Resect AI Secures $25 Million Funding to Combat AI Hallucinations with Real-Time Detection Technology

Startup takes on AI hallucinations with $25M and an HQ rooted in a small town south of Seattle

Resect AI, an artificial intelligence startup led by a team of scientists and engineers in Washougal, Wash., launched out of stealth Thursday with $25 million in funding to commercialize an open-source technology designed to catch AI hallucinations before they happen. Unlike traditional AI monitoring tools that evaluate generated text after the fact, Resect AI says its patented technology operates in-stream -- looking deep inside large language models in real time to observe, detect, interpret, and modify model behavior before a hallucination can occur. By intervening directly within the model's internal decision-making process rather than running post-hoc checks, the platform stops fabrications at the source while simultaneously generating an audit trail for enterprise compliance and due diligence. "AI has prematurely been put in a position of trust. Adding labels such as 'use at your own risk' flies in the face of proper governance or compliance," Kevin Owens, co-founder and CEO of Resect AI, said in a news release. "We are building the next large enterprise AI company to bring transparency and accountability to AI for industries such as publishing, finance, healthcare, research, and education where factual accuracy is absolutely critical." Beyond its tech, the startup's leadership is also bullish about its small-town presence. Washougal is a city of roughly 18,000 residents, 175 miles south of Seattle, tucked along the Columbia River across from Portland. Resect AI employs four people at an office on Main Street -- including its co-founders -- out of a 30-person workforce spread across the Seattle area, California, New York, and Texas. "We believe the talent is up to par and we loved the sense of community that we found when we first came up here," Owens told GeekWire. "We have been coming to the greater Washington and Oregon areas on and off over the years and finally decided this needed to be our headquarters." Owens said the decision has already paid off, noting that the startup has quickly tapped into the region's talent pool by recruiting PhDs from both the greater Seattle and Portland markets while connecting with Northwest capital markets leaders. Resect AI is also planning to open an office in the Seattle area in the near future for engineering and to serve as a business hub. Alongside Owens, Resect's other co-founders include Tim Walton, chief artificial intelligence officer; Tyler Gerber, chief operating officer; and Tommy Lofgren, chief product and marketing officer. The company plans to use the funding to accelerate research and development, expand its go-to-market initiatives, and fuel talent acquisition -- bringing its total headcount to 50 by the end of 2026.

Resect launches with $25M to reduce hallucinations in AI models

Resect launches with $25M to reduce hallucinations in AI models Seattle-based artificial intelligence research startup Resect AI announced Thursday it raised $25 million in early funding to build an accountability layer for enterprise AI by capturing and reducing hallucinations at runtime. Hallucinations, or confabulations, happen when an AI model replies with a false or fabricated response and presents it with high confidence. At their core, models operate by predicting patterns based on training, not "remembering." This means that gaps in data or a lack of real information lead them to guess. Additionally, many models are designed to appear helpful during refinement and post-training; instead of generating an "I don't know" reply, they can confidently give wrong answers. "AI has prematurely been put in a position of trust. Adding labels such as 'use at your own risk' flies in the face of proper governance or compliance," said Chief Executive Kevin Owens. "AI must be anchored in truth to be widely adopted across the enterprise." Owens added that Resect aims to bring accountability and transparency to AI models, which until now have been black boxes. Resect AI is building an open-source offering for enterprise products that opens up that black box to look inside large language models to observe, detect, interpret, audit and modify that behavior. "Many argue that understanding the black box internals of LLMs is out of reach, but we fundamentally disagree," said Chief AI Officer Tim Walton. "We've spent an extensive amount of time and resources researching how models think, and what causes them to choose the answers that they do." Resect's journey started with rolling its own AI models capable of producing high factuality. According to the company, it built its own model to control data, developed a training process, behavior and reinforcement training, which it discovered could be applied to existing open models including DeepSeek, Qwen and Llama. After that discovery, the task became less about building a better model and turned toward building a suite of tools that could work within model architecture itself to intercept misbehavior and redirect it before hallucinations happen. That means understanding why AI behaves the way that it does internally. The company said these internal visibility tools will form the basis of an enterprise-level audit tool called the NeuroWave Product Suite, described as a polygraph for neural networks. "We've spent an extensive amount of time and resources researching how models think, and what causes them to choose the answers that they do," Walton explained. "Through this process, we've developed technology that observes exactly when and how models fail, and surgically fixes them." The surgical language is especially fitting. The name of the company, "Resect," is a medical verb that means to cut out or remove part of an organ, tissue, or bone - it is a surgical operation that involves the careful cutting away of damaged tissue while leaving healthy tissue untouched. Currently, the company offers two models on HuggingFace, the model repository: a 0.6-billion-parameter Veritas fact checker and an 8-billion-parameter model. Both are based on the Qwen3 architecture and operate non-thinking, meaning they do not have an underlying chain-of-thought layer. According to the model cards, the performance of the models was evaluated on LLM-AggreFact; the overall performance of the 0.6B model scored an average of 72.3% compared to Qwen3, an improvement of 7.4% The company also links to a GitHub repository where it will offer open-source tools; however, at the time of publication, it is not populated yet. Resect didn't announce the names of its backers, but said the funding came from private equity investors. The company said the funding would go toward research and development, go-to-market initiatives, and drive local talent hiring in the greater Seattle and Portland regions.