New York-based AI detection startup Pangram has raised $9 million to build tools that distinguish human-generated text from machine output.
The funding round was led by Menlo Ventures, with participation from Haystack, ScOp, Script Capital, and Cadenza. The capital injection coincides with the launch of Pangram 4, a new text detection model, and Pangram Image, a tool for spotting AI-generated visuals.
New tools and accuracy claims
Pangram states the updated text model achieves over 99% accuracy at identifying AI-assisted writing and mixed human-AI content. It also claims to detect humanizer programs more effectively than previous iterations. The image detector remains in research preview, with a wider release expected in the coming weeks.
Stanford graduates Max Spero and Bradley Emi founded the company two years ago. They launched the venture after ChatGPT opened the floodgates for bot-generated content and what Spero describes as “LLM-powered Russian disinformation campaigns and UAE-influenced campaigns on Twitter.”
“I think it’s just incredibly valuable to know whether what you’re looking at is something that’s AI-generated or not,” Spero told TechCrunch. “Especially text that you’re reading, because it changes how people approach the text. Is this something that I’m going to have to look out for hallucinations and jump in skeptically, or is this something that I trust was well-researched from an actual journalist?”
The detection system relies on a large machine learning model trained on tens of millions of known human documents. The startup then created a “synthetic mirror” for each document, replicating the topic, length, and tone of voice, but written by a frontier LLM.
“Our model is learning the stylistic differences and the choices that AI makes consistently and is able to use that to learn what makes something AI-generated with high confidence,” Spero said. He added that the detector does not rely on copy-paste metadata or hidden watermarks.
For Pangram, detection is not just about whether text was written entirely by a machine. It is also about distinguishing levels of AI assistance. Spero believes using AI for editing is acceptable, provided the writer discloses their use of the tool.
AI usage is becoming commonplace, leading to consequences ranging from embarrassment to sanctions. In some cases, like the Canadian politician who read an AI prompt aloud in a speech to lawmakers, the result was ridicule. In others, such as lawyers making cases using fake citations created by ChatGPT, the consequences could be fines.
This backlash is starting to appear in institutional rules. The open-access archive arXiv introduced a new enforcement policy this year. Submissions containing evidence that authors failed to review LLM output, like hallucinated references or meta comments such as, “Would you like me to make any changes?”, can trigger a one-year submission ban.
Pangram is not the only company betting on detection demand. Competitors like Winston AI, Originality.ai, Copyleaks, and GPTZero are chasing the same market, each building its own detector.
The technology could help fuel resistance against the AI-generated content flooding the internet, the courtroom, and academic papers.
Users can access Pangram via a $20-per-month subscription on the web or by downloading the Chrome extension. The extension automatically labels posts in real time on X, LinkedIn, Substack, Reddit, and Medium. It also provides a feed health score with a percentage breakdown of human versus AI content on your screen.
Pangram also offers its technology via API. Substack recently integrated the tool to show readers which of their favourite authors write their newsletters using AI. Other API customers include Quora, schools and universities, publishers and agents, and recruiters, among others, per Spero.
Does Pangram work?
Spero said roughly one in 10,000 human documents are incorrectly labeled as AI with Pangram’s model. I decided to put it to the test. The text detection model was impressive but not perfect. It easily flagged entirely AI-generated news articles written by both ChatGPT and Claude, and was rarely fooled by my attempts to edit the AI-generated text into sounding more human. At the same time, Pangram did flag sentences that I completely rewrote as being AI-written. Pangram also wasn’t at all fooled by my attempts to prompt ChatGPT and Claude into evading AI detectors when generating content.
I also gave ChatGPT and Claude one of my own articles and asked them to polish it up. Pangram gave it a 13% AI assisted score, which was probably close to accurate. The model was able to detect subtle word-choice changes in some sentences and ignored them in others. It also flagged some sentences as AI-assisted when they were human written. That was notable because when I gave Pangram that same article in its entirety, as I had written it, it got a 100% human score.
Maybe the problem was that news articles can be a bit dry and could easily sound like AI. So I tried a different tactic. I tested Pangram on my own more voicey, personal Substack newsletter content, pasting the first half of the text into Pangram and then asking ChatGPT and Claude to copy my style and write the second half. For the most part, Pangram easily detected human-written text versus AI-written text.
My limited testing of Pangram’s new image detection model turned out to be equally impressive.
Pangram’s AI image detection system promises to spot AI-generated images across AI models, unlike OpenAI’s or Google DeepMind’s watermark-based checks, which mostly detect their own output. It works on pixel-level distributions, learning subtle statistical differences between real photos and AI-generated images. Spero says the model can even detect an AI image that appears inside a real-world photo.
In my testing, the model easily detected AI-generated imagery, whether it was photorealistic or cartoonish. I can also confirm the model could detect an AI image appearing in a real-world photo — the heat map Pangram provides clearly lighting up over the image — though in one instance it incorrectly labeled a photo of an AI-generated image as human content.
Spero says he doesn’t want his technology to fuel a witch hunt against people using AI for writing, but that there needs to be some sort of mechanism to push back against the slop.
“The future that I see is that AI content just continues to proliferate,” Spero said. “We’re getting new GPUs faster than new people are being born. If we do not actively discriminate in favor of human content, then we’re just gonna see more and more AI, and it’s just gonna drown out any human signal that we have.”
What it means
The $9 million raise signals that the market for verification tools is maturing. Writers and editors will likely face new workflows where they must justify their methods or disclose AI assistance to maintain credibility. Platforms like Substack are already using this data to inform readers, which could shift how audiences consume newsletters and academic papers.
