Example project · Ruby Peaks Digital Solutions
Document AI with RAG
Why retrieval-augmented generation beats “just chat with a model,” how companies use it for SOPs and training, and a live demo on US Metal/Nonmetal mine regulations (30 CFR).
Why RAG?
A plain chatbot answers from what it was trained on. That is fine for general knowledge — and risky for your procedures, safety rules, or regulated content. Models invent details, miss revisions, and cannot cite the PDF your team actually approved.
Retrieval-augmented generation (RAG) flips the order: find the relevant passages in your documents first, then ask the model to explain them. The result is faster lookup with answers tied to real sources.
That is the same pattern behind our public Metal/Nonmetal regulations assistant— and the same approach we use when companies want AI over SOPs, training packs, and internal manuals.
How RAG works
Four steps. No magic — just search plus generation, wired carefully.
1. Index your documents
SOPs, training manuals, procedures, or regulations are chunked and stored with embeddings so the system can find relevant passages later.
2. Retrieve on every question
When someone asks a question, the system searches that private index first and pulls the most relevant excerpts — not a random guess from memory.
3. Generate with citations
The language model answers using those excerpts, so responses stay grounded in your materials and can point back to the source section.
4. Keep humans in control
Access controls, bot protection, and clear “not legal advice / verify against the source” messaging keep the tool useful without pretending to be an oracle.
How this helps companies
If your knowledge lives in shared drives, binders, or LMS exports, RAG turns it into a practical Q&A layer for the people who need it on shift.
SOPs & procedures
New hires and supervisors ask “what’s the process?” and get the right section instead of hunting through PDFs or Slack threads.
Training materials
Turn handbooks and lesson packs into an always-on study assistant that quotes the curriculum, not invented tips.
Internal knowledge bases
Policies, safety docs, and ops notes become searchable in plain language — with links back to the approved document.
Faster answers, fewer mistakes
People stop relying on outdated copies or tribal knowledge. The model is constrained to what you indexed.
Live example: mine regulations
We built a public demo of this stack for US Metal & Nonmetal mine regulations (30 CFR Parts 56/57 and related standards such as training, HazCom, and silica). It is useful for mines in Nevada and other states — not a single-state-only tool.
Under the hood it indexes official eCFR Title 30 content into a vector store, retrieves matching sections for each question, and streams an answer with citations. The public chat is protected with Cloudflare Turnstile. An optional MCP server lets Cursor agents look up live eCFR sections while developers work.
Swap the corpus — SOPs, handbooks, or your own manuals — and the same architecture becomes an internal assistant for your company.
Important
The demo is a research assistant, not legal advice and not an official MSHA product. Always verify critical decisions against the current eCFR and your compliance team. The same caution applies to any SOP or policy chatbot we build for you.
Ready for AI over your documents?
We design and ship RAG chat for SOPs, training materials, and regulated corpora — starting from patterns proven in our public regulation demo.
Related: About Ruby Peaks, Web development, Via Wireless Systems