Overview
Gumloop is a no-code AI workflow automation platform founded in April 2023 by Max Brodeur-Urbas and Rahul Behal, two McGill University classmates who went through Y Combinator's Winter 2024 batch. The company, originally called AgentHub, is headquartered in San Francisco and has raised over $70 million across seed ($3.1M led by First Round Capital), Series A ($17M from Nexus Venture Partners), and a Series B ($50M led by Benchmark, closed March 2026).
The platform positions itself as "AI-first automation" — distinct from traditional tools like Zapier or Make in that AI model calls are native nodes in the graph rather than add-ons. Users drag and drop blocks onto a canvas to build pipelines that can scrape websites, parse documents, call LLMs, and push results to external services. A standout feature called Gummie lets users describe what they want to automate in plain language; Gummie then generates the corresponding workflow graph automatically.
By mid-2026 the platform reports enterprise adoption at Shopify, Ramp, Gusto, Instacart, and Opendoor. The core weakness is economics: a credit-based billing system where advanced AI calls consume credits quickly, and credits expire monthly. Pricing has also climbed steeply since 2024, making it pricier than Zapier at comparable workflow volumes.
Key Benefits
- No-code AI pipelines: Build document-processing, lead-enrichment, and content workflows entirely through a visual canvas.
- Multi-model flexibility: Swap between GPT-4, Claude, and Gemini on a per-node basis within one workflow.
- Gummie meta-agent: Describe a task in natural language and receive a ready-to-run workflow, dramatically reducing setup time.
- Enterprise-grade scale: Proven at companies like Shopify and Ramp with dedicated workspaces, SSO, and SLAs on the enterprise tier.
Use Cases
- Lead research and enrichment — Scrape LinkedIn or web sources, pass results through an AI summarization node, and push enriched records to a CRM automatically.
- Document processing — Extract structured data from PDFs or contracts using AI parsing nodes, then write results to Google Sheets or Airtable.
- Content generation pipelines — Chain a research step, an LLM drafting step, and a publishing step to produce blog posts or social content at scale.
- Sales and ops reporting — Pull data from multiple SaaS tools, aggregate with AI analysis nodes, and deliver a formatted Slack digest on a schedule.