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SmartDS – AI route optimization for dark store picking

SmartDS – AI route optimization for dark store picking

by tomasappsrecord·Feb 16, 2026·1 point·0 comments
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AI Analysis

●●SolidNiche GemSlickSolve My Problem
The Take

Real-time routing plus parallel-picking logic and per-shelf heatmaps is a sensible feature mix for dark stores — the UI shows concrete tools (heatmaps, layout recommendations, parallel order assembly) ops teams can actually act on. The pitch leans hard on a 70% travel reduction but gives no solver details or benchmarks; ask about datasets, how the planner handles blocked aisles/ congestion, and integration latency before committing.

Category
AI/ML
Target Audience

Dark store operators, micro-fulfillment and Q-commerce logistics managers, warehouse operations teams

Post Description

Hi HN,

We built SmartDS to solve the "walking problem" in dark stores and micro-fulfillment centers. In facilities over 400 sq.m, pickers often spend more time walking than actually picking.

We developed an AI-powered engine that:

Optimizes picking routes in real-time (reducing travel distance by up to 70%).

Uses parallel picking logic for multi-order fulfillment.

Generates heatmaps and layout recommendations based on SKU velocity and physical constraints (e.g., heavy items first).

It's hardware-agnostic and integrates via API. We’ve seen implementation go from months to about 4 weeks by mapping store layouts digitally first.

We’d love to hear your thoughts on the routing logic or how you’ve handled similar optimization problems in logistics.

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