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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3 solutions
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.
vercel-ai-gateway-agent-eval-harness-for-smb-support-bots
Small businesses deploying AI support bots lack a systematic way to catch regressions before they reach customers. Ad‑hoc manual testing and single‑metric checks miss subtle degradations in answer quality, tool‑use accuracy, and cost creep.An automated regression testing pipeline that evaluates SMB support agents against golden datasets, using Vercel AI Gateway as the LLM backbone and exporting observability to Langfuse.
openai-agent-eval-harness-for-smb-customer-support-quality
SMB customer support agents powered by OpenAI often drift in tone, hallucinate product details, or miss steps, but manual spot-checking doesn't scale as ticket volume grows.Automatically evaluate every production AI support interaction to catch bad answers, hallucination, and policy violations before they affect customers.