You will get Self-Hosted AI Sales & Support Assistant Powered by Your Website Data
Project details
Self-Hosted AI Sales & Support Assistant Powered by Your Website's Data (RAG): 24/7 Automation&Conversion Growth
A š³šš¹š¹š š¼š½š²š»-šš¼ššæš°š² solution with no recurring API fees for ChatGPT, Claude, or Gemini.
š„š²ššš¹šš & šššš¶š»š²šš ššŗš½š®š°š
The implementation of the AI assistant led to significant and measurable improvements:
Reduced Support Workload by 60-80% (LLM answer accuracy is 60-80%).
Accelerated First Response Time to 3-5 Seconds.
Cost Optimization.
Privacy.
**Technology Stack (100% Open-Source)**
AI & Machine Learning: RAG, Ollama (Llama 3, Gemma)
Vector Database: ChromaDB
Customer Support Platform: Chatwoot
Backend: Python
Deployment: Docker, Self-hosted Private Cloud Server (Hetzner)
How it works:
A backend system automatically scans the client's website (WordPress+WooCommerce database) to collect information about products, descriptions, prices, and knowledge base articles (FAQ).
This information is converted into a vector database (ChromaDB), enabling lightning-fast semantic search.
When a user asks a question in the chat, a Self-hosted AI model finds the most relevant information in the vector database and generates an answer.
A š³šš¹š¹š š¼š½š²š»-šš¼ššæš°š² solution with no recurring API fees for ChatGPT, Claude, or Gemini.
š„š²ššš¹šš & šššš¶š»š²šš ššŗš½š®š°š
The implementation of the AI assistant led to significant and measurable improvements:
Reduced Support Workload by 60-80% (LLM answer accuracy is 60-80%).
Accelerated First Response Time to 3-5 Seconds.
Cost Optimization.
Privacy.
**Technology Stack (100% Open-Source)**
AI & Machine Learning: RAG, Ollama (Llama 3, Gemma)
Vector Database: ChromaDB
Customer Support Platform: Chatwoot
Backend: Python
Deployment: Docker, Self-hosted Private Cloud Server (Hetzner)
How it works:
A backend system automatically scans the client's website (WordPress+WooCommerce database) to collect information about products, descriptions, prices, and knowledge base articles (FAQ).
This information is converted into a vector database (ChromaDB), enabling lightning-fast semantic search.
When a user asks a question in the chat, a Self-hosted AI model finds the most relevant information in the vector database and generates an answer.
AI Development Type
Knowledge RepresentationAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$4,999
|
Standard
$5,999
|
Advanced
$7,999
|
|---|---|---|---|
| Delivery Time | 14 days | 20 days | 25 days |
AI Model Integration | |||
Detailed Code Comments | |||
Knowledge Graph | - | - | - |
Model Documentation | - | - | - |
Ontology | - | - | - |
Source Code | |||
Taxonomy | - | - | - |
About Mike
AI Product manager, Product Owner, Technical Project Manager
Kiev, UkraineĀ - 12:22 am local time
š š” Technology leader for SMB with 20+ years in IT and a PhD in Computer Engineering. Combine infrastructure depth (networks, data centers, servers) with full product ownership (ERP, CRM, SFA, analytics, mobile and web platforms, e-commerce).
š š”8 years as CTO/CIO at an FMCG company ā built analytics systems, ERP/CRM, managed complex integrations.
š š”Last year ā AI-native focus: 2 production AI products on RAG and LLM, AI-assisted development and vibe-coding as standard practice. Build and manage distributed teams of up to 15+ people. Work end-to-end: strategy, architecture, hiring, delivery, integrations, training, operations.
š¼ Core Competencies
š¼ āØAI & AI-Native Development: AI agents, RAG systems, Embeddings, LLM bots, AI sales
assistants, AI-native development, AI integration into enterprise systems
š¼ āØTechnology Leadership: IT strategy, Technical architecture, Stack and vendor selection, Hiring and team building, Distributed team management (15+), Process design, Vendor management
š¼ āØProduct & Project Management Product discovery, PRDs and technical specifications, Roadmapping, Full development lifecycle, Release management, Agile (Scrum, Kanban), UX/UI logic, Monetization, Business models
š¼ āØEnterprise Systems ERP, CRM, SFA, Logistics and fleet management, Price monitoring, Retail outlet mapping, Secondary sales analytics, IP telephony, Warehouse management
š¼ āØAnalytics & BI Data warehouses and PowerBI dashboards, Analytical systems, Management reporting
š¼ āØDevelopment & Release Web development, Mobile development (iOS / Android), Telegram bots, Streaming services, QA, App Store and Google Play publishing (15+ apps)
š¼ āØInfrastructure & R&D Network administration (Cisco), Data centers, Server administration (Windows Server, FreeBSD, Linux), Virtualization (VMware ESXi)
š¼ āØDomain Expertise FMCG (SFA, secondary sales, price monitoring, executive dashboards), Business automation, EdTech, B2B systems, Mobile apps, Startups
ā“ļøSelected Projects
ā“ļø ā”ļøAI-Native Focus
Last year ā strong focus on AI as both the core of products and a daily working tool.
Workspace for psychologists and coaches (production) A platform-workspace for therapists and their clients with AI integrated at multiple levels:
ā RAG system over the therapist's knowledge base ā relevant materials surfaced during sessions
ā AI-driven recommendations of exercises and materials for clients based on session context
ā Automatic transcription and note generation from sessions
ā AI assistant for the therapist during and between sessions
ā“ļø ā”ļøEnterprise RAG knowledge search (production pilot) An internal tool for searching across corporate documentation, books, and manuals. Architecture designed to scale to the full corpus of corporate knowledge.
ā“ļø ā”ļøAI-native development as standard practice I use AI as a standard tool for development and research: vibe-coding, prototyping, source analysis, and architecture design. Example: an analytics system for calculating profitability of a generative power station (CHP unit) ā a dashboard aggregating multiple data sources ā was built end-to-end with an AI-assisted approach, from research to production.
ā“ļø ā”ļø RAG models, local or server less LLMs , local LLM portals, Ollama, OpenWebUI, ChromaDB, DeepSeek, Llama 3, and Gemma 3.
Steps for completing your project
After purchasing the project, send requirements so Mike can start the project.
Delivery time starts when Mike receives requirements from you.
Mike works on your project following the steps below.
Revisions may occur after the delivery date.
Client purchases the project and sends requirements.
Technical Audit
I will analyze your WordPress/WooCommerce site structure, database schema, and current Chatwoot setup.