RapidCanvas raises $29m Series B to scale managed AI for mid-market
RapidCanvas has closed a $29 million Series B funding round led by Vertex Ventures SEA & India, with participation from Hitachi Ventures and existing backers Accel, Peak XV Partners, Valley Capital Partners and Titanium Ventures. The Austin, Texas-based company says total funding now stands at $52.5 million. Proceeds will fund go-to-market expansion, growth of its field AI engineering team across the US, Brazil and India, and continued product development across its three-component platform.
The company says annual recurring revenue has grown eightfold over the past six quarters, and that more than 40% of its customers now run multiple AI applications in production. RapidCanvas counts more than 75 enterprise customers in manufacturing, financial services, retail, business services and infrastructure, and says those customers have collectively made 1.4 million AI-led decisions using its platform in 2026 to date.
The product model
RapidCanvas positions itself as a hybrid between a professional services firm and a SaaS platform. Its offering pairs a subscription-priced software platform with embedded field AI engineers who remain accountable for outcomes after deployment. The platform comprises three products: an Enterprise Context Engine that captures business-process knowledge and data schemas; a Visual Development Canvas through which AI engineers build custom applications, reportedly drawing on a pre-built skills library the company says gives each new solution a 60% head start; and an Operational Harness for governance and monitoring of deployed models.
Rahul Pangam, co-founder and chief executive, said the company was targeting the gap between early pilots and reliable production. "Vibe coding has made it easy to start building with AI," Pangam said. "Getting AI into production reliably and safely still remains the biggest gap. Our growth over the past six quarters is evidence that AI in enterprises is moving beyond pilots into production and real operations."
Market context
The segment RapidCanvas is addressing sits between two established categories: large-scale AI consultancy engagements from firms such as Accenture and Deloitte, which typically carry multi-million-pound price tags and multi-month timelines, and self-service MLOps platforms such as Dataiku, Databricks or H2O.ai, which presuppose in-house data-science capability. The company says it identified this gap through conversations with more than 400 business leaders, finding that most C-suite executives expect an AI return on investment within six to twelve months, yet few have moved beyond proof-of-concept stage.
Investor interest in the segment reflects a broader market thesis: that enterprises with revenues of $100 million to $10 billion represent the largest untapped addressable market for production AI, precisely because they lack the internal engineering talent that hyperscaler-dependent Fortune 100 firms have hired over the past five years. Radhika Ananth, head of India and investment director at Hitachi Ventures, framed the investment around what the firm calls its "AInomics" thesis, pointing to the shift from services economics toward software economics as deployments scale.
Competitive positioning and outlook
The managed AI execution model carries inherent margin tension: human-in-the-loop delivery is costly to scale, and the long-term commercial case rests on whether the platform's skills library and context engine can progressively reduce the labour component per deployment. RapidCanvas has not disclosed per-customer contract values, gross margin, or churn rates, so the unit economics underpinning the 8x ARR growth figure remain opaque.
The company is also expanding its channel through private equity and advisory partners, aiming to embed managed AI execution into PE portfolio company operating models. That go-to-market motion could accelerate customer acquisition but adds distribution-cost complexity. Investors and prospective customers will be watching for named customer case studies with quantified business outcomes as the next substantive proof point.