Gain Unveils AI Employees for Procurement with $12M Seed Funding

Oct 9, 2025
Gain, a deeptech startup from Tel Aviv, has emerged from stealth with a $12 million seed funding round led by The Garage, introducing AI Employees for end-to-end procurement processes.

Gain, a deeptech startup based in Tel Aviv, has emerged from stealth with the announcement of a $12 million seed funding round led by The Garage, announced in a press release. The company has introduced AI Employees designed to manage end-to-end procurement and operational workflows, aiming to optimize processes in industries such as retail, CPG, energy, and chemicals.

Gain's AI Employees are equipped with advanced reasoning capabilities, allowing them to handle tasks ranging from category strategy and negotiations to contract execution and transaction processing. These AI Employees are currently being piloted by global enterprise clients, including Tempo Beer Industries and Bazan Group, who are testing the technology's ability to integrate seamlessly with existing ERP and procurement systems.

The funding will support further R&D efforts, including the launch of additional AI Employees and expansion into the U.S. and European markets. Gain's approach disrupts the traditional SaaS pay-per-seat model by offering a pay-per-outcome pricing structure, which aligns costs with results. This innovative model is expected to encourage early adoption and provide measurable ROI for clients.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like AI Funding Brief, Enterprise AI Brief or Daily AI Brief.

Also, consider following us on social media:

AI Funding News Enterprise AI AI Brief AI Brief (X)
Free newsletter

Enterprise AI Brief

Weekly report on AI business applications, enterprise software releases, automation tools, and industry implementations.

AI Token Webinar

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more

You may also like

Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.