Chapter 01
Challenge
01
Reduce the time first-line operators spend digging through SOPs and historic tickets without leaking PII to public model endpoints.
Back to archive
Agents & AssistantsAssisted OperationsNext.js + pgvectorCitation-first operator retrieval NDA
Exhibition page
Internal Knowledge Assistant
A retrieval assistant over operations docs and tickets, with citation, feedback capture, and human escalation.
Onboarding speed
Shorter
New operators reach useful answers faster because the playbook and ticket history are easier to query with citations.
Trust model
Citations first
Every answer includes sources so staff can check it before using it.
Data handling
PII protected
Redaction and private retrieval boundaries keep the model useful without exposing raw customer data carelessly.
ai
What this case covers
Assisted OperationsStack focus: Next.js + pgvectorOutcome focus: Citation-first operator retrievalService: Build an operator assistantTool: Operations automation noteConfidential delivery workflow
Next step
Use the build brief when the team needs a citation-first assistant with measurable evaluation and escalation rules.
Exhibition notes
Industry: Assisted Operations
Related: Build an operator assistant / Operations automation note
Outcome: The assistant keeps sources, evaluation results, and operator actions in one reviewable record.
This exhibition focuses on delivery logic and outcomes rather than exposing confidential internal UI.
Chapter 02
System
02
Hybrid retrieval over chunked SOPs plus structured ticket history.
Citation-first answers with inline links to source pages.
Feedback loop captures corrections to fine-tune ranking weights.
Chapter 03
Interface
03
Primary capabilities: RAG, Operator Tools, PII Redaction.
Signature stack markers: Next.js, pgvector, LLM APIs, Resend.
ai
Annotation
RAG
Annotation
Operator Tools
Annotation
PII Redaction
Chapter 04
Outcome
04
Operator onboarding time shortened with self-serve playbook lookup.
No raw customer data sent to model providers; PII routes through redaction.
Citations make answers verifiable rather than blindly trusted.
map
Source architecture
Keep the chapter format, but give the reader a clear sense of how sources, retrieval, and escalation fit together.
Source layer
Chunked SOPs and structured ticket history sit behind the answer path instead of vague training claims.
Answer layer
Responses foreground citations so the operator can trace the evidence immediately.
Escalation layer
When confidence is thin or context is missing, the system routes the operator toward human escalation.
In short
Evaluation and operator outcomes
The case connects measurable answer quality with the day-to-day operator gain.
Answer quality
Citation-backed
The evaluation model focuses on verifiable answers rather than eloquent guesses.
Feedback loop
Corrections captured
Operator edits feed ranking and retrieval improvements instead of disappearing into chat history.
Operational gain
Lookup faster
The system reduces the time spent hunting through SOPs while keeping the review boundary visible.
Related archive drawer
Choose the next step that fits this case.
crypto
Crypto & DeFi
Rexter Exchange: full crypto desk
A multi-chain exchange website, KYC flow, and operator console.
Open exhibitioncrypto
Crypto & DeFi
Tethrex: DeFi yield routing
Yield aggregator front end with route comparison, slippage transparency, and a real-time vault dashboard.
Open exhibitionweb
Web & Platforms
Attnom Tools: utility OS
A free, browser-native suite of file, image, OCR, and archive utilities that work entirely client-side.
Open exhibition