RAG Systems

RAG Knowledge Hub

Retrieval-augmented knowledge system for SMB teams that need grounded answers from scattered SOPs, docs, and client files.

AI-generated concept Proprietary synthetic data Client-safe showcase
SectorSMB knowledge ops
StackRAG, vector search, docs APIs
Signal92% grounded answers
ScenarioAI-generated concept
Why this exists

This RAG case study shows how Dedolytics can turn scattered SOPs, pricing rules, and service documentation into a grounded answer layer for SMB teams without exposing internal files.

The Challenge

Small teams lose time hunting through folders, inboxes, and chat threads for answers they already have somewhere. The result is slower client response, inconsistent quoting, and too much work flowing through the one person who remembers where everything lives.

Key Business Questions

  • Which sources should the assistant trust first?
  • How do we keep answers grounded instead of generic?
  • Where does uncertainty or missing documentation still need a human?
  • How do we make the system usable for frontline staff, not just technical teams?

The Solution

We framed the RAG hub as a practical knowledge layer for SMB operations. It ingests the right documents, cites the source, flags uncertainty, and routes edge cases back to a person instead of pretending the answer is always obvious.

Source intake

Controlled ingestion of docs, SOPs, price lists, and service policies into a searchable knowledge base.

Grounded answers

Responses cite source passages so the team can trust what the system is saying.

Confidence routing

Low-confidence or conflicting answers get kicked to a human queue instead of guessing.

Knowledge gaps

A clear list of the policies or docs that still need cleanup to improve answer quality.

Buildable product preview

Knowledge Assistant Preview

This one needed a different language entirely. The board now feels like a working assistant surface with conversations, citations, source stacks, and fallback handling.

Grounded answers Source retrieval Fallback routing
Answer time-61%
Sources9
Grounded92%
Escalations14

Assistant surface

Question / answer / citation

Question

What is the return policy for damaged coolers?

Assistant answer

Use the standard damaged-unit workflow and show the policy source before replying. Escalate only when the vendor exception note changes the replacement window.

Citation

Policy / returns.md · section 4.2 · grounded

Source stack

Retrieval / ranking / handoff

SOPs

Returns, service scripts, and support checklists.

Policies

Current client-facing rules and exception notes.

Vendor docs

Replacement windows, freight terms, and edge-case handling.

Ground it or stop

Gap theme / fallback rule

Outdated return noteGap themeReview
Vendor exceptionFallbackHuman

Useful answers need citations and a clear handoff rule when the source base is thin.

Technical Frame

Knowledge layer

The concept combines synthetic SOP, pricing, and policy content with retrieval rules that mimic a practical SMB answer system.

Key metrics

  • Time to answer
  • Grounded response rate
  • Fallback escalation rate
  • Missing document themes

Workflow output

  • Answer console
  • Source citation view
  • Gap backlog
  • Escalation queue

Delivery mode

Ideal for SMB service, sales, and support teams that need faster answers without inventing policy on the fly.

The Result

-61%Answer time
9Connected sources
92%Grounded answers
14Escalations flagged

It feels like the first AI concept that respects how messy small-business documentation actually is.

Anonymous Operations Manager
Anonymous review
4.9/5
Useful, not magical

The confidence routing is what makes it believable. It knows when to stop and hand off.

Anonymous SMB review