Cyrus Khajvandi, a Stanford biology graduate and two-time entrepreneur, often found it challenging to stay on top of scientific research while managing his daily workload. Recognizing that he wasn’t the only one — and that AI technology was becoming more accessible — Khajvandi began developing an AI platform to summarize and answer questions about documents, particularly scientific studies.
The platform, Humata AI, launched in February, with former Labelbox founder Dan Rasmuson joining as CTO. And it quickly gained traction — processing tens of millions of pages of files, growing to a user base of millions and securing $3.5 million in funding from investors, including Google’s Gradient Ventures, ARK invest and M13.
“Our mission at Humata is to empower people and organizations to make smarter and faster decisions by being able to ask questions across all their files,” Khajvandi told TechCrunch in an email interview. “Humata is like [OpenAI’s] ChatGPT for all your files.”
Humata is exceptionally simple in its execution. True to the premise, the platform simply lets users ask questions about their files — namely PDF files — and get answers. Users can upload one or more PDFs and ask questions across them; Khajvandi says that customers include not only academics but professionals in law, the oil and gas industry and customer support.
Now, chatbots like the aforementioned ChatGPT and Anthropic’s Claude offer similar file-analyzing features. But Khajvandi makes the case that Humata — in part because of its limited functionality and focus — is more robust.
“People can ask AI any question and get the answer from their own data instantly with highlighted references,” he said. “This is possible because of the recent advancements in AI enabling every worker to get instant answers to their questions.”
Image Credits: Humata AI
Now, AI isn’t necessarily the best at summarizing. Fast Company tested ChatGPT’s ability to sum up articles, and found that the model had a tendency to get content wrong, leave pieces out or outright invent facts not contained in the documents it summarized.
There’s also the obvious privacy question. Companies — and individual users, for that matter — might not feel comfortable uploading their documents to Humata’s platform for processing — particularly if the documents contain sensitive info.
Khajvandi stands by Humata’s summarization skills, claiming that the company trained its models on “diverse datasets” and “rigorously tested” them for bias. He also says that Humata only collects “necessary data,” and has implemented “strong safeguards” to prevent unauthorized access.
“We ensure informed consent, helping users understand what they’re agreeing to,” Khajvandi added. “As our AI systems advance, we’re careful not to infer sensitive information without explicit permission. We adhere to legal and ethical standards across different regions and cultures, making Humata enterprise-ready.”
Humata, which now has thousands of customers on its paid plan (or so Khajvandi claims), plans to put the capital it has raised so far ($3.58 million, inclusive of a pre-seed round) toward enhancing its AI capabilities, improving the user experience and expanding its market reach.
“We chose to raise now because we’ve seen a growing demand for efficient, AI-driven solutions in synthesizing insights from vast volumes of enterprise files,” Khajvandi said. “The funds will help us develop new features, refine our existing products and expand into new markets, ultimately by empowering businesses to make better and faster decisions with their private data using Humata.”
