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Gumloop
A multiplayer AI agent builder with integrations and centralized IT control
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4.3MB · 10,805px tall · uses GeistSans, Gellix, GeistMono
Industry
AI Agents, Productivity
Style
Dark Mode, Gradient, Bento, Scroll Animation
Stage
Series B
Built with
Next.js
About the site
Gumloop frames agent building as a collaborative capability for the whole company. A dark, high-energy visual system pairs with clear workflow diagrams, product canvases, and recognizable integrations, with enterprise control shown alongside approachability. A strong reference for broad AI platforms that risk sounding abstract, using motion to clarify rather than decorate.
How this page sells
For teams rolling out agents and the IT staff approving them: 'your experts can build this' alternates with 'you stay in control'.
Primary navigation
- Solutions
- Resources
- Enterprise
- Pricing
- Careers
ClickTap a section to see it on the page
'We raised a $50M Series B led by Benchmark' above 'Build, share, optimize & control agents', with Get Started and Book a demo over a full product view: a GTM agent, AI spend by model, and adoption by teammate.
Why it converts: The four verbs map the page, and the adoption panel (teammates with task counts) shows agents being used across a company, not stuck in one person's experiment.
Shopify, Instacart, Gusto, Opendoor, Samsara, Webflow and Ramp beside 'The AI infrastructure powering the world's most AI native companies', 1B+ tasks automated and 314K+ agents deployed.
Why it converts: 'AI native companies' flatters the buyer's own ambition, and the task count turns adoption into scale an IT team can trust.
'Understanding a task is the only prerequisite to automating it… No learning curve involved', beside a CRM agent returning a Q1 pipeline table with deals, amounts and probabilities.
Why it converts: It moves agent-building from engineering to the people who know the work, which is the adoption bottleneck every AI rollout hits.
A 'company brain' globe linking Gmail, Jira and GitHub, plus Skills ('agents write their own playbooks, self-improve') and a Live activity feed of who did what.
Why it converts: Context is what separates a useful agent from a chatbot, and the live feed doubles as visibility for the admin.
Real Slack threads where teammates @mention an agent ('Where are we losing people in the onboarding flow?'), plus Teams and Gmail views where an agent drafts outreach.
Why it converts: Agents living in Slack and email means no new tool to adopt, the difference between a pilot and company-wide use.
Two cards: one teammate builds, 'the whole org puts it to work', and share at any level with Owner, Editor, Viewer and Use Only permissions.
Why it converts: Granular sharing answers IT's worry about who can change what, while showing how one expert's work scales to hundreds of users.
Three panels: 'Open-source by default' with an 83% cost reduction per task ($0.42 to $0.071), self-improving agents, and built-in evals.
Why it converts: A per-task cost drop is the number finance asks for, and evals answer 'how do we know it's still working?'
A dark band: usage monitoring with an 'At Risk: projected overrun' flag, audit logs, VPC deployment on AWS, Azure or GCP, SSO, AI model restrictions, SOC 2, spend caps and approvals.
Why it converts: Spend caps and model restrictions give IT a veto without killing adoption, which is what lets a security review say yes.
Four customer quotes with numbers: Gusto '$1.5M+ additional ARR in 3 months', Instacart's CEO on adoption by non-technical teams, prep time cut from 30-45 minutes to under 5.
Why it converts: Revenue and time saved, from a CEO and named operators, give the champion proof sized for their CFO.
A dated changelog (background subagents, new Claude and Gemini models), then 'Build your team of agents' with Explore agents.
Why it converts: Dated releases prove the platform keeps pace with new models, so buyers aren't locked into last year's AI.
