Solutions
Production-grade solutions that turn our open-source packages into deployable AI systems for specific business problems. Pick one, follow the DIY tutorial to see how it's done, download the examples and deploy them on your own infrastructure — for free — or tell us which ones you want customized and deployed.
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2 solutions
databricks-llm-observability-suite-for-smb-ai-operations
SMBs using Databricks for AI workloads have no easy way to monitor spending, latency, or error rates across multiple models, leading to bill shock and debugging blind spots.Gain end-to-end visibility into every LLM call on Databricks, from token usage to cost, with ready-made dashboards and alerts.
agnostic-agent-feedback-loop-for-fine-tuning
A boutique marketing agency uses an AI agent to generate ad copy. The agent often misses the brand voice, requiring manual edits. The agency wants to capture these corrections and use them to fine-tune a smaller, cheaper model that better matches their style. They need a system that logs agent outputs, captures user feedback (accept/reject/edit), and periodically exports a clean dataset for fine-tuning. This reduces reliance on expensive API calls and improves quality over time.Collect agent decisions and user corrections to build a dataset for model fine-tuning.