Developer tooling startup Convex has raised $57 million in Series B funding led by Insight Partners, with participation from Etna Labs, Andreessen Horowitz, and Spark Capital. The round brings the company's total funding to $110.5 million. Founded in 2021 by former Dropbox infrastructure engineers, Convex provides an AI-optimised application backend. The platform offers building blocks that companies can use to develop custom software. The San Francisco-based startup focuses on simplifying backend development by providing developers with tools to build applications more efficiently. The fresh capital will support Convex's continued development of its backend infrastructure platform.

AI infrastructure startup DataBahn raises $40M Series B to build agentic data control plane for enterprise AI. The AI boom has created an expensive problem few companies expected. Every AI assistant, autonomous agent, and copilot depends on massive amounts of enterprise data, yet moving, storing, and processing that data is becoming one of the biggest cost centers inside modern organizations. AI infrastructure startup DataBahn believes the answer isn't building another data pipeline. It's creating a new software layer that decides what data AI actually needs, when it needs it, and where it should go. Investors are betting that vision could become a core piece of enterprise AI infrastructure. Today, Dallas-based DataBahn announced it has raised $40 million in a Series B funding round led by Insight Partners, with participation from existing investors Forgepoint, GTM Capital, and S3 Ventures. The latest investment brings the company's total funding to $59 million and will support product development and engineering as the company expands its agentic data control plane platform. The funding arrives at a time when enterprises are rethinking how information flows across their technology stacks. Traditional data pipelines were built to transport logs and telemetry from one destination to another. AI has changed that equation. Large language models, AI agents, analytics platforms, and security tools all compete for access to the same enterprise data, driving storage bills higher and increasing cloud transfer costs. DataBahn's approach focuses on controlling those data flows instead of simply moving everything into another repository. Its platform ingests telemetry from hundreds of enterprise systems, enriches and governs the information, then routes only the data required for a specific application or AI model. Extra context can be retrieved when needed instead of duplicating entire datasets across multiple platforms. That architecture addresses one of the growing financial challenges facing enterprise AI deployments. Companies are paying more for cloud ingress and egress fees, storing larger volumes of telemetry, and covering AI inference costs that continue to rise as workloads increase. Reducing unnecessary data movement has become a meaningful way to lower operating expenses without limiting AI capabilities. "The next generation of enterprise infrastructure won't be built around moving more data - it will be built around intelligently orchestrating the right data at the right time. We believe every enterprise will need an agentic data control plane that continuously reduces, enriches, governs and activates enterprise data for both applications and AI. Organizations that build this foundation will accelerate AI adoption while dramatically reducing the cost of moving, storing and processing data," said Nanda Santhana, CEO and co-founder of DataBahn. With $40 million in funding, DataBahn aims to solve enterprise AI's growing data bottleneck. DataBahn says the latest evolution of its platform moves beyond transporting enterprise data. The company recently introduced Autonomous In-Stream Data Intelligence (AIDI), a capability that analyzes, validates, and acts on data as it moves through enterprise systems instead of waiting until it reaches a downstream destination. Rather than treating pipelines as passive channels, AIDI interprets telemetry in real time, allowing organizations to apply governance, enrich context, and automate decisions before the data reaches AI models, security platforms, or analytics tools. The startup argues that enterprises need a control layer sitting between data sources and AI systems. Instead of copying information into separate databases for every application, organizations can retain ownership of their data and give AI systems secure access to the information they need when they need it. That model aims to reduce storage requirements, cloud transfer charges, compute expenses, and AI token consumption. DataBahn says customers are already using the platform across industries including healthcare, financial services, manufacturing, and transportation. According to the company, the business has recorded more than 400% year-over-year revenue growth, 180% net revenue retention, zero customer churn, and a 97% proof-of-concept win rate. Those figures have helped the startup build momentum through strategic partners rather than relying primarily on a direct sales organization. Investors see the company's platform as part of a broader shift in enterprise software. Businesses are moving beyond viewing AI as a standalone application and are beginning to treat data orchestration as foundational infrastructure. "The next generation of enterprise infrastructure won't be defined by where data is stored, but by how intelligently it can be orchestrated for AI," said Max Wolff, Managing Director at Insight Partners. "DataBahn is building that layer, and enterprise leaders across industries describe DataBahn as foundational to their future security architecture. We look forward to supporting the team through this next stage of growth." Industry analysts have pointed to similar challenges. In its Future of Data Modeling report published in April 2026, Forrester Research said fragmented data models and inconsistent definitions remain major obstacles for organizations trying to scale AI and analytics. A separate Forrester report released in February argued that AI-enabled data fabrics give organizations a trusted and governed foundation for advanced analytics and enterprise decision-making. Customers describe the platform as a way to simplify increasingly fragmented data environments. MVB Bank Chief Information Security Officer Parrish Gunnels said DataBahn helped consolidate multiple data sources into a single operational framework that supports the bank's fleet of AI agents and regulatory requirements. Canada Pension Plan Investment Board security executive Ricardo Henry said the company reduced the engineering effort required to onboard new telemetry sources and improved visibility into security logging coverage across its environment. The Series B funding will support continued investment in research and product development ahead of new platform capabilities that DataBahn plans to preview during Black Hat USA 2026. As enterprises push deeper into AI, the race is shifting beyond building larger models. The next challenge is making enterprise data available without sending infrastructure costs through the roof. DataBahn is betting that the future belongs to companies that can make enterprise data smarter, cheaper to manage, and ready for AI the moment it is needed.

The company will accelerate investments in R&D and product innovation to expand its agentic data control plane.