A personal AI assistant built in Python that combines leading language models (GPT, Claude, Gemini) with dynamic model selection per task. Designed for on-premise deployment on the client's own infrastructure — full control over the data and full confidentiality. Available through two interfaces: a fully featured web app and a Telegram integration. The defining feature is finely tuned personal logic and an AI "character" matched to the client's workflows and communication style.
Client: private individual · Platform: Telegram + web interface · Category: automation
Technology stack
- Language: Python.
- LLMs: GPT, Claude, Gemini with dynamic model selection per task.
- Deployment: on-premise, on the client's infrastructure (data confidentiality).
- Interfaces: web interface + Telegram integration.
- Integrations: external APIs, SSH, Google Sheets, web browser, file system.
Full list of implemented features
The assistant covers more than 100 operations. Key areas:
🧠 Intelligence and language models
- A symbiosis of GPT, Claude and Gemini with dynamic model selection per task
- Personal logic and AI "character" tailored to the client
- Advanced search, content generation, data analysis
- Image generation
⚙️ Automation and system operations
- Shell command execution
- File management
- Web browser automation
- PDF processing
- Server monitoring over SSH
- Google Sheets workflows
💬 Access channels
- A fully featured web interface for complex operations
- A Telegram integration — control from anywhere in the world
🛡 Security
- On-premise deployment on the client's infrastructure
- Full control over all processes and data
Workflow
1. Requirements analysis and concept — diving into the client's business processes, shaping the assistant's concept and its character. 2. Architecture design and tech choices — system architecture, LLM selection (GPT, Claude, Gemini), the stack for on-premise deployment. 3. AI core development and LLM integration — the core Python code and dynamic model selection mechanisms. 4. Building the interfaces (Telegram and Web) — the web interface and the Telegram API integration. 5. Logic, character and feature tuning — personalising responses and logic, rolling out 100+ specialised features. 6. Testing, debugging and optimisation — end-to-end testing, performance and security. 7. Deployment, training and support — on-premise deploy, staff training, technical support.
Results
| Metric | Value |
|---|---|
| Reduction in time spent on routine tasks | 45% |
| Increase in decision-making speed | 2.5x |
| Process automation level | 90% |
| Reduction in operational costs | 20% |