Clueso Raises $10M

Clueso announced a $10 million Series A on October 13, 2026, from San Francisco, with Dharana Capital leading; the round brings total funding to $12 million and drew more than 80% of its new capital from existing investors. Founders Future and Neotribe Ventures joined as new investors, alongside returning backers Y Combinator and f7 Ventures.

The financing is intended to expand Clueso’s platform for making and updating business videos from recorded software workflows. PPC Land’s account records Clueso’s claim that more than 1,000 companies use the platform and that users have reported productivity gains above 5× and an 80% reduction in video production costs; it identifies no disclosed sample, baseline or independent verification for the performance figures.

Existing investors supplied most of the round

The investor mix puts a minimum dollar amount on repeat backing. More than four-fifths of a $10 million round means existing investors contributed over $8 million, leaving less than $2 million for new investors. That is arithmetic from the disclosed proportion, not a breakdown of individual commitments.

The names alone do not establish who supplied that insider total. The financing details identify Dharana Capital as lead and specify which firms are new, but do not state the lead’s prior position or each investor’s contribution. No valuation or share price was disclosed, so the amount or terms of Dharana’s investment cannot be inferred from its lead role.

The planned spending covers enterprise sales, the agent infrastructure, editing capabilities and closer connections to systems containing product and company knowledge. That places the new capital behind a specific challenge: producing explanatory videos for businesses whose software and processes change after a recording is made. The value of a video library depends partly on how quickly those recordings can be brought back into line with the product.

Agents record the product, then revisit changed steps

On Clueso’s Series A page, Seismic customer education director Rachel Moss Ellsworth describes updating product tutorials as Seismic’s software evolves and calls the previous constraint “a longstanding production bottleneck.” Clueso’s computer agents navigate real software, perform a workflow and capture the footage needed to show it. An editing agent can coordinate scripting, voiceover, motion graphics and assembly, while a browser-based editor allows people to refine the result.

When a product or process changes, the capture agents can navigate the workflow again and refresh the footage. A tutorial may still look polished after a menu moves or a sequence of steps changes, yet direct a viewer through an obsolete process. Repeating the underlying action gives Clueso a way to replace outdated footage without rebuilding every part of the video.

Clueprints provide reusable rules for voice, visual style, localization, captions and music. Integrations bring in context from other company systems, and Clueso MCP allows assistants such as Claude and ChatGPT to request video creation, edits and management. Together, those components are meant to keep a revised recording consistent with the company’s other videos while leaving people control over the final edit.

Named customers give the adoption claim some shape

The customer roster includes Atlassian, Zendesk, Gainsight, GlobalPartners, Seismic and Ironclad. Those names make the reported adoption more concrete, particularly for product education and training, but a list of examples does not explain the broader company count. The published total does not distinguish a paid deployment from lighter forms of use or show how many teams and videos each organization represents.

Seismic’s account addresses a narrower question than the platform-wide figures: its customer education team uses Clueso to make tutorials and revise them as its product changes. That describes a real maintenance task and a reported benefit for one team. It supplies no before-and-after production time, number of videos or cost calculation that could be extended to all customers.

The distinction matters because a company with one occasional tutorial and a customer maintaining a large training library can both count as users while placing very different demands on the software. The published adoption figure establishes claimed reach, but it cannot by itself establish how extensively the agents are used or how much labor they replace.

The productivity and cost figures need different baselines

A productivity multiple needs a defined unit of output. It could measure videos completed per employee, time spent on each video or a whole team’s output over a period; those measures are not interchangeable. The reported gain above 5× does not identify which comparison produced it, how many teams were measured or what their production process looked like beforehand.

An 80% reduction in production costs raises a separate accounting question. Agency fees, staff hours, voiceover work, revisions and the platform subscription could each change the result if included or excluded. Updating a tutorial also creates work beyond initial production: someone may need to confirm that the captured steps, narration and current product behavior still agree. A capture success rate and the time spent reviewing refreshed videos are absent from the disclosed performance figures.

A defined customer sample, a before-and-after period and a consistent cost calculation would show whether the reported gains recur beyond individual testimonials. For businesses maintaining frequently changing product tutorials, those measurements would determine whether faster capture also produces a lasting reduction in the work required to keep videos accurate.