PROPOSED AI APPLICATION

Device repair knowledge assistant

Technicians search many approved documents

Mobile and computer repair shopsSpecialist evaluationProposed solution scope

The problem and the proposed outcome

Application: Mobile and computer repair shops — Device repair knowledge assistant
Problem to solve: Technicians search many approved documents
Target buyer: Repair owners, service counter teams
Intended use: Employee / expert own workflow; internal business solution
Benefit to test: Test whether evidence lookup and response preparation become faster while correctness remains acceptable.
Potential delivery: Internal application, reviewer dashboard, private assistant or authorized user portal; choose one surface after validating the workflow.

AI method: Evidence-grounded knowledge assistant / RAG
Method code: R
AI contribution: Use semantic retrieval and a language model to answer from approved sources, attach source references, and abstain when evidence is missing.
Output artifact: Answer draft with supporting source passages and an unresolved-question flag.
Core modules: Approved source intake → semantic retrieval → evidence-linked answer → reviewer feedback
Simple baseline: Manual document search, curated FAQ and keyword search.
Why test AI: Could help with semantically varied questions over many documents; retain the baseline if AI does not improve usefulness or handling time.

Pilot track: Specialist evaluation
Required data: Approved manufacturer references; exact models
Data readiness: A current approved corpus, access rules and representative questions with reviewer-approved answers are needed.
First development step: Select an approved corpus and create a small reviewer-labelled question set; measure keyword/FAQ performance first.
Initial MVP scope: One defined task, one permitted dataset and one reviewer workflow. Manual upload or authorized export first; no unattended external action. Validate the model before live integration.
Demo data: Synthetic or licensed public samples may illustrate the workflow. Real benefit and model performance need representative authorized held-out data.
Buyer validation question: Ask Repair owners, service counter teams: show a recent case where technicians search many approved documents, record the present effort or error, and confirm whether the required data can lawfully be used.

Primary success metric: Correct-source retrieval time
Model evaluation: Measure answer correctness, grounding, source relevance, abstention and unauthorized-source exclusion on held-out questions.
Metric target: Set after baseline measurement; no improvement or accuracy result asserted.

Specialist review: Yes
Human review boundary: Qualified domain specialist reviews evidence, interpretation and any consequential action. Start with controlled offline or shadow evaluation; no autonomous clinical, financial, employment, industrial or safety decision.
Domain review: Confirm data ownership, access, source rights, reviewer authority and correction routes with the operator.
Related micro-ERP catalogue IDs: IWA-B-A57E18164E1F; IWA-B-8328C4D009A8; IWA-B-C1D517398B74
Integration status: Potential segment link only; no product integration implemented.