RabbitHawk turns static planning into a living, adaptive system: one that sees patterns humans miss, learns from the unexpected, and continuously improves.

Goal-led, not supply-led

Starts with your desired outcome and dynamically maps the optimal path to achieve it.

Unified self-learning forecasting & optimisation

Every team sees the same reality. One continuous, self-reinforcing loop that learns from results and recommends the smartest next moves.

Structured, unstructured & contextual data

The first model to combine numbers, human intuition, imagery, and signals into a single adaptive view. For the first time, companies will be able to utilise the majority of their data.

Lightweight & cloud-native

Delivers supercomputer-level forecasting through affordable API-based cloud architecture.

Multi-agent, self-repairing network

Specialised domain agents collaborate to fix data and improve accuracy.

Goals without Games

Modular, goal-based cards that make data usable for everyone - RabbitHawk aligns decisions to outcomes eliminating KPI gaming and Goodhart drift.

RabbitHawk brings different viewpoints together, and focuses them in one direction. RabbitHawk transforms fragmented enterprise data into real-time decision intelligence.

The inflection point

Three macro shifts are redefining what’s possible.

The explosion of unstructured data

80–90% of enterprise data now lives outside structured systems—in text, imagery, conversations, and signals. The intuition of experienced retail teams lives here. Every message, sensor, and transaction adds context that old models can’t even read.

ERP modernisation

Platforms like NetSuite, Shopify, and Lightspeed have gone API-first, opening their ecosystems for external intelligence. Forecasting and optimisation can now live above legacy stacks—enriching data, not replacing systems.

AI capacity leap

Reinforcement learning and agentic AI now let systems sense goals, weigh trade-offs, and act autonomously in real time.

The shift to adaptive intelligence

We’ve entered the era of adaptive intelligence where models don’t just forecast, they learn and decide.

RabbitHawk sits at the centre of this transformation. A goal-driven forecasting and optimisation engine that learns continuously from every signal, every outcome, every constraint.

RabbitHawk won’t just tell you what’s most likely to happen but will help you decide what should happen next.

Meet our team

Our founders bring decades of combined leadership in forecasting, optimisation, and autonomous decision systems with experience spanning Google, Walmart, Meta, and leading research centres in Australia and Europe.

Meet our team

For retailers and industry leaders

RabbitHawk integrates seamlessly with existing systems including NetSuite, Shopify, Lightspeed, SAP, delivering foresight and optimisation as a service, without replacing core infrastructure.

It helps teams to reduce working capital locked in inventory, improve forecast accuracy by up to 95%, cut manual workload by 90%, and align every decision to revenue, margin, or inventory goals in real time.

This is where forecasting ends — and foresight begins.

RabbitHawk solves retailer pain points

Painpoint

Heavy manual planning, relying on spreadsheets and intuition

Why it happens

Planners spend hours building spreadsheets, copying last year’s numbers, adjusting vaguely for trends. Forecast accuracy suffers.

How RabbitHawk outperforms

RabbitHawk automates the loop: ingesting source data, forecasting, optimising, learning. Planners spend time making decisions, not building spreadsheets.

Painpoint

Low forecasting accuracy, lost sales & excess inventory

Why it happens

Most retailers still rely on ‘what sold last year’ without social, trend, weather, or store-level variation context. The result is stock-outs or overstocks.

How RabbitHawk outperforms

RabbitHawk uses structured & unstructured data (like reviews, heat-maps, and social trends), one-off indicators and meta-learning ensemble models to raise accuracy and reduce inventory tie-up.

Painpoint

Low forecasting accuracy, lost sales & excess inventory

Why it happens

Most retailers still rely on ‘what sold last year’ without social, trend, weather, or store-level variation context. The result is stock-outs or overstocks.

How RabbitHawk outperforms

RabbitHawk uses structured & unstructured data (like reviews, heat-maps, and social trends), one-off indicators and meta-learning ensemble models to raise accuracy and reduce inventory tie-up.

Painpoint

Fragmented systems & unused unstructured data

Why it happens

Forecasting with multimodal unstructured data has been extremely difficult to achieve.

How RabbitHawk outperforms

RabbitHawk’s API card-based architecture connects to existing systems and ingests unstructured data without heavy system rebuilds.

Painpoint

Store-level misallocation

Why it happens

Generic central forecasting ignores local variations of size, trend, store demand. More granular forecasts tend to be inaccurate.

How RabbitHawk outperforms

RabbitHawk provides SKU × Store × Time-horizon granular forecasts, optimises transfers, replenishment, markdowns tailored to each store.

Painpoint

Seasonality & trend unpredictability

Why it happens

Planning 12–18 months ahead is tough when trend cycles shorten and external signals dominate.

How RabbitHawk outperforms

RabbitHawk dynamically ingests trend data, images, social signals, and continuously adjusts goals and forecasts; informing strategy for ‘what should sell next season.’

Painpoint

Promotions & pricing inefficiencies

Why it happens

Markdowns done late or too deep; promotions not aligned with forecasted demand; margin suffers.

How RabbitHawk outperforms

RabbitHawk’s optimisation agents run micro-simulations under margin, lead-time or MOQ constraints and recommend actionable pricing, transfer & bundle strategies in real time.

Painpoint

Slow rollout, high cost of specialist planning teams

Why it happens

Legacy F&O tools require heavy implementation, analyst teams, and lengthy deployment.

How RabbitHawk outperforms

RabbitHawk is cloud-native, light to deploy, bootstrapped model fits smaller retailers—delivering enterprise-grade capability without waiting for heavy tech projects.

Case study

Our services

Services

Our collaborative approach brings together strategic thinking and creative excellence to drive measurable outcomes.

Human-centered design

Strategic storytelling

Emotion-driven experiences

Touchpoint auditing

Experience cohesion

See all services

We fuse strategy and creativity to build brands that perform and inspire.

Get in touch

4.96/5 on clutch.co

We get results

Andersen delivers focused expertise to push brands and products forward

73%

Support reduction

Simplifying continuum's user interface led to a dramatic decrease in support tickets, even as their customer base tripled in size

42%

Efficiency uplift

Our collaboration with Chausseur transformed their assembly line controls, resulting in a 42% efficiency improvement

94%

Testing success

Our interface design for invarion's mixed reality controls achieved unprecedented user testing scores, setting new benchmarks for intuitive VR interaction