Cisco is acquiring Seattle-based enterprise AI platform provider NeuralFabric as it looks to expand small language model (SLM) availability in support of enterprise AI adoption.

DJ Sampath, SVP for AI software and platform at Cisco, explained in a blog post that NeuralFabric has developed a generative AI platform to support enterprises in the development of domain-specific SLMs using an organization’s proprietary data. This platform can be deployed as a cloud-based software-as-a-service (SaaS) or in an on-premises environment.

“We are quickly moving away from the era of one-size-fits-all AI in favor of purpose-built models,” Sampath wrote. “Enterprises don’t need another generic chatbot trained on the entire internet. They need specialized intelligence built on their data, addressing their specific use cases, operating within their compliance frameworks.”

Sampath linked these efforts to Cisco’s own AI Canvas, which is a generative user interface (UI) collaboration platform targeted at helping IT teams use agents across data domains. It uses SLMs and generative UI capabilities tied into Cisco’s AI Assistant and domain-specific Deep Network Model to provide context-aware insight.

Sampath noted that the NeuralFabric team would join Cisco’s AI software and platform division. Financial details on the purchase were not released, but the deal is expected to close during its second fiscal quarter, which concludes at the end of January 2026.

Cisco’s SLM interest

As noted by the AI Canvas launch, Cisco views SLMs as a new entry point into the enterprise AI space.

Lawrence Huang, SVP and GM of network platform and wireless at Cisco, previously explained to SDxCentral that agentic AI is evolving toward a model that can allow enterprises to structure more relevant workflows for building “specific outcomes.”

“And what that means for the campus and branch, and the enterprise infrastructure ultimately, is it's less about pre-defined types of data and users that we historically have seen, but it becomes much more, call it fragmented if you will, or increase in the number and types of people and services and agents that actually need to access the network infrastructure,” Huang said.

This parsing out of real need is important for enterprises that requires a thoughtful look into what exact business challenge or outcome needs to be overcome or is desired before plunging blindly into the agentic AI hype pool.

“There's definitely use cases that are emerging that customers are interested in,” Huang said, pointing to areas around compliance and surfacing insights. “One way to do this today is you send everything to some cloud service. But, one way that you can do it with these small language models is to process things more locally to reduce the number of bits and bytes that you have to transmit to the cloud. I think there's definitely a play here for customers to gain more efficiencies, but also to get faster insights into the things that they care about within their infrastructure.”

That efficiency is increasingly important as enterprises grapple with their agentic AI plans. A recent Gartner report predicted more than 40% of agentic AI projects will be canceled by the end of 2027 due to cost uncertainty.

“Most agentic AI projects right now are early-stage experiments or proof of concepts that are mostly driven by hype and are often misapplied,” Anushree Verma, senior director analyst at Gartner, explained. “This can blind organizations to the real cost and complexity of deploying AI agents at scale, stalling projects from moving into production. They need to cut through the hype to make careful, strategic decisions about where and how they apply this emerging technology.”

Huang said he has seen this challenge in the real world.

“When I talk to some enterprises who are trying to do this, they are still so early in the process and not really sure,” Huang said. “They have an idea of what the use cases, but they get kind of confused on how to make the next steps. Most of them already have a vendor that they work with who is providing them with some sort of insight or a platform, so they're dabbling with it, but they're not really sure what that ROI is going to be on this.”

This confusion is being further pressured by overarching cybersecurity concerns, “where people are getting nervous about throwing their information out in these large pools of data.”

SLMs could alleviate some of these concerns as they can be structured into easier-to-manage databases running in a localized or edge environment. This model is being propelled by more capable edge devices and what Huang said would be a “rise of more edge processing devices in the infrastructure.”

“That's what I believe as you think about the different use cases … and this is only going to explode the number of devices that IT teams need to think about,” Huang added.