More sales. Less inventory. Proven on your data in four weeks.
An AI planning layer that sits on top of your ERP, understands your complexity, and proves itself on your own numbers before you commit to anything.
Supply chain doesn't fail in one place. It bleeds in six.
Most teams treat these as separate fires. They aren't. They share a common root: a plan built on incomplete visibility and a demand signal that was wrong before it left the spreadsheet.
No visibility
Inventory is somewhere in the network — in transit, stuck at a DC, quarantined, mislabeled. Nobody can answer 'how much do we really have, where, and usable when?' without a spreadsheet marathon.
Stockouts and overstocks — together
The same SKU is empty in one store and piled up in another. They aren't opposite problems; they're the same broken allocation showing up in two places at once.
Margin erosion
Excess ages into markdowns, expedites paper over misses, and the cost of carrying the wrong mix compounds silently until it hits the quarter.
Shrink and write-offs
Product that sits too long walks, expires, or gets damaged. Every extra week of cover is another week of shrink risk you can't see in the forecast.
Lost sales invisible to the P&L
A customer who leaves empty-handed doesn't file a report. Censored demand hides the real gap between what you sold and what you could have sold.
Planners drowning in exceptions
Teams spend the week rebuilding the plan in Excel instead of acting on it. By the time the meeting starts, the numbers are already stale.
One planning layer that sees the network end-to-end, decides continuously, and learns from every miss.
Praxima unifies visibility, forecasting, inventory policy, allocation, and replenishment in a single control loop — supervised by your planners, auditable line by line, and running in parallel to your ERP so nothing has to be replaced to prove it works.
It starts with a reconciled view of inventory across every node and status, so you know what you really have, where, and when it becomes usable. It reconstructs demand signals that account for stockouts, promotions, seasonality, and new product behavior. It turns those signals into SKU-level policies — safety stock, reorder points, lot sizes, cover targets — that adapt as lead times and volatility change.
Then it pushes recommendations into purchase orders and transfers, and explains every number so your team can approve, challenge, or override in minutes, not days. The loop keeps learning: every miss is fed back, every override is captured, and the next plan is sharper than the last.
Why one-size-fits-all fails
"The value isn't the model. It's the model tuned to your data."
An intelligence layer that sits on top of your ERP — not instead of it.
Praxima runs in parallel to your current system, scoped to a set of SKUs or a region. Nothing to rip out, nothing to replace. Forecasting, inventory policy, and supply and distribution planning, with a senior planner supervising the agents.
The planner's cockpit — everything that needs attention, in one place.
Control Tower
Exceptions, signals and what needs your attention today
Exception Queue
Ranked by impact — click to drill into the SKU
Calendar
Next 90 days
Not "we do AI." Here's the engine.
A full planning stack — visibility, inventory policy, allocation, and forecasting — wired into one loop. Forecasting is one pillar, not the whole product.
AI runs the loop. A planner stays in charge.
Praxima closes the loop from signal to purchase order with an explainable, human-in-the-loop workflow — so planners supervise the work instead of drowning in it.
Ask why a number moved — not a black box.
Every recommendation comes with a plain-language rationale. Planners can interrogate any number, challenge assumptions, and feed corrections back into the models. Trust is built through transparency.
Where Praxima delivers value
Each capability targets a specific margin leak — built on the demand signal we reconstruct.
Don't trust us. Test us.
On your data. Against what you run today.
No slideware, no cherry-picked demo. Send us your history and we'll run a backtest head-to-head with your current planning solution. Same SKUs. Same weeks. Same rules. A scoreboard at the end — and you keep it either way.
The honest accuracy metric. Weights errors by volume, so misses on your fast-movers count more than misses on the long tail. Unlike MAPE, it doesn't blow up on low-volume SKUs — which is exactly where most forecasts lie about themselves.
Accuracy without bias is a coin flip. Persistent positive bias means chronic overstock and markdowns; negative bias means chronic stockouts and lost sales. We measure it per segment so you can see where the model is quietly costing you margin.
Volatility of the miss. Low σ means safety stock can shrink without breaking service levels. High σ is what forces planners to hold buffer everywhere. This is the metric that translates directly into working capital.
A blind test. The past is the referee.
Backtesting replays history: we hide the most recent weeks from both models, let each one forecast them, then compare their predictions to what actually happened. It's the only way to compare planning solutions without a year-long pilot — and without either side gaming the setup.
We show you exactly how we beat your current solution and by how much — then translate that lift into what a better forecast actually unlocks for your business:
We've operated supply chains — not just modeled them.
Praxima was founded by two people who have run supply chain at global scale and built the algorithms behind it. We're operators solving supply chain, not consultants documenting it.
Carlos has spent his career on the operator side of supply chain, where forecasts either hold up or they don't. He has led AI replenishment programs at global retailers and worked as an embedded product leader for private-equity-backed companies, turning ambitious roadmaps into shipped systems. He built Praxima to give mid-market and enterprise teams the caliber of planning intelligence that previously had to be built from scratch.
Milton is the architect behind Praxima's forecasting engine. He has led supply-chain and inventory data science at global retailers and holds a PhD in predictive analytics. His work spans mixture-of-experts architectures, foundational time-series models, and reinforcement learning for inventory policy. He designed PlannerStudio to be modular and API-first from day one.
Serving clients across the Americas and Europe.
Let's talk.
Scope a four-week Proof of Value on your own data. Quick intro call, no commitment.