The AI that reads a supply chain the way a doctor reads an X-ray - and finds the bottleneck before it finds you.
CAPTION: A 630-pixel logo for a company obsessed with a single number. On the factory floor, throughput is not a metaphor - it is the rate at which cash actually leaves the building.
Somewhere tonight a plant manager is staring at two problems that are secretly the same problem. Aisle 7 is drowning in a product nobody ordered. Aisle 12 is out of the one thing everybody wants. The spreadsheet says everything is fine. The spreadsheet is lying.
This is the world ThroughPut.ai was built for - the gap between what the data records and what anyone should actually do about it. Most software is happy to tell you what happened yesterday. It ships a dashboard, blinks a red number, and leaves the decision to you at 2 a.m. ThroughPut took the opposite bet: the dashboard is not the product. The decision is.
The company is a Silicon Valley outfit with an unglamorous fixation. While the rest of the Valley chased chatbots, ThroughPut pointed its machine learning at cement plants, food processors, and spare-parts depots - the physical economy, where a bad inventory call is not a rounding error but a truck that leaves empty. Its platform ingests time-stamped operational data and works backward from one deceptively simple question: what does the customer actually want, and can we make it in time?
The best way to cut down on cost, effort, and waste is to work backward from your true customer demand and match that with your actual capacity.
That sentence is the whole religion. It sounds obvious the way most profound things do. But most supply chains are run the other way around - forecast first, hope later. ThroughPut inverts it, and names the tool that does the inverting ELI: a machine-learning and language engine whose entire job is to find the slowest link in the chain and do something about it. Reorder here. Cancel there. Rebalance the whole board while you sleep.
Point it at your ERP, MES, WMS or TMS data. It connects the dots most planning tools leave scattered.
Visualizes the whole network and flags the real bottleneck - not the loudest alarm, the actual constraint.
Reads true customer demand and its consistency, so plans start from reality instead of a guess.
Matches inventory and resources to sensed demand - freeing cash trapped in the wrong aisle.
Optimizes product allocation and throughput across the distribution network end to end.
Rebalances spare-parts inventory so the part is there when the machine goes down - not a week later.
The ML + NLP core that ties it all together: automated ordering, cancellation, rebalancing, and bottleneck detection.
Start from what customers truly want, not last year's forecast.
Map the actual capacity you have across the network.
ELI locates the single constraint slowing everything down.
Reorder, cancel, rebalance - decisions, not just charts.
The founding team is unusual for a software company: it managed real logistics - including war-zone and oilfield operations - before it wrote a line of supply chain AI.
Ran onshore, offshore and war-zone logistics; one of the youngest Geomarket production leads at Schlumberger before founding ThroughPut.
An 8x serial entrepreneur with 6 exits. ThroughPut is his 4th Industrial IoT & AI company.
Leads the technical architecture behind ELI, turning industrial data into decisions at scale.
Mid-market and enterprise manufacturers, distributors and industrial operators - the businesses where a bad call costs a truckload, not a click.
Roughly $9.95M raised across its life - deliberately, and revenue-first.
Raised amid record momentum from industry angels, family offices and industrial executives - investors with ties to Robinhood, project44 and NeXT - while preparing for a Series A. The company reported 12+ industry awards in 2021.
Early support from SAP.io Foundry Munich, Plug and Play Ventures, Plug and Play Japan, TechCode Accelerator and Epicenter Memphis.
See ELI in motion and hear the philosophy straight from the founder.
Return to that plant manager and the two aisles. Aisle 7's overstock and aisle 12's stockout were never two problems - they were one bottleneck wearing two costumes.
With ELI running underneath, the red number is no longer a riddle to solve at 2 a.m. It arrives with a recommendation attached: move this, cancel that, reorder the other. The truck leaves full. The cash that was hiding in the wrong aisle walks out the door where it belongs. Nobody stayed up staring at a spreadsheet that lied.
That is the quiet thing ThroughPut.ai is really selling. Not a dashboard. Not a forecast. A supply chain that stops reacting and starts deciding - and a plant manager who finally gets to go home.