is raising pre-seed capital
is raising pre-seed capital
is raising pre-seed capital
download our pitch deck here
book a quick intro call
book an intro call
or email us at hello@kapnetix.ai
or email us at hello@kapnetix.ai
frequently asked questions
frequently asked questions
What is your long-term vision for Kapnetix?
Our vision is to democratize AI in the gaming industry, enabling professionals to focus more on creativity and less on repetitive tasks. We aim to bridge the gap between cutting-edge AI research and practical, accessible applications for the gaming community. By doing so, we plan to revolutionize the way games are created, enhancing both the creative process and the end-user experience. With this game developers can focus on creating the experience we fall in love with.
What is the core problem your technology addresses?
Our technology targets inefficiencies in the various game development processes. We have started with motion capture and significantly reduce the time required for motion capture cleanup, transforming a traditionally manual and labor-intensive task into an automated process. An hour of raw motion capture data, that typically can take 8-40 hours of cleanup, can be cleaned in 10 minutes, providing a quality level on par with professionals.
Why do users want this?
1. Rising development costs and competition: Game development costs are escalating as studios strive to meet rising consumer expectations for AAA quality and immersive experiences. This financial pressure is pushing studios to seek more efficient production methods to maintain profitability. 2. Demand for faster and more agile production processes: The industry is moving towards faster production cycles, with a focus on developer velocity and release stability. Studios are looking for tools that can support rapid iteration and continuous delivery, essential for live services and regular updates
What so special about the Kapnetix team?
Our team has a distinctive blend of professional game development and animation expertise and advanced AI research and development. With backgrounds in notable studios such as Illusion Softworks, 2K Games, Hangar 13, and Vatra Games, our team brings a rich industry experience. On the AI front, we have successfully completed over 120 projects across 12 industries and have been honored with 5 international innovation and growth awards over the past decade.
How does your technology stand out in the market?
Kapnetix's unique selling point is our advanced AI-driven approach to motion capture cleanup. Unlike traditional optical mocap or markerless mocap technologies, our AI automates editing in minutes, saving significant time and production costs.
Why do users want this?
1. Rising development costs and competition: Game development costs are escalating as studios strive to meet rising consumer expectations for AAA quality and immersive experiences. This financial pressure is pushing studios to seek more efficient production methods to maintain profitability. 2. Demand for faster and more agile production processes: The industry is moving towards faster production cycles, with a focus on developer velocity and release stability. Studios are looking for tools that can support rapid iteration and continuous delivery, essential for live services and regular updates
What’s the market size and opportunity?
Our technology is creating a new category of automation replacing a massive and unwanted manual effort. Therefore we calculate the market size based on the market cost of this inefficiency. LinkedIn Search for people working with motion capture technology 310,000. Industries include - Game and movie productions, AR/VR, Sports performance analysis, Medical simulation (biomechanics). LinkedIn Search for people working with motion capture technology in gaming 18,000. The average salary across major geographies (USA, Canada, EU, UK, Switzerland, Japan, Australia and New Zealand) is $65,000 USD Motion capture cleanup and associated tasks in the game industry is on average a 70% effort. The market value that Kapnetix is capturing in the gaming industry is 18000 * 65000 * 70% = $800,000,000 If we go beyond gaming then the effort to do cleanup will drop to an average of 20%. The market value that Kapnetix is capturing in total is 310000 * 65000 * 20% = $4,000,000,000 This means that companies using motion capture are globally loosing $4bn USD every year due to a manual effort that we can automate.
What makes your technology difficult or impossible to copy?
Overall there’s a huge barrier in acquiring data on the level that we have. You need a couple of things: —Various mocap tech (a pro optical system starts at 30k USD) —Mocap actors able to perform a variety of motions —You need to understand in which scenarios current mocap solutions break —You need a professional to validate the cleaness of the data —You will need at least 10 hours of raw and cleaned footage pairs (skeletons and marker cloud) —You need a variety of rigs if you plan to train retargeting of characters Now the co-operation especially with 2K games has enabled us to achieve a massive head start. We have access to local motion capture setup at Masaryk University in Brno, just 15 minutes away by walk. They are happy to help with cleanup on best effort and we have Bohemia Interactive offering a studio and professional services as an alternative paid option.
Who are your competitors and how do you differentiate from them?
Our competitors include established optical mocap companies like Vicon, Optitrack, and Qualysys, as well as companies like move.ai using AI model to capture motion. Most of these players are focused on simplifying capture but have been very slow to improve final capture quality. We distinguish ourselves by offering quicker and more accurate cleanup processes and having strategic partnerships with industry leaders for data access and expertise. Going beyond motion capture we will face competition through companies building tools for game developers. This includes the likes of Autodesk, Unity, Epic Games, or newcomers like Luma.ai creating 3D assets.
Can you describe your current project stage?
We're at the MVP stage, with active collaborations with industry giants like 2K Games. Our technology is being employed in major game series, demonstrating its practical application and effectiveness. We’ve recently onboarded 1 game development studio and an app developer creating a fitness app with 3D avatars.
What is your strategy for scalable product deployment?
Our deployment strategy relies on our own Kubernetes-based platform compatible with major cloud services, ensuring scalability, flexibility, and robust deployment capabilities for our clients. For the largest customers we can provide the platform as a whole and provide integration points for their pipelines.
What are the unique features of your product?
Our product stands out due to its cloud-native model, independence from hardware constraints, working with well-known tools and data formats, rapid processing capabilities, and unmatched accuracy in motion capture cleanup.
How do you ensure the technical quality of your product?
We maintain high-quality standards by measuring our technology against professional benchmarks and aiming for at least a 50% reduction in manual processing time. Customer feedback and real- world application success are key metrics for us.
Can you explain how the AI part works?
Our solution uses a set of neural network models (time-convolutional neural network, transformers) specifically designed for motion capture tasks. The whole motion capture cleanup process is broken down with an aim to always provide quality results in the next step of the process. We bring it back together using a triangulation method or our inverse kinematics system, depending on the cleanup stage. While the cleanup of the marker cloud may not be as valuable to developers, we do it because we get better accuracy for skeleton solving, which uses marker cloud data as an input. A distinctive aspect of our approach lies in how we prepare our training data. We generate artificial errors in clean animations automatically, which are then used for training our models. This means that we primarily use raw-clean animation pairs for evaluation purposes only, not for training. It's common for clients to retain only the cleaned animations, not the raw-clean pairs, due to storage considerations so we combine real and artificial data points to make a better training set.
What are your technical milestones for the upcoming year?
Over the next 12 months, we aim to develop a more universal model, launch the first version of our user-friendly interface, work on integration into popular tools, continue our AI research, and improve our data acquisition for model training and validation. On the research front we are planning to work on motion capture retargeting, single cam mocap and explore ai-assisted editing. We are also exploring validation of animations for QA purposes. We will strongly focus on data acquisition as it will play a key role in what market share we can achieve and provide strong defensive moats.
What is your long-term vision for Kapnetix?
Our vision is to democratize AI in the gaming industry, enabling professionals to focus more on creativity and less on repetitive tasks. We aim to bridge the gap between cutting-edge AI research and practical, accessible applications for the gaming community. By doing so, we plan to revolutionize the way games are created, enhancing both the creative process and the end-user experience. With this game developers can focus on creating the experience we fall in love with.
What is the core problem your technology addresses?
Our technology targets inefficiencies in the various game development processes. We have started with motion capture and significantly reduce the time required for motion capture cleanup, transforming a traditionally manual and labor-intensive task into an automated process. An hour of raw motion capture data, that typically can take 8-40 hours of cleanup, can be cleaned in 10 minutes, providing a quality level on par with professionals.
Why do users want this?
1. Rising development costs and competition: Game development costs are escalating as studios strive to meet rising consumer expectations for AAA quality and immersive experiences. This financial pressure is pushing studios to seek more efficient production methods to maintain profitability. 2. Demand for faster and more agile production processes: The industry is moving towards faster production cycles, with a focus on developer velocity and release stability. Studios are looking for tools that can support rapid iteration and continuous delivery, essential for live services and regular updates
What so special about the Kapnetix team?
Our team has a distinctive blend of professional game development and animation expertise and advanced AI research and development. With backgrounds in notable studios such as Illusion Softworks, 2K Games, Hangar 13, and Vatra Games, our team brings a rich industry experience. On the AI front, we have successfully completed over 120 projects across 12 industries and have been honored with 5 international innovation and growth awards over the past decade.
How does your technology stand out in the market?
Kapnetix's unique selling point is our advanced AI-driven approach to motion capture cleanup. Unlike traditional optical mocap or markerless mocap technologies, our AI automates editing in minutes, saving significant time and production costs.
Why do users want this?
1. Rising development costs and competition: Game development costs are escalating as studios strive to meet rising consumer expectations for AAA quality and immersive experiences. This financial pressure is pushing studios to seek more efficient production methods to maintain profitability. 2. Demand for faster and more agile production processes: The industry is moving towards faster production cycles, with a focus on developer velocity and release stability. Studios are looking for tools that can support rapid iteration and continuous delivery, essential for live services and regular updates
What’s the market size and opportunity?
Our technology is creating a new category of automation replacing a massive and unwanted manual effort. Therefore we calculate the market size based on the market cost of this inefficiency. LinkedIn Search for people working with motion capture technology 310,000. Industries include - Game and movie productions, AR/VR, Sports performance analysis, Medical simulation (biomechanics). LinkedIn Search for people working with motion capture technology in gaming 18,000. The average salary across major geographies (USA, Canada, EU, UK, Switzerland, Japan, Australia and New Zealand) is $65,000 USD Motion capture cleanup and associated tasks in the game industry is on average a 70% effort. The market value that Kapnetix is capturing in the gaming industry is 18000 * 65000 * 70% = $800,000,000 If we go beyond gaming then the effort to do cleanup will drop to an average of 20%. The market value that Kapnetix is capturing in total is 310000 * 65000 * 20% = $4,000,000,000 This means that companies using motion capture are globally loosing $4bn USD every year due to a manual effort that we can automate.
What makes your technology difficult or impossible to copy?
Overall there’s a huge barrier in acquiring data on the level that we have. You need a couple of things: —Various mocap tech (a pro optical system starts at 30k USD) —Mocap actors able to perform a variety of motions —You need to understand in which scenarios current mocap solutions break —You need a professional to validate the cleaness of the data —You will need at least 10 hours of raw and cleaned footage pairs (skeletons and marker cloud) —You need a variety of rigs if you plan to train retargeting of characters Now the co-operation especially with 2K games has enabled us to achieve a massive head start. We have access to local motion capture setup at Masaryk University in Brno, just 15 minutes away by walk. They are happy to help with cleanup on best effort and we have Bohemia Interactive offering a studio and professional services as an alternative paid option.
Who are your competitors and how do you differentiate from them?
Our competitors include established optical mocap companies like Vicon, Optitrack, and Qualysys, as well as companies like move.ai using AI model to capture motion. Most of these players are focused on simplifying capture but have been very slow to improve final capture quality. We distinguish ourselves by offering quicker and more accurate cleanup processes and having strategic partnerships with industry leaders for data access and expertise. Going beyond motion capture we will face competition through companies building tools for game developers. This includes the likes of Autodesk, Unity, Epic Games, or newcomers like Luma.ai creating 3D assets.
Can you describe your current project stage?
We're at the MVP stage, with active collaborations with industry giants like 2K Games. Our technology is being employed in major game series, demonstrating its practical application and effectiveness. We’ve recently onboarded 1 game development studio and an app developer creating a fitness app with 3D avatars.
What is your strategy for scalable product deployment?
Our deployment strategy relies on our own Kubernetes-based platform compatible with major cloud services, ensuring scalability, flexibility, and robust deployment capabilities for our clients. For the largest customers we can provide the platform as a whole and provide integration points for their pipelines.
What are the unique features of your product?
Our product stands out due to its cloud-native model, independence from hardware constraints, working with well-known tools and data formats, rapid processing capabilities, and unmatched accuracy in motion capture cleanup.
How do you ensure the technical quality of your product?
We maintain high-quality standards by measuring our technology against professional benchmarks and aiming for at least a 50% reduction in manual processing time. Customer feedback and real- world application success are key metrics for us.
Can you explain how the AI part works?
Our solution uses a set of neural network models (time-convolutional neural network, transformers) specifically designed for motion capture tasks. The whole motion capture cleanup process is broken down with an aim to always provide quality results in the next step of the process. We bring it back together using a triangulation method or our inverse kinematics system, depending on the cleanup stage. While the cleanup of the marker cloud may not be as valuable to developers, we do it because we get better accuracy for skeleton solving, which uses marker cloud data as an input. A distinctive aspect of our approach lies in how we prepare our training data. We generate artificial errors in clean animations automatically, which are then used for training our models. This means that we primarily use raw-clean animation pairs for evaluation purposes only, not for training. It's common for clients to retain only the cleaned animations, not the raw-clean pairs, due to storage considerations so we combine real and artificial data points to make a better training set.
What are your technical milestones for the upcoming year?
Over the next 12 months, we aim to develop a more universal model, launch the first version of our user-friendly interface, work on integration into popular tools, continue our AI research, and improve our data acquisition for model training and validation. On the research front we are planning to work on motion capture retargeting, single cam mocap and explore ai-assisted editing. We are also exploring validation of animations for QA purposes. We will strongly focus on data acquisition as it will play a key role in what market share we can achieve and provide strong defensive moats.