RapidCanvas Secures $29 Million in Series B Funding
Enterprise AI startup RapidCanvas has raised $29 million (approximately ₹280 crore) in a Series B funding round led by Vertex Ventures Southeast Asia and India. The company plans to use the fresh capital to expand its go-to-market (GTM) teams, strengthen its AI engineering capabilities and accelerate enterprise adoption of its platform.
The funding round also saw participation from new investor Hitachi Ventures and existing investors Accel, Peak XV Partners, Valley Capital Partners and Titanium Ventures. With this investment, RapidCanvas has raised a total of $52.5 million.
Founded in 2021 by Rahul Pangam and Uttam Phalnikar, RapidCanvas helps enterprises build, deploy and manage AI applications by combining specialised AI agents with human expertise.
How RapidCanvas Plans to Use the Funding
The company will focus on three key areas as it expands its operations.
1. Expanding Go-to-Market Teams
RapidCanvas plans to strengthen its sales and commercial teams to reach more enterprise customers. It will also expand its network of private equity and advisory partners to help introduce its AI solutions to a wider range of businesses.
2. Growing AI Engineering Operations
The startup intends to expand its field AI engineering teams across India, the United States and Brazil. These teams work closely with customers to develop AI applications, integrate them into existing systems and support their deployment in real business environments.
3. Strengthening Its AI Platform
RapidCanvas will continue investing in its three core products:
Enterprise Context Engine: Helps capture and organise business context for AI applications.
Visual Development Canvas: Supports the development of custom AI applications through a visual interface and reusable capabilities.
Operational Harness: Provides governance and operational capabilities for managing AI applications after deployment.
These investments are aimed at helping enterprises move from AI experimentation to reliable, production-ready implementations.
Enterprise AI Moves Beyond Pilot Projects
Many organisations have invested in AI initiatives but continue to face challenges when moving projects from proof of concept to production.
Common obstacles include fragmented business data, a shortage of specialised AI talent, integration complexity, security concerns and the difficulty of measuring returns on investment.
RapidCanvas is targeting enterprises that have AI budgets and plans but lack the internal expertise to implement and scale solutions independently.
Its approach combines an agentic AI platform with engineering support, allowing businesses to develop applications for specific operational requirements.
According to the company, its annual recurring revenue grew eightfold over the past six quarters. It also reported that more than 75 enterprises across manufacturing, financial services, retail, business services and infrastructure used applications built on its platform to make 1.4 million AI-led decisions in 2026.
These figures were reported by the company and have not been independently verified.
Why Investors Are Backing Enterprise AI
The funding comes amid increasing investor interest in AI companies focused on practical business applications rather than general-purpose models alone.
Enterprise AI startups are developing solutions for workflow automation, decision support, predictive analytics, customer operations and industry-specific processes.
Investors are increasingly evaluating whether these companies can demonstrate measurable business outcomes, secure enterprise customers and build recurring revenue.
RapidCanvas's latest funding will support its efforts to scale its commercial operations and engineering capabilities as demand for enterprise AI implementation grows.
What This Means for the Enterprise AI Market
The investment highlights an important challenge in business AI adoption: building a model or prototype is only the beginning. Organisations must also integrate AI into existing workflows, establish governance controls and ensure that systems perform reliably in production.
Companies that can bridge the gap between AI experimentation and operational deployment may find opportunities across industries.
For RapidCanvas, the next phase will involve expanding its customer base, strengthening its technology and demonstrating that its platform can deliver repeatable value across enterprise use cases.
The key question is no longer simply whether businesses can build AI applications, but whether they can deploy them safely, reliably and at scale.
