Solutions

Janea Systems partnered closely with the client's Product, Engineering, and Research teams to enhance the intelligence, performance, and scalability of their AI stack. Solutions deployed include:

• Expanded the LLM stack with the LangGraph framework to handle complex queries, preventing infinite loops and improving overall performance.
• Enabled on-premises autonomous AI deployment for sensitive information processing, allowing AI agents to chain together multiple actions while maintaining data security.
• Introduced load shedding and connection pooling to eliminate database throughput bottlenecks and handle traffic spikes gracefully.
• Built a RAG pipeline with LangChain, LangGraph, DSPy, AWS Bedrock, and Datadog to encode frequent query results into a specialized vector database - a system that enables semantic search of previously answered questions.
• Established Langfuse alongside Datadog; enabled telemetry for on-premises agents; developed customer usage understanding pipeline, facilitating error detection and analysis.
• Created infrastructure templates for faster and more consistent provisioning.
• Migrated and scaled ML inference and training from on-premises to the cloud, unlocking greater computational resources.
• Benchmarked storage solutions to enable training on datasets exceeding 1 billion records.

Together, these improvements transformed the platform from a capable MVP into a production-ready system: faster, more resilient, and easier to operate at scale.

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If you’re building an LLM product and need to take it to production, we can help. Agents, RAG, token cost reduction, end-to-end observability, and more: let’s talk about how Janea Systems can strengthen your AI stack.