Solutions Delivery AI Pricing PoC Programme About Contact Us →

AI Built for
Retail &
Wholesale
Australia.

20 years of applied AI research, now purpose-built for Australia's most complex supply chains, store operations, and workforce challenges. From Woolworths to Harvey Norman — we solve what generic AI can't.

20+
Years of AI Research
30
AI Engineers on Staff
4
Core AI Solutions
Demand AI — Active
Loss Prevention — 98.2% Accuracy

Built for Australia's leading retail & wholesale enterprises

Woolworths Coles Wesfarmers / Kmart JB Hi-Fi Harvey Norman Chemist Warehouse

Four AI Engines Transforming
Retail & Wholesale Operations

Each solution is purpose-engineered for Australian market conditions — from outback logistics distances to multicultural workforces and extreme weather-driven demand swings.

Use Case 01 — Supply Chain

Agentic Supply Chain &
Demand Forecasting

AI agents autonomously monitor inventory across every store location in Australia — from Perth to Cairns. Integrating real-time climate data, social media trend signals, and regional event calendars, our demand engine predicts stockouts and overstock situations before they happen. Autonomous agents re-route shipments, balance inter-store stock, and draft purchase orders without human intervention — reducing markdowns and opportunity loss simultaneously.

Key tech: LLM-based agentic orchestration, time-series forecasting, real-time data fusion, edge IoT sensors.

Wesfarmers/KmartWoolworthsColes
Use Case 02 — Loss Prevention

Computer Vision Store
Intelligence & Loss Prevention

Our edge AI platform turns existing in-store cameras into a real-time intelligence network. At self-checkouts, produce is identified instantly from any angle — even inside plastic bags — eliminating manual look-ups and reducing queue times by up to 40%. Simultaneously, the system detects suspicious behaviour patterns (sweet-hearting, skip-scan, bulk-removal of high-value items) and pushes instant, discreet alerts to floor staff devices — without creating confrontation or impacting customer experience.

Key tech: Edge AI inference, computer vision, object detection, real-time alerting pipeline.

ColesWoolworthsJB Hi-Fi
Use Case 03 — AI Advisor

Expert AI Sales Advisor
& In-Store Copilot

Powered by RAG (Retrieval-Augmented Generation), our AI Advisor ingests every product manual, spec sheet, compatibility matrix, and pharmaceutical interaction database your business holds — and turns it into instant, accurate guidance. For electronics staff, the system answers complex compatibility questions in seconds. For pharmacy environments, it offers safe, pharmacist-supervised supplement and wellness recommendations personalised by customer needs. Staff access via earpiece, smartwatch, or tablet; customers via kiosk or app.

Key tech: RAG, vector search, knowledge graph, enterprise LLM fine-tuning.

JB Hi-FiHarvey NormanChemist Warehouse
Use Case 04 — HR & Back Office

Agentic HR & Back-Office
Automation

Australia's major retailers employ tens of thousands of casual and part-time workers — many from culturally and linguistically diverse backgrounds. Our multilingual HR AI agent handles leave balance queries, shift-swap requests, contract interpretation, and award interpretation in plain language — in English, Mandarin, Vietnamese, Hindi, and more. Integrated with SAP and Microsoft Fabric, it doesn't just answer — it acts, drafting applications and completing workflows end-to-end. Meanwhile, AI-powered VoC (Voice of Customer) analysis turns tens of thousands of weekly feedback items into a concise "Top 3 Actions This Week" brief delivered directly to store managers every Monday morning.

Key tech: Agentic LLM, SAP/ERP integration, multilingual NLP, semantic clustering.

WoolworthsColesChemist Warehouse

The Last Mile Is the Hardest Mile.
We've Solved It.

Australian retailers lose an estimated A$2.4B annually to last-mile inefficiency — failed deliveries, idle drivers, and over-promised ETAs. Our AI Predictive Delivery Advisor turns your existing fleet and warehouse into a precision fulfilment network.

⚠ The Problem Today
🚚
Route Planning Done at Start of Day
Static morning routes ignore real-time traffic, new orders, and cancellations — leaving drivers stuck in inefficient sequences all day.
📦
Warehouse Picks Triggered Too Late
Pick begins only after order confirmation — adding 15–25 minutes of warehouse latency before any driver even moves.
🌧
No Weather or Event Awareness
AFL finals, sudden rain, school holidays — demand spikes that human dispatchers can't respond to fast enough, causing stockouts and missed SLAs.
Failed Deliveries Cost $15–$25 Each
Re-delivery attempts, customer service calls, and refunds erode margin on every failed attempt — often 8–12% of all deliveries.
✅ What Roocentric Delivers
🧠
Continuous Real-Time Re-Routing
Our RL engine re-optimises every driver's route every 90 seconds — adapting to traffic incidents, new orders, and cancellations instantly.
Predictive Pick Initiation
AI forecasts orders 20–40 minutes before checkout — triggering warehouse picks proactively so drivers depart the moment orders are confirmed.
🗺
Hyper-Local Demand Clustering
Orders within the same suburb are clustered and co-dispatched, slashing per-delivery cost and enabling the 60-minute promise.
📱
Proactive Customer ETA Engine
Customers receive live ETA updates and re-scheduling options before they even notice a delay — reducing failed delivery rates by up to 35%.

Quantified Business Impact

↓35%
Failed Deliveries
Proactive ETA management and pre-emptive re-scheduling eliminate the majority of "no-one home" failures
↓22%
Cost Per Delivery
Route clustering and predictive dispatch reduce idle km driven and driver downtime between drops
↑41%
On-Time Rate
From typical 58–65% industry average to 90%+ with continuous AI re-routing and warehouse slot management
↑28%
Driver Utilisation
AI fills schedule gaps with co-located orders, increasing drops-per-driver-per-shift by an average of 4–6 deliveries
📊 Qualitative Benefits
  • Customer Trust & Retention: Consistent 60-minute delivery becomes a brand differentiator — customers who receive on-time deliveries have 2.4× higher repeat purchase rates.
  • Driver Satisfaction & Retention: Optimised routes reduce driver stress and overtime — lowering driver churn in an industry facing acute labour shortages.
  • Sustainability Credentials: Fewer kilometres driven per delivery reduces fleet CO₂ emissions — directly supporting ESG reporting targets.
  • Competitive Moat: 1-hour delivery capability matches or exceeds the promise of quick-commerce entrants (e.g., DoorDash, Milkrun) without the unit-economics risk.
  • Warehouse Labour Efficiency: Predictive pick initiation smooths warehouse workload peaks, reducing overtime costs and pick errors under time pressure.
🧮 Illustrative ROI Scenario
For a mid-size retailer operating 500 deliveries/day at A$18 average cost per delivery:
Current annual delivery costA$3.29M
↓22% cost reduction via AI−A$724K / year
↓35% failed delivery saving (re-deliveries)−A$191K / year
↑ Revenue from higher repeat rate+A$380K / year
Total Annual Benefit~A$1.3M
* Indicative only. Actual results vary by fleet size, delivery density, and current operations baseline.

🏭 Warehouse & Inventory Management AI — What's Included

Smart Slotting Optimisation
AI determines optimal warehouse bin locations based on co-purchase patterns, reducing pick walk distance by up to 30%.
Predictive Replenishment
Forecasts warehouse-level stock needs 48–72 hours ahead, preventing slot starvation on high-velocity SKUs during peak delivery windows.
Pick Wave Sequencing
Batches orders by delivery zone and departure slot — so picks always align with driver availability, eliminating staging bottlenecks.
Dock & Carrier Management
AI schedules inbound and outbound dock slots dynamically, avoiding congestion and reducing dwell time for third-party carriers.
Returns Intelligence
Predicts return likelihood at the order level, pre-routing return labels and processing queues to minimise restock lag.
Real-Time Visibility Dashboard
A single-pane-of-glass view across all warehouses, drivers, and deliveries — with exception alerts pushed to operations managers instantly.

Pricing is tailored to your fleet size, delivery volume, and integration requirements.

Request a Delivery AI Demo ↗

Flexible Models to Match
Your Scale & Ambition

From PoC to enterprise-wide deployment — we structure every engagement around your organisation's size, risk appetite, and outcomes, not our preferred billing model.

Starter / SME
A$50,000 project

Ideal for single-site pilots, one AI solution in scope, with a clear business case validation target.

  • One use-case deployment (e.g., Loss Prevention or Demand Forecasting)
  • 3-month PoC engagement with fortnightly reviews
  • Integration with up to 2 existing data sources
  • Model training on your historical data
  • KPI dashboard and final business case report
  • Post-PoC scaling roadmap
Enquire →
Enterprise
A$200,000+ project

Full-suite deployment across multiple business units, custom model development, and white-label options available.

  • All four AI solutions in scope
  • Unlimited site integrations, national rollout support
  • Custom foundation model fine-tuning on proprietary data
  • On-premise or private cloud deployment options
  • Dedicated engineering pod (3–5 engineers)
  • SLA-backed 24/7 support
  • Board-level ROI reporting and governance framework
Talk to Us →

SaaS Subscription Plans

For ongoing access to our platform — ideal for in-store advisor, HR bot, or analytics modules — we offer per-user monthly subscriptions with no lock-in annual commitment required after the first 3 months.

A$30/mo
Essentials
per user
A$60/mo
Professional
per user
A$100/mo
Enterprise
per user

Start Small.
Prove Value Fast.

Our SmallStart PoC is designed to eliminate procurement risk. In 90 days, you'll have live AI running in your environment — with real data, real outcomes, and a clear go/no-go decision point before committing to a full deployment.

Fixed price, no overruns
Fortnightly progress reviews
IP stays with your business
No lock-in after PoC phase
AI ✓ IDENTIFIED
Month 1
Foundation

Discovery, Data Audit & Environment Setup

  • Weeks 1–2: Business workshops — define success KPIs, identify target stores/SKUs/processes, data availability audit
  • Weeks 1–2: Stakeholder alignment — IT, Operations, Finance, Store Management sign-off on scope and access
  • Weeks 3–4: Data pipeline setup — connect to existing POS, ERP/SAP, camera systems, HR platforms
  • Weeks 3–4: Baseline measurement — establish pre-AI performance benchmarks across agreed KPIs
  • Milestone: PoC kick-off demo with live data feeds confirmed ✓
Month 2
Build & Test

Model Training, Deployment & Iteration

  • Weeks 5–6: AI model training on your anonymised historical data (12–24 months of transaction, staffing, and inventory data)
  • Weeks 5–6: Solution deployment in UAT (User Acceptance Testing) environment — zero risk to live operations
  • Week 7: Go-live in selected pilot stores — staff briefing and shadowed operation mode
  • Week 8: First performance review — model calibration and bias correction based on real-world results
  • Milestone: AI running live, first weekly performance report delivered ✓
Month 3
Optimise & Decide

Optimisation, ROI Measurement & Scale Decision

  • Weeks 9–10: Advanced parameter tuning — model fine-tuning based on edge cases identified in live operation
  • Weeks 9–10: Expanded pilot — optional roll-out to 2 additional sites with learnings applied
  • Week 11: Final KPI vs baseline comparison — ROI calculation, qualitative staff and customer feedback synthesis
  • Week 12: Board-ready PoC report — go/no-go recommendation with full-scale deployment roadmap and pricing
  • Milestone: Decision point — clear, data-backed go/no-go for full deployment ✓
↓40%
Typical reduction in markdown losses within 90 days
↑35%
Improvement in self-checkout throughput with Computer Vision
↓80%
HR inquiry handling time reduction in Agentic HR deployments
AI

Sydney Born.
Research Backed.
Retail Focused.

Roocentric AI Factory Pty Ltd. was founded in Sydney after two decades of applied AI research that began at the University of California AI Research Lab. A core team of five researchers turned their academic breakthroughs into commercially viable solutions — and haven't stopped since.

Today, we operate as a Sydney-based venture with fewer than 30 specialist AI engineers who work exclusively in the retail and wholesale sector. We partner with leading Australian universities and research institutions to ensure our models reflect the latest in academic AI progress — applied to the practical realities of Australian commerce.

We believe generic AI produces generic outcomes. The only way to create lasting competitive advantage for Australian retailers is to build AI that deeply understands your industry, your workforce, your geography, and your customers.

OL

Founding Team — Roocentric AI Factory Pty Ltd.

Originally formed as a 5-person AI research group at the University of California. 20+ years of continuous research commercialisation across supply chain, computer vision, and natural language systems.

📍 Sydney HQ
WeWork, Level 13, 333 George Street
Sydney, NSW 2000, Australia

Let's Talk About
Your AI Challenge

Whether you're exploring a specific use case, evaluating vendors, or ready to kick off a PoC — we'd love to hear from you. Our team responds to every enquiry within one business day.

admin@roocentric.com
WeWork, Level 13, 333 George Street, Sydney NSW 2000
Mon–Fri, 9:00 AM – 6:00 PM AEST
AI

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In the meantime, explore our PoC programme above.