How Sema4.ai is empowering business users to deploy AI agents in minutes

2025 will undoubtedly be the year AI agents get real. Many early entrants to the market, though, either tend to be singularly-purposed and less flexible, or more horizontal yet IT and developer-driven (and thus not always business user friendly). 

Startup Sema4.ai says it has the differentiating factor that future-thinking enterprises need: The company has put a “tremendous amount of intelligence” into its platform to make it suitable for a wide variety of business use cases. 

“We think it’s much better to have a horizontal platform that enterprises can build their agents for, versus coming in with a single purpose,” Rob Bearden, Sema4.ai co-founder and CEO, told VentureBeat. 

Today Sema4.ai is announcing the general availability of its full-stack enterprise AI agent platform. In less than 9 months, the startup has come out of stealth, piloted its platform with six of the Fortune 2000, secured $30.5 million in funding and acquired open-source automation company Robocorp. And, it has already been featured in two Gartner hype cycles. 

“Agents are going to drive the biggest transformation in business models and efficiencies that the enterprise has seen since the launch of the internet,” said Bearden. 

AI agents outside DevOps and IT teams

Sema4.ai’s no-code agent platform was designed to “speak industry language” and integrate with existing business processes and applications. It has seven key components: 

1. Studio: Users can quickly build, test and deploy AI agents.
2. Runbooks: Users can build and maintain agents with natural language runbooks and pre-built actions.
3. Control Room: Features complete lifecycle management as well as security and scalability. 
4. Actions: An automation framework that allows agents to integrate with apps including SharePoint, SAP and APIs using automation-as-code and Python.
5. Work Room: Users can find, work with and supervise enterprise agents.
6. Document Intelligence: Provides accurate document interpretation.
7. Dynamic Data Access: Gives agents zero-copy access to past, present and future data.

It is critical to shift the current operating model from “programmatically driven by DevOps and IT” to the business user, Bearden emphasized. This is because business users deeply understand specific processes and procedures and best practice outcomes, as well as potential problems and remediation methods. 

In Sema4.ai, business users can define parameters and expected outcomes in runbooks that calibrate AI; agents, possessing an understanding of the data they need and best reasoning paths, then construct automations and software development kits (SDKs). 

“It’s all guided by the business user in natural language,” said Bearden, the former CEO of data platform company Cloudera. “Agents will disintermediate the legacy ERP applications and even the SaaS applications. They will put the power into the hands of the business user versus the DevOps and IT teams.”

Sema4.ai’s platform is architected to be interoperable with whatever large language model (LLM) is most cost-effective for the enterprise use case — currently including Claude, OpenAI, Azure and Bedrock, but that will be expanded, Bearden explained. 

“Bring your own LLM, we’ll make sure that we interoperate with it at the highest standard,” he said. 

Use case: Koch invoice reconciliation

Customers have used Sema4.ai’s platform for a range of use cases — from simple scenarios requiring just one agent for a specific use case, to “15, 18, 20-plus” working collaboratively to manage entire business processes, Bearden explained. Agents (at least for now) are best in areas where work is procedural, high volume, human-intensive, understood, measurable and has definitive outcomes. 

“It tends to be high ROI kind of work,” said Bearden. “It’s measurable. It’s auditable.”

Six Fortune 2000 companies are piloting the platform in early proof-of-concept (PoC). Bearden explained that these partners are using agents to automate invoice processing, payment reconciliation, employee onboarding and regulatory compliance. In two of the PoCs, Sema4.ai’s platform is autonomously performing more than 80% of knowledge work tasks. 

One early adopter is industrial giant Koch, which is using agents to automate one of its invoice reconciliation processes, Koch Labs director Tanner Gonzalez told VentureBeat. Previously, he explained, this involved manually reviewing invoices that can be 80 pages or longer. Sema4.ai allows them to use natural language processing (NLP) to create automated workflows that extract relevant data and validate invoices. 

The key benefit of the platform is that it provides an easy-to-maintain, document-like interface for building and updating gen AI workflows. “Compared to previous robotic process automation tools we’ve used, Sema4.ai is much more user-friendly and doesn’t require specialized technical skills to manage over time,” said Gonzalez. 

Using natural language, employees — finance analysts, accountants, operations engineers or other non-technical individuals — interact with the platform similar to how they would describe their workflow in a Word document, “explaining their logic and the tasks they complete again and again,” Gonzalez explained. In more complex use cases, the platform provides capabilities for data scientists to deploy custom AI models, and for data engineers to connect new data sources for read and write functions. 

Looking ahead, Koch sees potential to expand the use of the platform to other areas such as market research analysis or external communications for commercial teams, said Gonzalez. “The flexibility and low-code nature of the platform makes it well-suited to tackle a variety of automation and conversational AI use cases across our organization,” he said. 

A horizontal approach to address a variety of business needs

When looking to adopt AI agents, Koch analyzed many alternatives in the market, Gonzalez noted. They found others to be too narrowly focused on specific industries, building their own foundation models or limited on integrations. 

The key highlights for Sema4.ai, he said, are 1.) flexibility, “meaning we’re not tied to a specific model as new ones emerge”; 2.) ease of use for business users that can write out their steps as opposed to coding or learning a new tool; and 3.) the ability to implement closed-loop automation, driving real agent automation and monitoring progress periodically for new anomalies.

Navin Chaddha, managing partner at Mayfield Fund, one of Sema4.ai’s top backers, said the startup is on a “mission to build the agentic enterprise” and “pioneering the future of knowledge work” with AI agents that can accurately, efficiently and autonomously perform complex tasks. 

“Their platform delivers real value to enterprises and will be critical to powering the era of human-AI collaborative intelligence,” he said.