AI Quality Control for Trench Documentation.
Build a functional AI prototype that checks trench photo documentation, maps coverage gaps and flags construction risk before acceptance.
What we cannot see will cost us.
Fiber trench documentation is high-risk when survey data is wrong, photos are missing or burial depth is unclear.
Bad documentation can invalidate warranty claims, shift liability and create expensive fiber cuts during future excavation or road works.
Your task is to build an AI-powered quality-control prototype that reviews trench documentation and identifies where evidence is complete, weak or missing.
Start here.
The challenge is built around trench photos, route geometry and automated quality-control logic.
Trench Photos
Use the provided route photo set as the main input for AI-based documentation review.
GeoJSON Route
Map photo evidence to route segments and identify missing or weak coverage.
Compliance Signals
Check GPS/date metadata, duct visibility, sand bedding, pipe end seals, ruler readability and privacy issues.
Map + Report
Produce a reviewer-friendly result: green, yellow and red segments plus a concise summary report.
Ingest
Load trench photos and route geometry into your pipeline.
Geo-match
Match image metadata to route segments, ideally at fine segment resolution.
AI review
Score photo quality and documentation evidence: ducts, bedding, depth, coverage and compliance.
Classify
Flag each segment as complete, partial or missing evidence.
Report
Show an interactive map, risk list or PDF-style report that a reviewer can act on.
Fully functional AI-QC workflow.
Your prototype must process real or provided trench documentation inputs and generate a usable quality-control result.
It does not need to be production-ready, but the core workflow must function end-to-end: image data in, AI/QC logic applied, risk output generated.
Manual review should become faster, more complete and easier to defend.
Turn photos into risk signals.
Your AI review can focus on a few high-value signals rather than solving every defect at once.
Missing Evidence
Where route sections have no usable photo or survey evidence.
Poor Documentation
Where photos exist but ducts, bedding, ruler, seals or context are unclear.
Acceptance Hotspots
Where missing evidence should block sign-off or trigger follow-up.
Ideas in the room.
These are starting points, not a menu. Build a focused solution around one clear reviewer, risk or decision.
Automated sign-off assistant
Pre-check whether trench documentation is complete before a section is accepted.
Evidence-based accountability
Flag missing or weak documentation so issues can be resolved while the trench is still actionable.
Future excavation risk map
Identify undocumented sections that could cause expensive fiber cuts later.
Reviewer productivity cockpit
Reduce manual review from days to minutes by prioritizing only problematic segments.
Audit trail generator
Create structured evidence for warranty claims, compliance checks and contractor disputes.
Rollout QC platform
Design a repeatable workflow that can be used across future construction phases.
Saturday late afternoon / evening.
The Sustainista team will run a first technical check to see how far the AI workflow, route matching and output logic have progressed.
Working pipeline
Be ready to show what data you ingest and how your first checks work.
Early risk result
Even a rough green / yellow / red segmentation is useful if the logic is clear.
Show the workflow, not just the idea.
Each team presents a functional AI-QC prototype for trench documentation.
AI-QC Workflow
Process trench photos and produce quality-control classifications.
Map or Report
Show where evidence is complete, partial or missing.
Value Case
Explain who uses it, what risk it reduces and why it should scale.
Find the risk before it becomes expensive.
Use trench photos. Match the route. Flag missing evidence. Protect the network.
Start with Resources →