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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13 solutions · page 1 of 2
ollama-agent-eval-harness-for-on-prem-smb-support-qa
SMBs running on-prem LLMs with Ollama lack automated QA to catch regressions in agent performance before customers encounter errors, leading to support drift and quality degradation.Run continuous quality evaluation on local AI agents using Ollama, with regression gating and cost tracking, all from a CLI.
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.
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.
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.
xai-grok-agent-eval-harness-for-smb-support-qa
Small businesses using xAI Grok for customer support agents have no automated way to verify response quality across prompt changes, model updates, or conversation scenarios. Manual spot-checks miss regressions, leading to incorrect answers, safety issues, and lost trust.Continuously evaluate your xAI Grok-powered customer support agents to catch regressions before they affect 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.
perplexity-agent-eval-harness-for-smb-ai-quality-assurance
Small businesses deploying AI chat or email agents struggle to know when an update breaks quality—manual testing doesn't scale, and proprietary LLM judges are expensive to use at volume.Run continuous, automated evaluations of your customer‑facing AI agents using Perplexity as a neutral LLM judge, with version‑gated prompt promotions.
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.
anthropic-eval-harness-for-agent-quality-assurance
SMBs shipping customer‑support or sales agents on Anthropic’s models see quality drift over time—toxic phrasing, hallucinated facts, or missed tools—but lack a repeatable test suite to catch these regressions before they reach users.Continuous regression testing and safety scoring for Anthropic‑powered agents, with automated quality gates before any customer‑facing deployment.