Citation Detective
Stress-tests your company's internal AI assistant against the trickiest real-employee questions, then hands you a plain-English report pointing to the exact documents and chunks it gets wrong.
small business owners running an AI helpdesk or internal SOP chatbot
- Synthetic-question bank that mimics how real users ask, including ambiguous phrasings that break retrieval
- Side-by-side: question, retrieved chunks, the AI's answer, and a trust score per response
- Plain-language fix instructions like "merge sections 5.3 and 5.4" or "rename the '90-day rule' page"
- Weekly digest shared via email or Slack to the doc owners
As small ops rush to deploy RAG-based chatbots on internal docs, retrieval failures (chunking, ambiguous matches) recur everywhere — and there is no audit tool aimed at non-technical owners.
Stanford research cited via Galileo shows 17-33% hallucination rates in production RAG, and 77% of enterprises fear AI hallucinations costing millions — recurring accuracy failures are a real, quantified pain point for internal deployments.7 Top Rag Evaluation Tools | Galileo ↗Top 11 AI Hallucination Detection Tools for Enterprise ↗
RAG eval market is crowded with 7+ engineering-focused platforms (Galileo, Ragas, DeepEval, Maxim, Arize Phoenix, LangSmith, TruLens, Braintrust), but none are aimed at non-technical SMB owners of internal helpdesk bots — a genuine SMB niche exists inside a saturated enterprise category.Best RAG Evaluation Tools 2026: 7 Ranked ↗7 Top Rag Evaluation Tools | Galileo ↗
Existing players price for enterprise (Galileo's Luna-2 at $0.02/1M tokens for enterprise-scale monitoring); no evidence of SMB-priced audit tools, and small biz owners typically spend under $50-100/mo on chat widgets, making willingness-to-pay unproven.7 Top Rag Evaluation Tools | Galileo ↗
RAG remains the dominant internal-knowledge pattern in 2026 guides and will persist as long as internal docs exist; retrieval/grounding failures are an enduring class of bug, not a trend.Build a RAG chatbot for your business knowledge base in 2026 ↗RAG Chatbot for Internal Knowledge Bases - M3DS AI ↗
Requires building adversarial-question generation, RAG-output evaluation with chunk attribution, and integrations across heterogeneous RAG stacks — doable but non-trivial, and chunk-level diagnosis depends on the bot exposing retrieval traces many SMB setups don't have.