cloudsquid raises $1m pre-seed
The death of the human API
Internally, businesses communicate through applications and integrated APIs. Externally, they rely on a flood of unstructured text and documents. The result is expensive skilled workers spending most of their day on repetitive, manual data entry tasks.
Receive PDF → extract data → validate outputs → manually input to systems fields.
This inefficient workflow, using humans as APIs, spans industries, job roles, and regions. In the U.S. alone, the white-collar labor market—worth $9.1 trillion—is bogged down by low-value, data-heavy tasks. It’s more than just wasted time; it’s lost potential.
Legacy AI solutions have limitations
Existing OCR based document AI solutions are built to handle simple, repetitive, and transactional tasks. They require extensive data labelling and training, and can process text or documents that follow rigid, predictable formats. They don’t understand the context of the document; they categorized. A small deviation—an extra field, a misaligned table—can derail the entire process. They also perform poorly on complex, mixed-content documents that combine text, tables, and images.
These tools are still useful for automating specific back-office workflows, but are too rigid to handle real world variability like humans can.
LLMs change the data extraction and transformation Paradigm
Unlike their OCR predecessors, LLMs don’t just categorize unstructured data—they understand it. They excel in complex environments, capable of handling diverse content like documents that mix text, images, and tables.
Prompting combined with rule-based guardrails surpasses human accuracy, even in scenarios where variability is high. You no longer have to rely on rigid templates or constant retraining—LLMs adapt to the document, not the other way around.
Going past demo and into production
The promise of LLMs to solve unstructured data challenges is clear. We spoke with many product teams rushing to build Generative AI functionalities for data extraction in-house. From vertical software startups to large enterprises, they all struggled with critical last-mile issues around accuracy and reliability, preventing them from moving to production.
Creating an AI pipeline from scratch to handle complex documents at scale, designing interfaces to refine and test prompts, and implementing features like observability to monitor performance is an impractical challenge. Even a basic version would take a dedicated engineering team months to build, so they don’t.
Why we’re building cloudsquid
Our vision is to solve these data challenges at the infrastructure level for technical teams. Developers and data teams should be focused on building valuable end-user experiences from newly accessible data streams—not on rebuilding essential data infrastructure from the ground up. Unstructured data ETL will become foundational for AI feature development, and we’re building it to enable teams to harness Generative AI with the speed, security, accuracy, and scalability needed for production.
We’re thrilled to have HTGF and Backbone Ventures join us in this journey, along with experienced AI product leaders Reetu Kainulainen and Udi Miron. With their support, we’re poised to scale our engineering team and core platform. Expect much more in the coming months as we expand cloudsquid’s capabilities.