The company’s AI agents can automate up to 75% of tasks typically handled by data scientists and engineers. These advanced systems, powered by Large Language Models (LLMs), are capable of processing large volumes of data, spotting patterns, and making decisions, offering a practical alternative to manual expertise.

Through its unique service-as-a-software model, RapidCanvas enables businesses to adopt customised AI solutions without needing in-house technical expertise. 

In combining these AI tools with expert support, the company is making AI-powered transformation more accessible for enterprises.

The Series A funding round was led by Peak XV, with additional participation from Titanium Ventures and existing investors Accel and Valley Capital Partners. This brings RapidCanvas’s total funding to over $23.5M since its inception in 2021. 

Although Gartner predicts over 80% of enterprises will implement AI-powered processes in the coming years, 68% of executives cite a lack of technical talent as a critical barrier. 

Data scientists and engineers are expensive and often tied up in repetitive coding and data transformation tasks – stretching out AI implementation, delaying return on investment (ROI), and stalling business growth.

Unlike traditional software that merely enables humans to do tasks quicker, RapidCanvas’s AI agents can absorb and process information at an unprecedented scale, reading thousands of pages in seconds and performing tasks that would take humans days to complete. 

Uniquely, the platform adopts a hybrid approach that combines the power of AI agents with human expertise. While AI agents can handle up to 70% of coding tasks faster and more cost-effectively than humans, the remaining 30% of expert tasks—such as system design, hypothesis testing, and creative problem-solving—still require human intervention. 

This model allows RapidCanvas to deliver superior results with significantly fewer human resources, typically requiring only 1 or 2 expert engineers whereas traditional firms might employ 10.

This ‘Service-as-Software’ approach is particularly effective in areas like coding, where AI agents can handle much of the routine, repeatable tasks. The market potential for this is staggering. 

With over 30 million software engineers and data scientists globally representing nearly $1 trillion in salaries, RapidCanvas estimates that 70% of these tasks can be performed by AI agents, freeing them to focus on valuable work that drives business growth.

RapidCanvas was founded by veterans of AI-powered business transformation. Co-founders Rahul Pangam and Uttam Phalnikar previously built Simility, an AI-powered risk management platform acquired by PayPal. Their technology was later integrated into PayPal’s global fraud detection operations. 

The duo’s deep expertise in AI implementation and its real-world impact drives their vision and mission to make AI accessible and effective for businesses of all sizes. The duo has also assembled a seasoned leadership team that has been part of multiple successful startup exits – including 5 IPOs.

“At RapidCanvas, we’re revolutionizing how businesses solve complex challenges by seamlessly integrating the power of generative AI with the expertise of domain specialists,” said Rahul Pangam, CEO and co-founder of RapidCanvas. 

“Our context-aware AI agents automate critical tasks like data preparation, transformation, and modelling, allowing business users to create tailored AI solutions using simple natural language prompts. With our expert-in-the-loop approach, we ensure human oversight at key decision points, validating outcomes and delivering real-world impact. 

“Moreover, our Reliable AI framework ensures all outputs are validated, secure and explainable. This customer-centric approach empowers businesses to achieve results in days or weeks, not months—at a fraction of the cost of traditional methods. This funding round will accelerate our mission to make trusted, efficient AI transformation accessible to more enterprises.”

The company’s ‘Service-as-Software’ model marks an entirely new era in automation. For the past 25 years, Software-as-a-Service (SaaS) has dominated the software landscape. Although SaaS tools drive efficiency gains through workflow automation, their ROI is limited to productivity gains for employees, rather than direct business outcomes. 

In contrast, RapidCanvas directly links software costs to business outcomes. Its AI agents autonomously handle complex tasks, reducing the need for technical talent and delivering faster, more scalable results.

This shift from indirect efficiency gains to tangible business results represents a fundamental change in how companies use software.

Harshjit Sethi, MD at Peak XV Partners, who led the Series A round, added “There is a huge gap in data science expertise across organisations. It either makes them rely on external consultants or drop these projects altogether. RapidCanvas’ innovative approach of combining AI agents with subject matter experts helps organisations fill this gap and drive results in a scalable and efficient manner. RapidCanvas has seen a strong pull from the initial base of customers who are consistently adding new use cases, demonstrating the value it is delivering”

Looking ahead, RapidCanvas is well-positioned to lead the charge in AI-powered business transformation.

In eliminating the need for extensive technical expertise, RapidCanvas is able to overcome the skills gap and help enterprise companies unlock tailored AI solutions in less time, letting those companies focus on what truly matters: bringing about new growth opportunities and driving efficient revenue and profitability gains.

This initiative aims to provide organisations with the required tools to build solid AI systems that effectively utilise real-time data.

Despite the disruptive abilities of Large Language Models (LLMs), many enterprises have limitations when it comes to implementation in production settings. Recent findings by Gartner reveal that only 53% of AI initiatives progress from the prototype phase, pointing to a huge gap in delivering absolute business value.

The limited adoption of LLMs in corporate environments is largely attributed to their unpredictable behaviour in dynamic situations, compounded by their insufficient access to current, domain-specific information. Tecton’s new features aim to address these challenges by integrating detailed, real-time contextual data directly into AI applications, thereby enhancing their reliability.

“The AI industry is at a crossroads. We’ve seen the potential of LLMs, but their adoption in enterprise production environments has been stifled by reliability and trust issues,” says Mike Del Balso, CEO and co-founder of Tecton. 

“Our platform expansion represents a paradigm shift in how enterprises can leverage their data to build production AI applications. By focusing on better data rather than bigger models, we’re enabling companies to deploy smarter, more resilient AI applications that are customized to their unique business data and can be trusted in mission-critical scenarios.”

This new approach introduces retrieval-augmented generation (RAG), which blends enterprise-wide data with LLMs, enabling more informed decision-making. For example, an AI system for e-commerce could evaluate customer browsing history, inventory status, and ongoing promotions to deliver highly relevant product recommendations.

Tecton is also rolling out a suite of capabilities that include managed embeddings and scalable integration of real-time data, optimising the way LLMs process and respond to queries. 

The managed embedding service will create rich representations of unstructured data, making it easier for AI systems to capture semantic nuances, such as turning a customer review into a vector that highlights opinions and key themes.

Furthermore, the new Feature Retrieval API will allow developers to provide LLMs with engineered features that reflect real-time user behaviour and operational metrics. This enhancement enables applications to deliver tailored responses based on the most current data, thereby improving user experience and operational efficiency.

To simplify development, Tecton has introduced dynamic prompt management, which incorporates version control and standardisation into the AI application development process. This systematic approach to managing prompts will allow for better guidance of LLMs, ensuring they produce accurate and contextually appropriate outputs.

Added to this, Tecton’s updated feature engineering framework enables the extraction of adequate insights from unstructured data, transforming it into structured formats for enhanced machine learning applications. 

This ability will support e-commerce firms in automating tasks such as categorising products and deriving sentiment from reviews, further refining customer engagement strategies.

Experts have already begun to recognise the impact of Tecton’s advancements. Joshua Hansen from Atlassian noted the prospect of these tools to create smarter, more efficient AI-driven experiences that enhance collaboration and productivity.

With the launch of these innovative features, Tecton goes beyond seeking to bolster AI performance but also aims to change how businesses approach AI development. 

The unified framework enables organisations to seamlessly integrate predictive machine learning and Generative AI functionalities, leveraging their unique data to build advanced applications.

Tecton’s generative AI features are currently available for preview, inviting organisations to explore these innovative tools.