Skip to main content

AI Features (Priority Order)

AI Features (Priority Order)

Overview

Plock is AI-native — not AI-bolted-on. Every data model is designed with AI querying in mind. The AI layer runs continuously; warehouse workers are alerted, not asked to check.

Target user for AI: A warehouse worker with dirty gloves asking var finns SKU-X on a cracked phone screen — not an analyst building pivot tables.

Language: Swedish first. Short, direct responses. Action-oriented. Mobile-friendly.

Priority 1 — Plock AI Chat (Launch)

Type: LLM natural language interface Model: Claude API (claude-sonnet-4-5) Language: Swedish

What it does

  • Answer warehouse questions in natural language Swedish
  • Execute actions with single confirmation (move stock, reserve, assign pick)
  • Access real-time inventory, orders, performance data
  • Multi-turn conversation with warehouse context injected

Example queries

  • Hur manga enheter av SKU-1234 har vi? — live stock count + trend
  • Vilka ordrar ar forsenade idag? — list with root cause
  • Flytta 50 enheter fran A-01-01 till B-02-03 — confirmation + action

Architecture

  • System prompt includes: warehouse schema snapshot, org context, user role, current time
  • Function calling for write operations (move, reserve, assign)
  • Organization data never sent raw to Claude — only sanitised context
  • Response target: less than 3 seconds

Priority 2 — Smart Picking (Launch)

Type: Route optimisation Engine: OR-Tools (Google) — VRP/TSP solver Reduction target: 30-70% travel time

What it does

  • Automatically generates optimal pick routes when orders are released
  • Groups orders into picking waves based on zone, carrier cutoff, priority
  • Outputs sequential bin list on mobile scanner
  • No configuration required — runs on every order release

How it works

  1. Order released — ML microservice receives order + bin locations
  2. OR-Tools VRP solver computes shortest path across warehouse zones
  3. Optimised pick list returned to mobile app in under 2 seconds
  4. Worker follows sequence — no backtracking

ROI

  • Industry data: 30-70% of warehouse time is walking (not picking)
  • Conservative 20% efficiency gain = 8,800 SEK/month per worker saved

Priority 3 — Anomaly Detection (Launch)

Type: Statistical detection on inventory transactions Algorithms: Z-score + isolation forest (scikit-learn) Trigger: Real-time, on every transaction

What it detects

  • Sudden stock drops (theft, mispick, data error)
  • Unusual pick rates (demand spike or data entry error)
  • Inventory discrepancy after goods receipt
  • Carrier label printed but no scan out (lost shipment risk)

Alert flow

  1. Transaction recorded — Python microservice analyses against rolling baseline
  2. Z-score above 3 sigma or isolation forest flags as outlier — alert created
  3. Warehouse manager receives push notification + AI chat message
  4. Manager confirms or dismisses — feedback improves model

Priority 4 — Demand Forecasting (Month 6)

Type: Time-series forecasting Models: XGBoost + Prophet (Facebook) Input: Order history (minimum 90 days)

What it does

  • Predicts demand per SKU for next 7/14/30 days
  • Generates reorder recommendations with suggested quantity and timing
  • Adjusts for seasonality (Swedish calendar events, e-commerce peaks)
  • Accessible in AI Chat: Vad ska vi bestalla hem nasta vecka?

Priority 5 — Smart Slotting (Month 6)

Type: ML-based placement recommendations Input: Pick frequency, order affinity, SKU dimensions

What it does

  • Recommends optimal bin locations for each SKU based on pick frequency
  • Groups frequently co-picked SKUs near each other (affinity slotting)
  • Identifies slow-movers in prime locations and suggests moves
  • Presented as actionable task list, not a forced migration

Priority 6 — Labor Planning (Month 12)

Type: Shift optimisation Constraints: Swedish labour law (Arbetstidslagen)

What it does

  • Predicts staffing needs based on demand forecasts
  • Generates compliant shift schedules (max hours, breaks, rest periods)
  • Suggests overtime or temp worker needs 1-2 weeks in advance
  • Integrates with order volume forecasts from Demand Forecasting module

AI Cost Model (per customer)

Feature Est. cost/month (Growth plan)
Plock AI Chat (1,000 queries/month) ~45 SEK (Claude Sonnet pricing)
Smart Picking (10,000 optimisations) ~5 SEK (OR-Tools compute)
Anomaly Detection (100K transactions) ~10 SEK (Python microservice)
Total AI cost per Growth customer ~60 SEK/month
Growth plan revenue 2,990 SEK/month
AI cost as % of revenue ~2%
Back to top