Stack
Proof metrics
Problem
Warehouse audits often run for weeks with multiple field staff and heavy manual reconciliation in spreadsheets.
Damaged, faded, or angled labels fail frequently on traditional handheld scanners, creating repeated variance loops.
Audit workflows require sustained context across long sessions, not isolated one-shot API calls.
Solution
Built a stateful agent workflow for end-to-end warehouse walk sessions with resumable progress.
Used high-fidelity model vision to extract bin and label data from low-quality real-world images.
Applied selective high-effort reasoning only for variance classification while keeping routine OCR paths cost-efficient.
Added self-verification before report output to improve confidence for enterprise audit handoff.
Architecture
Capture layer: phone/meta glasses image capture during aisle walkthrough.
Interpretation layer: Claude Opus vision + extraction pipelines for labels and bin codes.
Agent layer: managed multi-step session coordinating scan, lookup, reconciliation, and variance tagging.
Data layer: ERP/master-data reconciliation plus structured variance report output.
Outcomes
Proved a practical AI-first audit workflow that can run in real warehouse conditions in Nairobi.
Demonstrated operational viability for long-horizon agent sessions and resume/retry behavior.
Established a flagship product proof for forward-deployed AI engineering in East African enterprise environments.
Links & artifacts
Related work
AssetZen
AssetZen is an operations-focused product direction for streamlining asset visibility, issue tracking, and decision workflows with AI-assisted actions.
Read case studyAIDC Barcode Toolkit
Open-source toolkit that packages real-world AIDC workflows so Claude Code can generate, validate, and reason about barcode and labeling tasks with domain-correct defaults.
Read case studyDiscuss this work
Hiring or building something similar—reach out with context and constraints.
Email Joseph