B2i Technologies provides adaptable, fully integrated corporate and investor relations website solutions built for complete control and effortless integration.
Our platform is engineered to blend smoothly into a company’s existing corporate website, rather than requiring migration to a proprietary hosted environment.
Using modular data components, presentation-ready APIs, and a WordPress plugin, we enable comprehensive investor relations functionality to operate directly within your current digital ecosystem.
In contrast to many alternative providers that host IR content on proprietary systems or isolated subdomains, our approach stands apart.
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Most AI video tools hand you a black box: closed weights, a subscription, and no way to see what is happening under the hood. LTX takes the opposite approach. Built by Lightricks, LTX is an open foundation model that generates and simulates across video, audio, and the physical world, and it puts the weights, the code, and the control in your hands.
At the center of the model is LTX-2.5, a 22B-parameter dual-stream diffusion transformer that produces native 4K video at up to 50 frames per second, with audio and video generated together in a single pass rather than stitched together afterward. Artificial Analysis, an independent benchmarking group, currently ranks LTX among the top three AI video models in the world.
You choose how you want to use it. Download the open weights and run LTX-2.5 on your own hardware. License the model for on-premise deployment backed by enterprise support. Or build directly on LTX Studio, the production suite that turns the model into a full creative workflow. Companies like ElevenLabs, Asteria Film Co., Magnopus, and NVIDIA already rely on LTX for their own work.
LTX is not built for one-off social clips. It is infrastructure for teams that generate motion, audio, and physical environments as part of their own products and pipelines.
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Seaweed
Seaweed, an advanced AI model for video generation created by ByteDance, employs a diffusion transformer framework that boasts around 7 billion parameters and has been trained using computing power equivalent to 1,000 H100 GPUs. This model is designed to grasp world representations from extensive multi-modal datasets, which encompass video, image, and text formats, allowing it to produce videos in a variety of resolutions, aspect ratios, and lengths based solely on textual prompts. Seaweed stands out for its ability to generate realistic human characters that can exhibit a range of actions, gestures, and emotions, alongside a diverse array of meticulously detailed landscapes featuring dynamic compositions. Moreover, the model provides users with enhanced control options, enabling them to generate videos from initial images that help maintain consistent motion and aesthetic throughout the footage. It is also capable of conditioning on both the opening and closing frames to facilitate smooth transition videos, and can be fine-tuned to create content based on specific reference images, thus broadening its applicability and versatility in video production. As a result, Seaweed represents a significant leap forward in the intersection of AI and creative video generation.
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NVIDIA Synthetic Video Detector
The NVIDIA Synthetic Video Detector is an advanced microservice powered by AI, specifically created to assess whether a video is genuine or generated by artificial intelligence. Its primary focus is on content generated through diffusion models, making it particularly suitable for applications in media authentication, digital forensics, content verification, broadcast processes, and ensuring the integrity of media. The tool evaluates MP4 video inputs and provides a prediction for each individual frame on a continuum from 0 to 1; where values leaning towards 0 suggest authenticity and those nearing 1 indicate synthetic origins. Furthermore, it is engineered to maintain its effectiveness even under typical video compression scenarios, which helps in sustaining reliable detection capabilities after the footage has undergone processing or distribution via standard media channels. Utilizing a Vision Transformer architecture that incorporates an ensemble of DINOv2 and DINOv3 backbones, it adeptly merges visual representations to differentiate between real and artificially created video content. Input frames are resized to 504 x 504 pixels and subjected to normalization prior to the inference process, ensuring optimal performance in the analysis. This sophisticated approach enables a robust assessment of video authenticity, making it a vital tool in the evolving landscape of digital media verification.
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