Enterprise-Grade Universal Analytics & AI Platform
Transform Any Data into Actionable Insights with AI
- $250B+ Global Business Intelligence Market by 2030
- 15%+ CAGR in AI-powered analytics
- Growing demand for multi-LLM platforms
- Enterprise shift to cloud-native analytics
- Multi-LLM Support: YandexGPT, OpenAI, Anthropic
- Enterprise-Ready: RBAC, multi-tenant, audit logs
- Open Source Core with Enterprise tiers
- Template Marketplace for recurring revenue
| Feature | TITAN | Competitors |
|---|---|---|
| Multi-LLM Integration | ✅ YandexGPT, GPT-4, Claude | ❌ Single vendor lock-in |
| Anomaly Detection | ✅ AI-powered | |
| Template Marketplace | ✅ Built-in ecosystem | ❌ Not available |
| Self-hosted Option | ✅ Full control | ❌ SaaS only |
| RBAC & Multi-tenant | ✅ Enterprise-ready | |
| Real-time Streaming | ✅ Supported |
TITAN Analytics Platform is a powerful, enterprise-ready platform for data collection, AI-powered analysis, and visualization from multiple sources. The system combines web scraping, multi-LLM processing, intelligent search, and automated report generation.
- 🔍 Semantic Search — Synonym-aware search with relevance ranking
- 🤖 Multi-LLM AI — YandexGPT, OpenAI GPT-4, Anthropic Claude
- 📊 Interactive Visualization — Plotly-powered dashboards
- 📝 Export Formats — PDF, Word, Excel, JSON, CSV
- 🎬 Multimedia — YouTube video analysis via subtitles
- 🔌 Extensible — Modular processor architecture
- 📚 Template Library — Ready-to-use analytics templates
- 🎨 Report Builder — Drag-and-drop visual editor
- 🔐 RBAC — Role-Based Access Control with custom permissions
- 🏢 Multi-Tenant — Organization-level data isolation
- 📊 Anomaly Detection — AI-powered pattern analysis
- 💡 Recommendation Engine — Smart suggestions
- 📈 Trend Analysis — Emerging topic detection
- 🔗 Clustering — Automatic content grouping
- 📋 Audit Logs — Enterprise compliance tracking
- 🏷️ License Tiers — Community, Professional, Enterprise
Challenge: A retail company needed to monitor competitor pricing across 500+ products daily.
Solution: TITAN with automated web scraping, AI-powered price trend analysis, and anomaly detection.
Results: 70% reduction in manual research time, $2M+ savings from competitive pricing insights.
Challenge: Research institution needed to review thousands of academic papers for meta-analysis.
Solution: TITAN with PDF parsing, AI summarization, and citation network visualization.
Results: 80% faster literature reviews, identified 15+ previously missed relevant studies.
Challenge: Track brand mentions across news, social media, and video platforms.
Solution: TITAN with multi-source collection, sentiment analysis, and real-time alerting.
Results: 24/7 automated monitoring, 90% faster crisis response time.
Challenge: Track regulatory changes across multiple jurisdictions.
Solution: TITAN with government website monitoring and AI-powered impact assessment.
Results: 100% regulatory change coverage, 60% reduction in compliance review time.
Challenge: Create structured learning paths from diverse educational content.
Solution: TITAN with content clustering and personalized learning recommendations.
Results: 40% improvement in course completion rates.
- Framework: Python 3.11+ / Django 4.2
- Database: PostgreSQL 15
- Task Queue: Celery + RabbitMQ
- API: Django REST Framework + OpenAPI
- Primary LLM: YandexGPT
- Alternative LLMs: OpenAI GPT-4, Anthropic Claude
- NLP: LangChain, RuWordNet, PyMorphy3
- Data Processing: Pandas, NumPy
- Framework: React 18 + TypeScript
- Visualization: Plotly.js
- UI: Tailwind CSS + Radix UI
- State: TanStack Query
- Containerization: Docker, Docker Compose
- Orchestration: Kubernetes (Helm charts)
- Web Server: Nginx
- CI/CD: GitHub Actions
TITAN Analytics Platform
│
├── 🎨 Frontend (React + TypeScript)
│ ├── Dashboard & Analytics
│ ├── Template Marketplace
│ ├── Report Builder (Drag & Drop)
│ └── Admin Panel
│
├── ⚙️ Backend (Django REST API)
│ ├── 📊 Data Processing Pipeline
│ ├── 🤖 AI Processing Layer (Multi-LLM)
│ ├── 🔍 Search Engine
│ ├── 🔐 Enterprise Layer (RBAC, Multi-tenant)
│ └── 🔌 Processor Registry (11 processors)
│
├── 💾 Data Layer
│ ├── PostgreSQL
│ ├── File Storage
│ └── Search Index
│
└── ⚡ Task Queue (Celery + RabbitMQ)
git clone https://github.com/NickScherbakov/Severstal_ICT2024.git
cd Severstal_ICT2024
cp .env.example .env
docker-compose up -dhelm install titan ./deploy/helm/titan-analytics \
--namespace titan --create-namespace# Backend
cd backend && pip install -r requirements.txt
python manage.py migrate && python manage.py runserver
# Frontend
cd titan_frontend && npm install && npm run dev| Processor | Description | Enterprise |
|---|---|---|
| Sentiment Analysis | Emotion detection | ❌ |
| Network Graph | Entity relationships | ❌ |
| Timeline | Event extraction | ❌ |
| Comparison | Multi-aspect analysis | ❌ |
| Forecast | Predictive analytics | ❌ |
| Table | Data processing | ❌ |
| Anomaly Detection | Pattern detection | ✅ |
| Recommendation | Smart suggestions | ✅ |
| Trend Analysis | Topic detection | ❌ |
| Clustering | Content grouping | ❌ |
| Summary | Summarization | ❌ |
| Feature | Community | Professional | Enterprise |
|---|---|---|---|
| Price | Free | $99/mo | Custom |
| Users | 5 | 25 | Unlimited |
| Reports/month | 50 | 500 | Unlimited |
| Anomaly Detection | ❌ | ❌ | ✅ |
| Priority Support | ❌ | ❌ | ✅ |
We welcome contributions! Fork the repository, create a feature branch, and submit a pull request.
MIT License - see LICENSE for details.
- GitHub: NickScherbakov/Severstal_ICT2024
- Website: TITAN Analytics
Built with ❤️ by the TITAN Analytics Team
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