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

Since ChatGPT emerged, businesses 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), allowing users to find answers and information quickly. But the trend has now shifted toward AI composite agents: systems capable of performing multi-step reasoning and handling tasks such as managing support tickets, answering emails, and making reservations.

Salesforce sparked the AI ​​agent wave with the announcement of AgentForce a few months ago. Now, these systems are moving deeper into the enterprise stack. Case in point: quick canvasa Texas-based startup, claims its context-aware AI agents can automate 70% of data tasks during custom AI deployment.

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

RapidCanvas AI Agents Address AI Deployment Bottlenecks

When running an AI project, organizations are often bogged down by a shortage of tech talent (due to high demand). Even if they manage to hire skilled engineers or external consultants, those teams have to spend a lot of time on coding and data science tasks, from integrating data assets to preparing them, transforming them, and modeling them, to produce downstream use cases. This extends implementation by several months, impacting return on investment (ROI) and business growth.

To solve this problem, former PayPal executives Rahul Pangam and Uttam Phalnikar, who handled risk strategy and architecture, came together to launch RapidCanvas.

“Our goal with RapidCanvas is to revolutionize the way companies create reliable and customizable AI solutions without the need for teams of technical experts; Our platform empowers business and operations teams by using a hybrid approach that combines AI agents and an expert in the loop,” Pangam told VentureBeat.

At its core, the RapidCanvas platform provides enterprises with content-aware AI agents that can be asked in natural language to handle various data engineering and scientific tasks, from data ingestion, orchestration, and preparation to enabling analytics, applications, pipelines, automation and modeling.

According to Pangam, agents execute these tasks on behalf of users by enriching their prompts with contextual information collected directly (business terminologies fed by users) as well as from connected systems (CRM, data platforms, support ticketing systems). It also takes into account the problem the user is trying to solve, as well as context gathered from previous projects to ensure the task is executed optimally.

This, Pangam claims, allows companies to handle up to 70% of data tasks faster and more cost-effectively than humans. And they can use the prepared data in combination with a visual canvas to implement the application in question.

But here’s the trick. 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 things like system design, hypothesis testing, and problem solving) falls to human experts. Pangam says that a company that has previously employed 10 expert engineers would only need one or two when using RapidCanvas agents to create AI projects.

Taking is DataRobot, Dataiku

RapidCanvas takes on leading players like DataRobot, Dataiku, Palantir, and Alteryx. However, the company says its hybrid human-agent approach is a key differentiator.

“In any of the legacy data science machine learning vendors, the primary way for non-coders to build end-to-end AI solutions is to use no-code templates,” Pangam explained. “For example, if I want to merge two data sets, I have to choose the ‘join’ template from the UI, add data sets, join conditions to indicate which columns should be matched for the index, set the join type, and then define the output columns. On the other hand, with RapidCanvas, the user tells the agent to combine two specific data sets and the agent automatically generates the code to merge them. This is because the agent already has the previous context of the type of tables, index and schema, size, join types, data types, etc.

Additionally, the CEO noted that the company offers a human expert as part of its subscription. This person works as an advisor, helping teams at key decision points with insights as well as support to perform complex operations, verify results, and understand industry best practices. Users can opt for this plan supported by human experts or an exclusive self-service platform offering for a fixed monthly fee per user.

Several companies, including Fortune 200 companies, in manufacturing, retail, infrastructure, and financial services have already begun adopting RapidCanvas for their AI development processes. The company counts PayPal, SFR, Suzlon, AutoFi and MTE Thomson among its first customers.

Looking ahead, 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 human-supported multi-agent setup.