Services

How Excubator helps innovation driven enterprises “Master their start-up play” ?

Our consultants and platforms help you build the strategies to identify those start-up wins that help you build an initial focused set of domains and technologies. As it is important to run many small experiments to take away some significant wins, taking a portfolio approach and building a deal flow helps you to derisk your innovation efforts. Excubator helps you to harness many high quality startups.

Setting specific innovation objectives for a large number of startups needs the involvement of many people in an ecosystem. Excubator's ExSeed and ExNet platforms help you with that, and can help you manage these interconnected people well.

Picking the top few and removing the red tape can create a fast-track process to pilot offers in the market. Excubator helps you with a DMZ for this, if needed, at our own incubation facility.

Finally, building on the partnerships that Excubator has can help you to scale your selected startup partners and add value to your own customers.

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Case Study

HUAWEI DESIRES TO ENTER THE $10B AUTOMOTIVE AI MARKET

Can Start-up led innovation help?

Overview

Huawei Technology Ltd is one of the largest public enterprises in China, focused in building next generation network and sensor solution for the connected world. With the mission to achieve market leadership , Huawei Technology is focused in building AI solution for Automotive and 5G connected cars.

  The Solution: CrafLogic Algorithms and AI

CraftLogic developed a suite of Software AI Algorithms for ADAS and AR HUD to cover all the scenarios, optimized to meet the use case scenario on the target platform (iMX6 Dual Core).

Integration support to enable the AI Algorithm for the ADAS and AR HUD, Suite of Algorithm for ADAS and AR HUD, Eye Box Detection, Optical Image Stabilization, Grid View and Car View Maps, Object Detection and Mapping, Geometric Correction, Ambient Light Adjustment and Low Light enhancement, Driver Monitoring Algorithm (Fatigue, Yawning and Tiredness)

  The Challenge

Huawei has expertise in network and hardware solutions, but lacked expertise in Visual Computing and ML, required to rapidly build the next generation network AI Solutions for connected cars and the Automotive industry. Their vision of achieving market leadership in Automotive AI components (ADAS and ARHUD) in next 3 years requires achieving strict timelines, but limited prior experience in building Vision Computing and AI technology was slowing them down. Rapidly evolving competition led to frequent revision of product technical specifications and increases in the R&D budget.

Results and Benefits

Huawei continued to focus on overall product development across the overall Automotive segment, rather than focusing on building an internal AI team, which could also have led to delays due to training times.

Leveraging the readily available AI Algorithm components and expert team for integration from CraftLogic helped Huawei to meet the overall program timeline.

Overall, this decision to “buy vs build”, helped Huawei to minimize the risk of failure, saved over 12 months of effort, and also led to close to a 50% saving on their project R&D budget.

Case Study

MAERSK DESIRED A $40M SAVING ON OPERATIONS COSTS

Can Start-up led innovation help?

Overview

The largest container shipping firm in the world, AP. Moller - Maersk operates in 120 countries and 300 ports, and carries millions of tons of cargo everyday , with warehouses in shipping areas which contain thousands of parts for smooth operations of shipping. To manage these parts , their team would periodically visit warehouses and spend several weeks in each one to manually track each and every part. This was leading to huge operating costs.

  The Solution: MintM’s Computer Vision and AI

MintM clubbed OCR with its AI engine to understand labels on parts and find manufacturers and part numbers. This was enabled through a simple web interface so that any kind of phone could be used for taking pictures. AI ensured self-learning to adapt for new parts and manufacturers.

  The Challenge

Data revealed that this level of inventory mapping would involve a full-time team and would take over 5 years to complete all warehouse inventory measurement. In addition, the possibility of human error causing data mismatch, as well as the inability to keep up with latest data, would lead to both high costs as well as potential business losses.

Results and Benefits

MintM’s solution enabled the warehouse visiting team to reduce its mapping time by over 90%. This increased efficiency led to significant man power effort saving reduced cost. As the system is frequently updated, this led to up to date inventory information leading to better operations at ports and warehouses. In addition, the system was designed in such a way that it could learn about new manufacturers and parts so that no future intervention is required, leading to constant and ongoing savings in operating costs.

Maersk was not only able to improve efficiency and reduce cost by over 90% but this could also lead to an estimated $40 million in indirect cost savings due to improved port operations and reduced down time.