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Funding Round

100 million dollars

to make AI work in the enterprise

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Invisible Technologies raises $100M to make AI work in the enterprise

Matt Fitzpatrick
CEO. Invisible Technologies
September 16, 2025
Today, I am excited to announce a $100m raise to advance enterprise AI.

The launch of generative AI chatbots was a watershed moment, and Invisible has supported the evolution and dramatic advancements in LLMs over the past 4 years. And yet, the headlines persist that AI is not delivering value in the enterprise.

For the past decade, Invisible has operated quietly, profitably, out of the limelight to make AI work. We’ve partnered with leading labs and major enterprises alike to push the bounds of what the models can do. We started plugging models into enterprises years ago, and learned that they work best alongside humans, not instead of them.

Earlier this year, despite running profitably for half a decade, we decided it was time to accelerate our mission. Due to our unique position training the major foundation models, our team has the institutional knowledge to make them work in any context, for any company.
“Invisible’s computer vision models provided high-quality data that played a key role in validating our 2025 draft analysis. The insights we gained proved incredibly valuable to our decision-making and we are excited to continue to work together.”
Patrick Harrel
VP of Basketball Insights & Analysis, Charlotte Hornets
“They maintain a really high bar for talent, with continuous observability that ensures we can trust the data. And they’re not afraid to challenge us, posing really complex questions that push us to create better data.”
Wojciech Galuba
Director of Data & Evaluations, Cohere
With the funds we have raised, we will keep advancing foundation models while translating that progress for enterprise. The same rigor that built these models is required by every company training on proprietary data.

We’ve worked with major players across industries – Microsoft and American Family Insurance, AWS, the Charlotte Hornets, SAIC, and Swiss Gear – using our horizontal AI software platform. From cleaning up messy structured and unstructured data, to fine tuning an initial model, to standing up custom software workflows and visualizations, to evaluating an advanced model already in production, our end-to-end platform meets client needs at every stage.

We’re building the infrastructure to make AI actually work in the enterprise, and we’re just getting started. If this mission excites you — join us.
- Matt

The era of AI in the enterprise is here

Join 20,000+ enterprise leaders.

Enterprise AI moves fast. We catch what matters and send it to your inbox—no hype, just signal.

We don’t talk.
We build.

Invisible operates with the intensity of a startup and the footprint of a global enterprise. We’re trusted by some of the world’s most complex companies to operate at the core of their business. That kind of responsibility only goes to people who earn it; and if you’re here, we believe you can.

What they’re saying

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Invisible delivered a faster, more accurate AI investment assistant at 90% lower cost that unlocked real-time insights.

Invisible helped Nasdaq streamline a data integration process, reducing customer onboarding time and saving 10,000 developer hours.

Invisible automated processes like invoice reconciliation, W9 processing, claim approval letters, and compliance support, resulting in significant cost and time savings.

Invisible's team stepped in to review partially completed conversations between different models. We analyzed the search results, reference material, and model responses.

Invisible's team worked within the client's platform to review model conversations from the research team and assess the level of accuracy of the model responses.

Invisible improved Cohere’s data quality and scalability, enhancing multilingual, coding, and reasoning capabilities to strengthen its enterprise-ready AI performance.

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We’ve trained over 80% of 
the world’s top AI models.

From people to process to platform, we solve the thing behind the thing.

Call it services-to-software. Or just call it done.