How RapidCanvas automates 70% of data tasks for public AI projects

Since the advent of ChatGPT, organizations have been focusing on AI and how it can help them address critical business challenges. It all started with chatbots and search tools powered by large language models (LLM), which allow users to find answers and insights quickly. But the trend has now shifted to compounding Artificial intelligence agents – Systems capable of performing multi-step reasoning and handling tasks such as managing support tickets, responding to emails, and making reservations.

Salesforce has sparked a wave of AI agents using… Advertisement for AgentForce A few months ago. Now, these systems are moving deeper into the enterprise stack. Example: RapidCanvasa Texas-based startup, claims that context-aware AI agents can automate 70% of data tasks during a custom AI deployment.

The company has raised $16 million in Series A capital to accelerate the expansion of its agent-based platform. In fact, organizations like PayPal, Suzlon and MTE Thomson already use it across their workflows, speeding up time to evaluation tenfold and reducing implementation costs by up to 80%.

RapidCanvas AI agents address the bottlenecks of AI deployment

When implementing an AI project, organizations often get bogged down by the technology Talent shortage (Due to high demand). Even if they can hire skilled engineers or external consultants, these teams have to spend a lot of time coding and Data science Tasks – From integrating data assets, to preparing, transforming and modeling them, to produce the final use cases. This extends the implementation period by several months, affecting ROI and business growth.

To fix this problem, former PayPal executives Rahul Pangam and Uttam Valnekar – who were handling risk strategy and engineering – teamed up to launch RapidCanvas.

“Our goal with RapidCanvas is to revolutionize how companies build reliable, customizable AI solutions without the need for teams of technical experts; our platform empowers business and operations teams with a hybrid approach that brings together AI agents and an expert in the loop,” Bingham told VentureBeat. “.

Essentially, the RapidCanvas platform provides organizations with content-aware AI agents that can be prompted in natural language to handle a variety of data engineering and science tasks, from data ingestion, formatting, and preparation to enabling analytics, applications, pipelines, automation, and modeling.

According to Bingham, agents perform these tasks on behalf of users by enriching their claims with contextual information collected directly (business terms fed by users) as well as from connected systems (customer relationship management, data platforms, support ticket systems). It also takes into account the problem the user is trying to solve, as well as the context gathered from previous projects to ensure the task is running optimally.

Bingham says this enables organizations to handle up to 70% of data tasks faster and more cost-effectively than humans. They can use the pre-prepared data with the visual panel to deploy the respective application.

But here lies the problem. While the offering reduces reliance on technical talent, such as data engineers, it does not eliminate the need for them. The remaining 30% of the work in the workflow – covering aspects such as system design, hypothesis testing and problem solving – goes to human experts. Pangam says that a company that may have previously hired 10 expert engineers will only need one or two when using RapidCanvas agents to build AI projects.

Counter DataRobot, Dataiku

RapidCanvas competes with the likes of leading players such as DataRobotAnd Datiko, Palantir, and Alteryx. However, the company says its hybrid human-factor approach is a key differentiator.

“At any of the legacy data science machine learning vendors, the primary way for non-coders to build the final AI solutions is to use no-code templates,” Bingham explained. “For example, if I want to join two datasets, I have to choose the Join template from the UI, add the datasets, join conditions to direct which columns to match for the index, set the join type, and then define the output columns. On the one hand Other, with RapidCanvas, the user instructs the agent to merge two specific sets of data and it automatically generates the code to merge them because the agent already has the prior context of table type, index, schema, size, join types, data types, etc.

Furthermore, the CEO noted that the company offers a human expert as part of its subscription. This individual acts as a consultant, assisting teams at key decision points with insights, as well as supporting the performance of complex processes, verifying results, and understanding industry best practices. Users can choose this plan powered by human experts or offer only the self-service platform for a fixed monthly fee per user.

Many organizations, including Fortune 200 companies, across manufacturing, retail, infrastructure and financial services, have already begun adopting RapidCanvas for their AI development pipelines. The company counts PayPal, SFR, Suzlon, AutoFi and MTE Thomson among its early clients.

Looking to the future, the company plans to grow its customer base and further enhance its AI agents to ensure they can work together to automate and simplify complex workflows in a multi-agent, human-in-the-loop setup.