Sarine goes the diversification way to meet challenges!

 

Acquires a minority stake in Kitov.Ai for the gem & jewellery industry  

Sarine Technologies Ltd, wishes to update the public on its closing of its acquisition of a minority stake in Kitov.ai. The purpose of this investment is the diversification of Sarine's focus to additional industries, also considering the current challenges the diamond jewellery industry faces.  

Being a company engaged in technologies similar to those employed by Sarine, thus speaking the same language, Kitov.ai provides the Group with the means to diversify into new fields separate from the diamond industry.  

Kitov.ai has many industry-leading customers in many varied industries including aerospace, defense, electronics, medical devices, energy control, consumer products and others, in the U.S., Europe, the Far East and Israel. The concluded deal includes an initial cash investment of US$ 4.1 million in consideration of a just over 33% stake in Kitov.ai, paid in part to the existing shareholders of Kitov.ai and in part infused into Kitov.ai as working capital.  

Sarine is also lending Kitov.ai an additional US$ 2.6 million, in the form of a convertible loan, which, not before 01 January 2027 and not after 15 February 2028, can be converted, at Sarine’s sole discretion, into additional equity shares, bringing Sarine's total stake in Kitov.ai to 51%.  

The transaction was conducted based on Kitov.ai's pre-money valuation of US$ 10.635 million. If Sarine does decide to convert into equity said convertible loan, possibly subject to shareholders' approval, the following arrangements shall apply:

1: Sarine may be required to pay (during 2027 or during the first quarter of 2028) certain current shareholders a conditional earnout of US$ 1.0 million triggered by meeting defined profitability goals; 

2: Kitov.ai, the company itself, not Sarine, may be required to pay (in 2029) certain current shareholders an additional conditional earnout payment of up to US$ 2.0 million, triggered and calculated by meeting certain sales and profitability goals;  

3: The current shareholders of Kitov.ai may exercise (during the second half of 2029) a Put Option and sell to the Company all or part of Kitov.ai shares held by them at such time, based on a valuation set by an independent appraiser, at a consideration not to exceed US$12 million. This Put Option will not be in force, if before mid-2029 equity altering events occur, such as a merger, a public offering, etc.  

If Sarine does not convert the aforesaid convertible loan, the aggregate consideration payable by Sarine for its 33% of Kitov.ai's shares shall be ~US$ 4.1M and the said convertible loan shall bear interest and be payable to Sarine. The investment documents also include minority-protection clauses, throughout the different phases of the above transaction.  

Kitov.ai has developed and markets an AI‑driven automated 3D visual inspection system that can concurrently check products for the existence of key elements and their correct positioning along with the inspection of surface finishing, labels, barcodes, etc. Kitov.ai's solution can be implemented, due to its flexible software characteristics, to inspect completely different parts at the same installation point in high‑mix/low‑volume manufacturing environments.

Kitov.ai's patented CAD2Scan AI-based inspection definition software package is unique in that the design engineer utilises the part's CAD model to directly define the inspection requirements (where to inspect, what to inspect for, pass/fail criteria, etc.) in minutes, without considering computer vision or automation issues, and without having to walk the robot through the inspection steps, creating a flexible inspection regime, reducing engineering effort and accelerating new‑product conversion from prototype to manufacturing.  

Kitov.ai's patented technologies automatically select the best viewpoints and lighting1 for each feature inspected and compute the most efficient robot path between them – optimising the eyes (vision), the hand (robotics) and the brain (AI) integration and improving failure detection beyond conventional approaches.

By integrating intelligent robotic image acquisition, CAD-based part definition2, cross-part learning, as detailed below, and even multi-sensor feedback (Kitov supports multiple sensors data acquisition on a single path of the robotic inspection head), Kitov’s system pushes far beyond the limitations of conventional machine vision inspection systems.  

Another key innovative aspect of Kitov's solution is cross part learning- namely how to overcome the problem of data scarcity, that plagues industrial deep learning applications. In high-quality manufacturing it’s common to have a relatively low number of defective samples for any given part type. Though obviously a positive for the manufacturer, training an AI model on only a handful of examples is challenging.  

Kitov addresses this by utilising images from different parts that have visually similar characteristics and creating a unified defect detection network on the aggregated data. In practice, this means that Kitov’s software pools the information derived from the inspection of distinct parts of the same material or finish. 

A scratch on an anodised aluminum aircraft bracket looks similar to a scratch on an aluminum valve's body. This unified, learning creates a model of scratches per se, across all similar-looking parts, resulting in a more robust AI model.  

The AI network detects defects better because it has seen multiple contexts across different shapes and sizes. Kitov’s AI gains broader experience, similar to how a seasoned human inspector applies his experience to new products. Kitov's AI breaks the glass ceiling of traditional deep learning, which typically requires training each model on one specific part with lots of examples.  

Kitov.ai's open, vendor‑neutral platform (use whichever CAD software, whichever robot, whichever lighting, whichever camera, etc.) integrates seamlessly with existing factory automation and inspection tools, enabling OEMs and integrators to collaborate and quickly deploy solutions within a constantly expanding partner ecosystem.  

Kitov.ai's next innovation will be the introduction of its model-based enterprise (MBE) solution in 2026. MBE's benefits for an organisation are that it leverages 3D digital models to integrate and manage technical processes throughout the product's lifecycle.  

An MBE uses an authoritative 3D model that contains all the necessary information pertaining to the design, manufacturing, inspection, cost and pricing of a product. This enhances collaboration and communication across plant departments, reduces errors and optimises corrective cycles, ultimately leading to faster product development and reduced overall costs.