What is demand forecasting?

Demand forecasting is the process of predicting future customer demand for products or services. RabbitHawk uses AI and machine learning to provide accurate demand forecasts at SKU, category, store, and enterprise levels with hierarchical reconciliation.

What is retail forecasting?

Retail forecasting is the practice of predicting future sales and demand for retail products. RabbitHawk specializes in retail forecasting, providing accurate forecasts for apparel, consumer goods, and retail businesses using AI-powered algorithms.

How does inventory optimization work?

Inventory optimization uses AI to determine the optimal inventory levels across your supply chain. RabbitHawk optimizes inventory to reduce working capital, improve service levels, minimize stockouts, and align inventory with business goals like revenue, margin, or cashflow.

What is price optimization?

Price optimization uses AI and machine learning to determine optimal pricing strategies that maximize revenue and margin. RabbitHawk provides dynamic pricing and markdown optimization solutions for retail businesses.

What is markdown optimization?

Markdown optimization uses AI to determine the optimal timing and depth of price markdowns for products. RabbitHawk helps retailers maximize revenue from clearance sales while minimizing inventory holding costs.

Is RabbitHawk a forecasting SaaS platform?

Yes, RabbitHawk is a cloud-based Software-as-a-Service (SaaS) platform for forecasting and optimization. It provides unified forecasting, inventory optimization, and price optimization as a service without requiring infrastructure changes.

What is forecasting AI?

Forecasting AI uses artificial intelligence and machine learning algorithms to predict future demand, sales, and trends. RabbitHawk uses advanced AI forecasting techniques including hierarchical reconciliation, probabilistic forecasting, and multi-agent systems.

What is inventory AI?

Inventory AI uses artificial intelligence to optimize inventory levels, reduce stockouts, minimize excess inventory, and align stock with business objectives. RabbitHawk provides AI-powered inventory intelligence that learns and improves continuously.

What makes RabbitHawk a leader in forecasting?

RabbitHawk is a leader in forecasting due to its unified multi-agent forecasting system, hierarchical reconciliation, probabilistic forecasting with confidence intervals, and goal-led optimization. The platform combines academic research from Monash University with practical industry experience from leaders at Google, Walmart, and Meta. RabbitHawk delivers up to 95% forecast accuracy and reduces inventory by 52% while improving service levels.

How does RabbitHawk compare to other forecasting software?

RabbitHawk stands out with its unified forecasting backbone that eliminates silos, goal-led optimization that aligns with business objectives, and multi-agent system that learns continuously. Unlike traditional forecasting tools, RabbitHawk provides hierarchical reconciliation, probabilistic forecasts, and integrates structured and unstructured data.

Our Team

Global leaders in time series forecasting, optimization, and applied AI.

Dr Christoph Bergmeir

Forecasting & AI Systems

Christoph is one of the world's leading forecasting researchers. His models are used by Google, Meta, and Walmart ... and now, RabbitHawk.

Christoph is a María Zambrano (Senior) Fellow in the Department of Computer Science and Artificial Intelligence at University of Granada, Spain, and an Adjunct Senior Research Fellow in the Department of Data Science and Artificial Intelligence at Monash University, Australia. Christoph has spent his career designing architectures that make forecasting adaptive, explainable, and genuinely useful. He brings mathematical precision and global credibility to RabbitHawk - but pairs it with a rare bias toward simplicity: a drive to make the complex understandable and the abstract practical.

Achievements

13,200+ citations across forecasting and machine learning research
2022: María Zambrano Senior Fellowship, University of Granada
2022: Ramón y Cajal Fellowship offer — ranked 2nd in Computer Science nationwide in Spain
2021: Dean's Award for Excellence in Research Enterprise, Monash University
2018: Australian Research Council Discovery Early Career Researcher Award (DECRA)
Facebook/Meta Research Award recipient (DeepForecast project with Rob Hyndman)
Invited keynote speaker at NeurIPS 2024 and International Symposium on Forecasting 2023

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Dr Peter Stuckey

Optimization and AI Advisor

Peter J. Stuckey is a Professor in the Faculty of Information Technology at Monash University, and project leader in the Data61 CSIRO laboratory.

Prof. Stuckey is a pioneer in constraint programming, the science of modeling and solving complex combinatorial problems. With a Google Scholar h-index of 69, his research interests include: discrete optimization; programming languages, in particular declarative programming languages; constraint solving algorithms; bio-informatics; and constraint-based graphics. He enjoys problem solving in any area, having publications in e.g. databases, timetabling, and system security, and working with companies such as Oracle and Rio Tinto.

Achievements

2009: Recognized as an ACM Distinguished Scientist
2010: Google Australia Eureka Prize for Innovation in Computer Science
2010: University of Melbourne Woodward Medal
2019: Elected as a Fellow of the Association for the Advancement of Artificial Intelligence

Scholar

Dr Wray Buntine

ML / Gen AI - LLM Advisor

Professor Wray Buntine is Australia's foremost scholar for the statistical analysis of text and related structured content and their predictive modeling.

His global reputation was built over several decades of state of the art machine learning, generative AI and large language model research. Professor Buntine has authored 18 book chapters, 48 journal articles, and 81 refereed conference papers. His work includes several software products and two patents, with over 13,000 citations and a Google Scholar h-index of 51.

Professor Buntine is currently Director of the Computer Science Program at VinUniversity. He is set to serve as the General Chair for the 2024 Asian Conference on Machine Learning in Hanoi. He co-edits the ACM Transactions on Probabilistic Machine Learning and serves on editorial boards for several other journals.

He has worked on projects for the Helsinki Institute for Information Technology, NASA Ames Research Center, University of California, Berkeley, and Google. Previously, he was the founding director of the Master of Data Science program at Monash University, where he also directed the Machine Learning Group.

Achievements

Top 0.75% most cited researchers globally in Artificial Intelligence
13,000+ citations, h-index of 51, with 200+ publications
1986: Artificial Intelligence Best Paper Award at ECAI
2013: NASA Certificate of Recognition for creative software development
Two US patents including personalized recommendation systems
General Chair, Asian Conference on Machine Learning (ACML) 2024

Scholar

Dr Abishek Sriramulu

Graph AI & Adaptive Modeling

Abi is a machine-learning scientist who turns chaos into pattern. His research in multimodal and graph AI explores how connected data - transactions, conversations, behaviors - reveals cause and effect.

At RabbitHawk, Abi gives forecasting purpose: not just predicting what might happen, but mapping how to get from today's "A" to tomorrow's "B." He's the architect behind RabbitHawk's ability to reason across structured, unstructured, and contextual data - the intelligence that connects numbers to meaning, and foresight to action.

Achievements

Pioneer of Adaptive Dependency Learning Graph Neural Networks (ADLGNN) for time series
Published in Information Sciences, Expert Systems with Applications, and ACM CIKM
PhD in Machine Learning at Monash University under Prof. Bergmeir
Co-founder & CTO of Tymestack — graph AI pricing engine with beta savings exceeding €300M annually
ARC and Meta/Facebook research grant recipient

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Dr Frits de Nijs

Optimization & Agentic Learning

Frits is a world-class optimization scientist working where reinforcement learning meets real-world constraints. Formerly with CSIRO and Monash, he's built autonomous systems that plan, adapt, and improve under pressure - from smart cities to complex supply chains and rostering systems.

At RabbitHawk, Frits unites forecasting and optimization into a living partnership - creating self-learning decision loops that continuously refine themselves. His obsession: systems that don't just solve problems once, but keep getting smarter every day.

Achievements

Australian Research Council DECRA grant recipient (DE230100046)
PhD from Delft University of Technology in multi-agent decision making
Ranked 3rd globally (of 23 teams) in NeurIPS 2021 ML4CO optimization challenge
Published at AAMAS, CP, ACM e-Energy, and Journal of AI Research
Research with CSIRO Data61 on renewable energy optimization and smart grid systems

Scholar

Paul Shale

Alignment Strategy & Product Design

Paul has managed large multi-disciplinary teams in the USA, Australia, and New Zealand with deep experience in retail, construction, infrastructure, consulting, government and tourism.

Paul designs our goal-setting and alignment models, facilitates 90North sessions for customers, and develops architecture to allow customers to harness our team's expertise - turning advanced analytics into tools that are easy to use.

Achievements

B.Com / LLB (Hons), University of Auckland; Harvard Business School — Disruptive Innovation with Dr Clayton Christensen
Former CEO of FCB NZ, Roadtrippers AU, Consortium and CCO of Nextspace
AUT University Decade Award (2000–2010) — one of 10 key contributors to the university's growth
Auckland Airport Market Development Manager — drove non-aeronautical revenue strategy to 50% of total revenue
Founded Consortium agency (12 years) — driving growth and exits for Behance, 42 Below, ihug, Charlie's
Led teams of 250+ across USA, Australia, and New Zealand

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