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.
Filtering by
3 solutions
perplexity-rag-eval-suite-for-smb-knowledge-bases
SMBs that deploy internal RAG bots for employee or customer support find their answers drift as documents change. Without automated evaluation, they only discover quality regressions through user complaints, with no reproducible benchmark and no way to track LLM judging costs.Continuously evaluate your small business RAG knowledge base using Perplexity’s LLM-as-judge, heuristic metrics, and cost-tracked CI gates from REAA’s eval packs.
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.
vllm-agent-eval-harness-for-fine-tuned-model-quality
SMBs that fine-tune open models locally lack a structured way to verify model quality before production, exposing them to regressions and failed customer interactions.Automated CI/CD-quality evaluations for locally-hosted fine-tuned LLMs using vLLM with LLM-as-judge and cost tracking.