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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4 solutions
aws-bedrock-rag-eval-harness-for-smb-customer-support-bots
SMB support teams rely on RAG chatbots to handle customer questions, but hallucinations or irrelevant answers slip through unnoticed, damaging trust. They have no systematic way to continuously measure answer quality and catch regressions before customers do.Automatically score RAG answer quality, track evaluation costs, and block deployments when your AI support bot’s accuracy dips.
vllm-agent-quality-gate-for-on-prem-smb-support-bots
An SMB running on‑premises support agents on vLLM lacks systematic regression testing after model updates or prompt changes. Manual conversation review is slow, and a bad deployment can degrade customer satisfaction before anyone notices.Automated regression testing for self‑hosted LLM agents, with CI gates that block deployment when support‑bot quality drops.
azure-ai-agent-eval-harness-for-smb-support-qa
Small businesses deploying Azure AI chatbots for customer support struggle with maintaining consistent answer quality as prompts, models, and knowledge bases change. Manual testing is time-consuming and unreliable, leading to wrong answers, inappropriate tool calls, and surprise cost overruns.Automated quality gates for Azure AI-powered support agents, catching regressions in tool use, answer quality, and cost before they reach customers.
databricks-agent-eval-harness-for-smb-support-bots
SMBs deploying AI support agents struggle to catch regressions before they impact customers, leading to poor responses and handoffs. Manual QA is costly and inconsistent.Automated regression testing for SMB customer support agents, running on Databricks with BrainsTrust analytics.