FAISS on schedule or on demand.

Does it support Hungarian?

Yes—auto language detection for HU/EN out of the box.