AI Assistant for Service and Documentation
Retrieve answers from an approved service corpus with citations, scope controls, and expert evaluation before release
Our role
We designed the controlled retrieval architecture, source and access rules, citation contract, user workflow, and expert evaluation process needed to assess AI as an accountable service tool.
The Problem
Technical knowledge for service lives across PDF manuals, spare-part notes, internal wikis, and senior specialists. Technicians lose time hunting for the right document, and generic AI tools answer confidently without proving which procedure actually applies.
- Documentation is searchable in theory but fragmented across formats and versions in practice
- Answers without citations are unsafe for maintenance and repair decisions
- Support teams repeat the same explanations instead of improving the shared knowledge base
Why It Matters
AI only helps service when it returns users to trusted documents with clear limits. An assistant must cite sources, stay inside an approved scope, and expose where the knowledge base is incomplete.
- Speed of finding the right approved procedure for a service task
- Reliability of answers backed by visible source references
- Clarity about which questions the controlled knowledge base can and cannot answer
Our Solution
Controlled ingestion pipeline for approved manuals, service bulletins, and procedures with source, version, and access metadata
Retrieval-augmented answer flow that filters the corpus, cites supporting passages, and refuses or escalates when evidence is insufficient
Role-aware service interface integrated with document lookup and feedback capture rather than an unrestricted general chatbot
Repeatable evaluation set with expected evidence, expert scoring, failure categories, and release thresholds
Results
Technicians searching across PDF folders and informal notes without a trusted single entry point
Scoped AI assistant that surfaces approved answers with citations and defined boundaries
- Accepted answers are the ones a technician can check against a cited source, not a fluent guess
- Questions the corpus cannot support stay visible as documentation gaps instead of invented procedures
- Escalation is a required path: a person takes the job when the assistant should stop, before any wider release
Project context
An equipment manufacturer had extensive service documentation but no fast, reliable path to the applicable section during a job. neexo defined an approved and versioned corpus, built a citation-first retrieval workflow, added refusal and feedback behavior, and evaluated representative questions with domain experts before any broader release.
Deliverables
- Controlled ingestion and retrieval index with source, version, and access metadata
- Citation-first service assistant prototype with refusal, escalation, and feedback paths
- Expert evaluation set, scoring rubric, release criteria, and documented failure analysis
Want to test AI on service knowledge without losing traceability?
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