Poetic Raises $50M in Series A Funding Led by Kleiner Perkins

The round also saw participation from Founders Fund, First Harmonic, and OpenAI. The company is valued at $500 million.

The company plans to use the funding to grow its forward-deployed team, expand into additional complex industries, and extend its platform to more large enterprises globally.

Industries like healthcare, banking, and insurance still rely on outdated processes and a lot of manual work. While software has improved efficiency, it has not been able to fully automate the complex, fast changing and unstructured problems in these sectors.

Generative AI has long been expected to solve these challenges but its impact has mostly been limited. Many solutions rely on simple LLM integrations that are not designed for mission critical workflows.

Poetic takes a different approach to automation. It is built for complex, repetitive workflows that run thousands of times a day, require very high accuracy, and depend on many informal or undocumented rules. Instead of using fully autonomous AI agents, Poetic uses a purpose-built programming language that lets operators describe workflows in natural language, which is then turned into structured, reliable execution.

This approach enables deterministic, controlled automation with high precision. As a result Poetic achieves up to 99% accuracy on processes that have traditionally been difficult to automate, while also reducing cost's compared to conventional AI-agent systems.

Poetic is already being used by some of the world’s largest financial services companies. It has helped Fortune 500 firms save tens of millions of dollars by automating complex, high-stakes processes such as transaction monitoring and dispute investigations. In 2025, the company reached an eight-figure revenue run rate with just four employees.

At SoFi, Poetic achieved over 99% accuracy in end to end fraud investigations within five weeks. At AIG, it also reached more than 99% accuracy on a complex, multi-hour workflow that previously required significant manual effort.

According to SoFi CEO Anthony Noto, "In just weeks, Poetic was able to execute many of our fraud processes end-to-end, all while improving quality metrics and the member experience. With auto-decisioning, members get access restored right away, instead of days. As we've scaled across the business, we've seen 100% process adherence, even in our most complex compliance investigations."

"Markie is one of the most prescient founders I've encountered on AI, and I've had a front row seat since the beginning," said Leigh Marie Braswell, Partner at Kleiner Perkins who led the Series A. "What Poetic has built is genuinely different - a platform that can execute the complex, high-stakes processes that large enterprises actually run, with accuracy that exceeds what human teams can deliver. The fact that they were able to automate at the largest companies with the highest requirements is a reflection of how deeply the product works.

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"The enterprise AI landscape is littered with pilots that never made it to production," said Wagner. "For us, we've had a 100% pilot-to-production conversion rate. Our technology works, and we hire the best of the best - because we're not here to run pilots. We're here to transform businesses."

"Right now in AI, there's too much attention on quick demos and shiny objects, and not enough on outcomes," said Markie Wagner, CEO and Founder of Poetic. "We built a new kind of software that learns like AI but runs like code, so the hardest work in your business finally gets done reliably, allowing your business to evolve faster and operate at a scale that was previously impossible."

About Poetic

Founded in 2023 by Markie Wagner, Poetic helps large enterprises define and automate their most complex processes. The company combines the intelligence of AI with the precision of software to deliver reliable, accurate, and scalable automation. Poetic uses a purpose built programming language that allows operators to describe complex workflow's in natural language. This is then translated into deterministic, near tokenless execution, enabling consistent, and high precision automation at scale.

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