Shofo: Building the Common Crawl for Video AI

Shofo is a San Francisco–based startup founded in 2025 that set out to solve one of the most pressing and least visible problems in modern artificial intelligence: access to high-quality, large-scale video data. As AI systems rapidly evolve beyond text and images toward fully multimodal understanding, video has become the most valuable—and most difficult—training input. Shofo positions itself as a “Common Crawl for Videos,” offering AI labs a way to tap into massive volumes of structured, labeled social media video without having to build complex infrastructure themselves.

Video is uniquely powerful as a training signal. It contains motion, context, temporal relationships, and real-world interactions that static data simply cannot provide. For models focused on robotics, embodied AI, activity recognition, or multimodal reasoning, video data is no longer optional. However, acquiring this data at scale is extremely challenging. Platforms are fragmented, access is restricted, and raw content is noisy, inconsistent, and unstructured. Shofo exists precisely at this intersection—where demand for video data is exploding, but supply remains difficult to unlock.

By continuously collecting, indexing, and enriching public social media videos, Shofo transforms chaotic streams of content into model-ready datasets. Instead of forcing AI teams to spend months scraping, cleaning, labeling, and validating data, Shofo delivers curated, task-specific datasets that can be used immediately for pre-training or fine-tuning.

What Problem Are AI Labs Struggling With When It Comes to Video Datasets?

The central problem Shofo addresses is not the absence of video content, but the absence of usable video datasets. Social platforms generate billions of videos, yet most of that data is effectively inaccessible to AI labs. APIs are limited, scraping is brittle, and legal or technical barriers often make large-scale collection impractical. Even when raw data is obtained, it is rarely in a form suitable for machine learning.

AI models require structured inputs: segmented clips, consistent formats, clean metadata, and precise labels. Raw social media videos, by contrast, are messy. They include irrelevant frames, inconsistent lighting, watermarking, variable camera angles, and unpredictable content. Extracting meaningful signals—such as object interactions, human activities, or contextual cues—requires sophisticated processing pipelines that many teams are not equipped to build.

This challenge is especially acute for video-centric models. Unlike text, where large public datasets already exist, or images, where benchmarks and repositories are well established, video data lacks a standardized equivalent of Common Crawl. Each AI lab is forced to reinvent the same data infrastructure, burning time, money, and engineering effort on non-differentiating work. Shofo’s insight was that this inefficiency is systemic—and therefore solvable with a shared platform.

How Does Shofo’s “Common Crawl for Videos” Actually Work?

Shofo’s core product is a large-scale, continuously updated index of public social media videos. The company began with TikTok, a platform rich in short-form, interaction-heavy content, and is expanding across all major social platforms. Instead of collecting data on demand, Shofo maintains an always-on ingestion system that monitors, indexes, and refreshes content in real time.

Once videos are ingested, Shofo runs them through a comprehensive end-to-end pipeline. This pipeline segments videos into meaningful units, sanitizes low-quality or unusable content, and applies a range of computer vision and reasoning models to extract structured information. Object detection, activity recognition, temporal segmentation, and interaction analysis are all part of the process.

The result is not a generic dataset, but a flexible foundation. AI labs can query Shofo’s index using highly specific criteria—such as activities, objects, environments, or interactions—and receive datasets tailored to their exact needs. For example, a research team working on fine-grained motor understanding could request tens of thousands of videos showing hand-object interactions in cooking contexts. Shofo handles the querying, filtering, labeling, and delivery, providing a clean, annotated dataset ready for training.

Why Is Customization So Important for Modern AI Training?

One of Shofo’s defining advantages is its emphasis on customization rather than one-size-fits-all datasets. Modern AI models are increasingly specialized. A robotics lab may need videos emphasizing physical manipulation, while a consumer AI company might focus on social interactions, gestures, or visual reasoning in everyday environments. Generic datasets often include large amounts of irrelevant data, which can slow training and degrade performance.

Shofo’s pipeline is designed to be query-driven. Instead of forcing customers to adapt their models to available data, Shofo adapts the data to the model’s requirements. This approach significantly reduces the time between idea and experimentation. AI labs can iterate faster, test hypotheses more efficiently, and focus their resources on model architecture and performance rather than data engineering.

Customization also improves data quality. By filtering and labeling content according to precise criteria, Shofo ensures that datasets are not only large, but also coherent and relevant. This is particularly valuable for fine-tuning, where the signal-to-noise ratio has a direct impact on downstream results.

Who Are the Founders Behind Shofo and What Experience Do They Bring?

Shofo was founded by a team of four entrepreneurs with deep experience in AI, engineering, and operations. The founding team previously worked together on Correkt, an AI-powered multimodal search engine that reached over 40,000 users. That shared history gave them firsthand exposure to the challenges of working with unstructured multimedia data at scale.

Bryan Hong, Founder and CEO, brings a product-driven vision shaped by his experience as a Berkeley dropout and startup builder. His focus is on translating complex technical capabilities into platforms that AI labs can actually use. Andre Braga, Founder and Head of AI, has a strong academic background in statistics and data science from UCSB, with prior experience at MIT. He leads the development of Shofo’s labeling, reasoning, and segmentation systems.

Braiden Dishman, Founder and COO, contributes operational expertise from his background in economics and prior work at AWS. He focuses on scaling Shofo’s infrastructure and ensuring reliability for enterprise customers. Alexzendor Misra, Founder and CTO, previously served as CEO of Correkt and brings deep technical leadership to Shofo’s platform architecture. Together, the team combines research depth with practical experience building production-grade AI systems.

How Did Shofo Emerge From Correkt’s Pivot?

Shofo’s origin story is rooted in the evolution of Correkt. Initially, Correkt aimed to be a multimodal AI search engine, indexing and retrieving content across different media types. As the team worked on that product, they repeatedly encountered the same bottleneck: acquiring and processing high-quality video data was disproportionately difficult compared to building models or interfaces.

Rather than treating data as a secondary concern, the team realized that data infrastructure itself could be the core product. The pivot from Correkt to Shofo represented a strategic shift—from building an end-user application to enabling the entire AI ecosystem with better data. By focusing on pipelines instead of products, Shofo positioned itself as foundational infrastructure rather than a competing application.

This pivot also aligned with broader trends in AI. As models grow larger and more capable, the limiting factor is increasingly data quality rather than compute. Shofo’s founders recognized that whoever controls scalable, high-quality video data pipelines would play a critical role in the next generation of AI development.

Why Does Shofo Start With TikTok and Expand Beyond It?

TikTok was a natural starting point for Shofo’s indexing efforts. The platform hosts an enormous volume of short-form videos that capture real-world behavior, creativity, and interaction. Unlike professionally produced content, TikTok videos are often raw, diverse, and spontaneous—qualities that make them especially valuable for training generalizable AI models.

Short-form videos also tend to be dense with information. In a matter of seconds, a single clip can contain multiple actions, objects, and contextual cues. This density makes TikTok an ideal source for tasks such as activity recognition, gesture understanding, and interaction modeling. By mastering data ingestion and labeling on TikTok, Shofo built a foundation that can be extended to other platforms with different formats and dynamics.

As Shofo expands across additional social platforms, its indexing strategy remains platform-agnostic. The goal is not to replicate individual platform APIs, but to create a unified layer that abstracts away platform-specific complexity. For AI labs, this means a single interface to access diverse video data, regardless of where it originates.

How Does Shofo Help AI Labs Move Faster and Build Better Models?

Shofo’s value proposition ultimately comes down to speed and focus. By outsourcing the hardest parts of video data collection and processing, AI labs can dramatically shorten their development cycles. What once required months of engineering effort can now be accomplished in days or weeks. This acceleration enables faster experimentation, more ambitious research agendas, and quicker deployment of new models.

Beyond speed, Shofo also improves model quality. Clean, well-labeled data leads to more stable training and better generalization. Shofo’s emphasis on segmentation, reasoning, and interaction-level labeling ensures that models are trained on meaningful signals rather than superficial patterns. This is particularly important for advanced applications such as robotics, multimodal assistants, and embodied AI.

In effect, Shofo acts as a force multiplier for AI teams. By handling the invisible but essential work of data infrastructure, it allows researchers and engineers to focus on what truly differentiates their products: model design, performance, and user impact.

What Does Shofo’s Trajectory Suggest About the Future of AI Data?

Shofo’s emergence reflects a broader shift in the AI landscape. As models become more capable, data is no longer just an input—it is a strategic asset. Companies that can reliably source, structure, and enrich complex data types like video will shape the direction of AI innovation.

By positioning itself as a Common Crawl for videos, Shofo is betting that shared data infrastructure will be as important for video as it has been for text. If that bet pays off, Shofo could become a foundational layer for multimodal AI, powering everything from research labs to production systems.

In a world where AI increasingly learns from how humans move, interact, and create, Shofo’s mission—to turn the chaos of social video into structured intelligence—may prove essential.