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Servers and GPU Infrastructure

We operate a self-hosted Large Language Model (LLM) ecosystem, selecting models such as DeepSeek R3 and Mistral based on task-specific requirements. These models power:

  • Conversational interactions
  • Complex reasoning operations
  • Advanced classification tasks

Our infrastructure runs on RunPod’s scalable GPU resources, within a robust Docker environment. This setup allows us to:

  • Dynamically adjust GPU instance count in response to real-time demand.
  • Ensure optimal performance by efficiently allocating resources.

For tasks requiring extensive context processing (e.g., analyzing lengthy articles), we strategically integrate centralized LLMs (O3 and Anthropic). This approach is specifically utilized for processing large-scale data collected through our Anatomy of Luigi scraping system.

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Embedding Models

We prioritize data privacy and security by running our own suite of embedding models rather than outsourcing user data to external providers like OpenAI. These models include:

  • Text embeddings
  • Contextual embeddings
  • Reranking models Our primary embedding framework is built using models from Nomic AI, ensuring high-quality vector representations while maintaining full control over data processing.

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Retrieval-Augmented Generation (RAG) Operations

To support scalable data retrieval and indexing, we operate a distributed TimescaleDB infrastructure across multiple geographical regions. This architecture ensures:

  • High availability and redundancy
  • Optimized performance for AI-driven data queries

By leveraging TimescaleDB’s seamless integration with PostgreSQL, our RAG pipeline supports:

  • Automated document embeddings generation
  • Advanced reranking model processing
  • In-database execution of complex LLM queries

This integrated approach significantly enhances data retrieval efficiency and query performance.

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Conversational History Management

We utilize a tiered data storage approach to balance speed, scalability, and privacy:

  • Redis Instances – Handle real-time processing of user interactions, ensuring ultra-fast response times.
  • MongoDB Atlas – Provides optimized long-term storage, supporting efficient indexing and retrieval.

Privacy-First Approach

  • User data is stored as heuristic fingerprints —you exist as an encrypted ID within our system.
  • Conversations remain private unless explicitly shared via our secure link-sharing feature. This privacy-first architecture ensures robust data protection while maintaining seamless user experience.

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Agent Monitoring & Performance Analytics

Our AI agents are continuously monitored through a self-hosted Elasticsearch cluster, enabling comprehensive real-time analytics on:

  • System health indicators
  • Token processing efficiency
  • Term frequency and usage patterns

By maintaining detailed agent telemetry, we can:

  • Identify performance bottlenecks early
  • Optimize model efficiency and responsiveness
  • Continuously fine-tune our self-hosted models for long-term improvement

This observability-driven infrastructure ensures our AI ecosystem remains scalable, efficient, and highly reliable.