Hevo Data

Hevo Data, based in the San Francisco Bay Area, USA, provides a SaaS platform for no-code data pipelines, enabling seamless data integration and mobility from various sources to various destinations. This platform helps businesses automate complex data flows and consolidate information without requiring extensive engineering resources, targeting enterprises in global SaaS markets. Operating on a SaaS subscription model, the company has secured venture capital funding, including a Series B round led by Sequoia Capital India, now known as Peak XV Partners. Hevo Data was also a participant in Sequoia India's Surge program in September 2019. While specific financial details, such as valuation or employee count, are not publicly disclosed, its technology aims to streamline data operations for its clientele. The organization was founded by Manish Jethani and Sourabh Agarwal.

Hevo Data is a no-code, near‑real‑time data pipeline platform that helps companies extract, transform and load (ETL/ELT) data from hundreds of sources into warehouses and databases so teams can run analytics and build AI products without heavy engineering work[6][1].[6]

High‑Level Overview

• Concise summary: Hevo builds a no‑code data movement platform focused on automating integration and transformations so organizations can make their data analytics‑ and AI‑ready with minimal engineering overhead[6][1].[6]
  - For an investment firm (not applicable): Hevo is a portfolio company (founded 2017) backed by Sequoia India, Chiratae Ventures and Qualgro with reported total funding around $30–42M in disclosed rounds[3][1].[3]
  - For a portfolio company (Hevo as the company): Hevo’s product is a no‑code data pipeline / replication platform that builds and runs pipelines to consolidate data into warehouses and databases[6][6].[6]
  - Who it serves: data teams and non‑technical business users at data‑driven companies across sectors (Hevo cites customers such as DoorDash, Shopify, Postman, Neo4J, Groww and others)[1][2].[1]
  - Problem it solves: removes engineering bottlenecks in data integration by automating extraction, transformation and loading from many SaaS tools, databases and streaming sources into centralized destinations in near real time[6][5].[6]
  - Growth momentum: Hevo reports rapid customer and team growth (customer base and headcount expansion cited in company materials) and “exponential growth” since inception, serving 2,000+ companies in company job and marketing pages[1][2].[1]

Origin Story

• Founders & background: Hevo was founded by engineers who experienced data fragmentation firsthand while building products at Grofers (now Blinkit), and set out to democratize access to analysis‑ready data for non‑technical users; the company lists roots in Bangalore and San Francisco with founding around 2017[5][3].[5]
  - How the idea emerged: the founding team observed that operational applications (sales, marketing, finance, support) hold valuable but siloed data, and built a no‑code solution to integrate those sources for analytics and decision‑making[5][6].[5]
  - Early traction / pivotal moments: early customer adoption among startups and scale‑ups led to rapid growth; Hevo later raised venture funding (Sequoia India and others) to scale product and go‑to‑market efforts[1][3].[1]

Core Differentiators

• Product differentiators: true no‑code setup for creating pipelines, built‑in transformations and models to deliver analysis‑ready data, and focus on near real‑time replication into warehouses and databases[6][6].[6]
  - Developer & user experience: designed to be usable by both technical and non‑technical users with prompt, preemptive product assistance and multi‑tenant AWS architecture that scales automatically for high throughput[6][6].[6]
  - Speed, pricing, ease of use: emphasizes near real‑time movement and automated scaling to process billions of records while reducing engineering overhead; pricing details are proprietary but messaging positions Hevo as cost‑effective by lowering engineering time[6][1].[6]
  - Ecosystem & security: broad connector catalog (SaaS apps, databases, streaming sources) and stated compliance posture (SOC / HIPAA references in secondary coverage), supporting enterprise adoption[4][6].[4]

Role in the Broader Tech Landscape

• Trend they are riding: the shift to cloud data warehouses/lakehouses and the AI‑first emphasis that requires clean, unified, real‑time data for analytics and ML/AI workflows[1][6].[1]
  - Why timing matters: as organizations accelerate analytics and generative AI initiatives, demand increases for tools that remove ETL bottlenecks and make data accessible to non‑engineers[6][1].[6]
  - Market forces in their favor: proliferation of SaaS applications producing fragmented data, wider adoption of cloud warehouses, and scarcity of engineering bandwidth to build bespoke connectors and transforms[6][5].[6]
  - Influence on ecosystem: by lowering the barrier to integrate operational data, Hevo enables smaller teams to run analytics and ship AI features faster, increasing velocity across startups and mid‑market companies[5][1].[5]

Quick Take & Future Outlook

• What’s next: continued expansion of connectors and transformation capabilities, deeper integrations with lakehouse/warehouse vendors, and positioning as foundational infrastructure for AI initiatives in customers[6][4].[6]
  - Trends that will shape them: growth in real‑time analytics, adoption of generative AI requiring high‑quality data, and consolidation in the data integration/ELT market (competition from players like Fivetran, Stitch, and rivals) will drive product differentiation and potential M&A or larger‑scale fundraising activity[3][6].[3]
  - How their influence may evolve: if Hevo sustains connector coverage, enterprise security/compliance and low‑code usability, it can become a preferred choice for companies seeking to operationalize analytics and prepare for AI—tying back to its mission of making data access seamless and AI‑ready for global customers[1][6].[1]

If you’d like, I can: produce a one‑page investor‑style memo with market sizing and competitor comparison, or extract and format Hevo’s connector list and key technical features from their docs into a table for quick evaluation.