Let’s be honest—most “analytics” systems are just glorified mirrors.
They tell you what happened, dress it up in charts, and call it insight.
Meanwhile, the world moves on. Demand changes. Supply collapses. Competitors act faster.
And your “intelligent” system? It’s still waiting for someone to export the data to a spreadsheet so they can have a play with it.
That’s why the next leap isn’t another dashboard.
The next leap is self-learning agentic loops—systems that don’t just predict but act, measure, and improve themselves with every cycle.
That’s the world RabbitHawk is building.
Forecasting that self-optimises in real time.
The end of passive forecasting
An agentic loop repeats—continuously and automatically.
Every action becomes an experiment:
“We transferred 40 units from Store A to B.”
“We raised price by 3%, not 10.”
“We delayed the next PO by one week.”
And with each action, learning about which actions actually worked.
It doesn’t need a meeting. It doesn’t need permission.
Reinforcement learning and constrained decision-making, brought out of ‘the lab’ at Monash University and into real retail.
If it's that easy. Why doesn’t everyone do it
Most enterprise forecasting and optimisation tools break at the same four points:
- They stop at prediction. “Here’s the forecast.” Great - now what?
- They assume the world is static. Lead times, demand, risk - frozen in time.
- They ignore goals. You care about cashflow, service levels, and margin - all at once. Forecasting and Optimisation models don’t.
- They don’t learn from impact. A promo pulls demand forward, wrecks next month’s margin - and the system forgets instantly.
So you get “AI” reports, PowerPoints full of KPIs… and a planning team still guessing in spreadsheets.
When the Loop Learns
Now imagine the opposite.
Forecasts that adapt, and optimisations that adjust, based on what actually worked.
Constraints - MOQ, budgets, lead times - that are built in, not workarounds.
A system that gets faster, not older. Learning from its own mistakes - the way people do - training a behaviour.
A perfect forecast paired with dumb decisions is still dumb.
A rougher forecast that learns, adapts, and self-corrects will win every time.
The drive for forecast accuracy is replaced with new concerns:
- How fast can the system notice it’s wrong?
- How quickly can it recover?
- How safely can it explore better options without breaking the business?
It doesn’t just look at the world. It learns from it. Turning analytics from passive hindsight into active foresight.
