Warehouse orchestration
A control layer for orders, inventory locations, picking tasks, packing flow and fulfillment status.
Robotic fulfillment software
Kiguri Fulfillment Automation Platform
Software for warehouses that combine inventory control, robotic picking, human exception review and API-based fulfillment integration.
Single-site first
Start with one warehouse that can actually run.
Multi-site ready
Keep warehouse boundaries in the data model from day one.
Robot-model agnostic
Support different picking, transfer and inspection robots.
AI-data-native
Capture useful evidence for tuning, not just success or failure.
Platform
A public product layer, not a public operations console.
Kiguri.com presents the product direction. Real robot control, warehouse state, SKU data, camera feeds and operator workflows should live behind authentication on a separate private console.
Automation layer
Pick
Verify
Pack
Ship
Robot fleet
Model capability matching
Inventory graph
SKU, tray, rack and warehouse state
Exception review
Controlled operator escalation
Data capture
Evidence for AI tuning
Robot-ready workflows
Task assignment can account for robot model, payload, sensor profile, tray type and handling requirements.
Exception handling
Failed picks, sensor mismatch and blocked workflows can be routed into controlled human review paths.
Fulfillment integration
Products, inbound stock, orders, shipment status and webhooks can be exposed through partner-facing APIs.
Robotics
Designed for more than one robot type.
The system should model robots by capability, not by hardcoded unit identity. That keeps the platform ready for suction pickers, carrier robots, inspection robots and future hardware.
Picker robots
Task matching can consider payload, movement, sensors, gripper type and safety constraints.
Carrier robots
Task matching can consider payload, movement, sensors, gripper type and safety constraints.
Inspection robots
Task matching can consider payload, movement, sensors, gripper type and safety constraints.
AI data
Operational evidence becomes tuning data.
Every pick attempt can create a labeled record: what the robot saw, what sensors measured, which policy ran and how a human resolved the outcome.
Product reference images
Weight and tolerance checks
Vacuum and sensor traces
Robot model and firmware version
Policy and AI model version
Human-reviewed outcome labels
Rollout
Build the private warehouse console separately.
Public pages should describe capability. Internal pages should be isolated by login, role, network policy and audit logging before they expose live fulfillment operations.
Auth firstPrivate consoleAudit loggingNetwork controls
01
Operational foundation
Products, inventory locations, orders, manual picking flow and audit logs.
02
Robot integration
Heartbeat, telemetry, task dispatch, charging state and safe exception routing.
03
Learning loop
Sensor evidence, image references, operator labels and model-version tracking.
04
External fulfillment
Seller portal, API keys, webhooks, reporting and multi-warehouse expansion.