Turn videos and files into docs, training, and AI-ready answers.
Every company keeps a graveyard of screen recordings. The onboarding call nobody rewatched. The Loom the senior engineer made at 11pm before going on leave. The forty-minute walkthrough of the billing system that lives in a shared drive folder called "misc." The knowledge is real, and it is trapped in a format nobody has time to consume. Docsie's pitch is that the recording should not be the destination - it should be raw material.
Docsie, run out of Toronto under the corporate name Likalo, LLC, builds an AI documentation and knowledge-orchestration platform. In plain terms: you feed it a video, a stack of PDFs, an old SOP or a website, and it hands back structured, editable documentation - with step-by-step instructions, captured screenshots and searchable text. From there the same content ships out as a branded help center, an AI chatbot, an embedded widget, or a training module, in more than 100 languages. The company describes the workflow in three verbs: convert, manage, deliver.
What makes the story worth telling is not the feature list. It is the fact that a documentation company - about the least fashionable corner of software - bootstrapped its way to roughly $10 million in revenue and 15,000 users across 68 countries without raising a single venture round.
The founding story has a nice accidental quality to it. Co-founders Philippe and Eugene Trounev - brothers - were not setting out to reinvent technical writing. Philippe had worked as a business consultant at Ericsson; Eugene had been a UI architect at IBM. Docsie grew out of Philippe's need for a better documentation tool while building an internal billing system. The tool for describing the product quietly turned out to be more interesting than the product itself.
That path - internal utility to standalone platform - is common in developer tooling, but it usually comes with a funding announcement attached. Docsie skipped that. Founded in 2016, it grew on customer revenue rather than term sheets, which is unusual for a category where the best-known names have all raised heavily.
Revenue tells the clearest version of the story. According to figures tracked by Latka, Docsie reached about $6.6 million in 2023 and roughly $10 million in 2024. Those are not hypergrowth numbers by venture standards, and that is rather the point - the company compounded steadily over nearly a decade instead of buying growth it could not sustain.
Docsie's users tend to be the people left holding the knowledge when someone else moves on: software consultants, engineers, QA teams, technical writers, and product or business leads. Content and design teams use it to publish clean guides; client-facing teams use it to standardize information so two people give the same answer. The common thread is a team that has more product knowledge than it has time to write down.
The pricing model is built around that reality. Docsie charges per workspace rather than per user. Paid plans start around $199 a month, with a free tier that includes real AI credits - enough to convert a ten-minute video without a credit card. The per-workspace decision matters more than it looks: it means adding the whole team to a knowledge base does not inflate the bill, which is exactly the friction that keeps documentation tools half-adopted.
Underneath the pricing sits a fairly broad product surface for a company its size. Beyond the core knowledge base, there is version control with branch-and-merge, role-based access and approval workflows, translation across 20-plus languages inside the editor, export to DOCX, PPTX and PDF, SCORM training packages, enterprise SSO and SAML, and built-in analytics for what readers actually open. In early 2026 the company also added an AI Presentation Maker that turns a training video into an interactive presentation - another way of taking the same source material and pointing it at a different audience.
In October 2025 Docsie leaned into the feature that now defines it: video-to-documentation. The platform uses multimodal AI to analyze audio and video at once, pull out the procedural steps, grab the relevant screenshots and flag the decision points, then assemble a structured document a human can edit. The company said it built this in response to a reported 500% jump in demand for turning training libraries into searchable docs.
The numbers Docsie cites for the underlying problem are worth repeating carefully, as company estimates. It points to enterprises spending an average of $2.4 million a year recreating redundant training content, and to 78% of SaaS companies reporting critical product knowledge gaps that cost roughly $847,000 each in support overhead and lost productivity. In its own analysis of 50 implementations, organizations that converted video training to searchable docs reported about a 70% reduction in time-to-productivity for new hires and 65% fewer repeat support tickets.
In April 2026, Docsie shipped the move that separates it from most documentation tools: a full on-premise deployment with a bring-your-own-model stack. The whole platform runs on the customer's own infrastructure, using the customer's own language models. No content transits an external network; no outputs get logged by a third party. Feature parity with the cloud version, minus the part where sensitive data leaves the building.
The target is specific - ERP consultancies, manufacturing quality teams, financial-services compliance departments, healthcare organizations. Docsie framed the launch around an Info-Tech Research Group finding that 72% of global IT leaders now name data sovereignty and regulatory compliance as their top AI-related challenge, up from 49% a year earlier, while only 11% of agentic AI use cases have reached production. The bottleneck, in that reading, is trust rather than capability. Shipping the AI where the data already lives is a direct answer to it.
Docsie competes in a crowded knowledge-base and documentation market against Document360, KnowledgeOwl, Confluence, GitBook, Notion, Zendesk Guide and others. Most of those tools start from a blank page and ask you to type. Docsie's difference is the front of the pipeline - it starts from material you already have, automates the conversion, and offers a multi-tenant, on-premise option that the typical wiki does not.
| Dimension | Typical wiki / KB | Docsie |
|---|---|---|
| Starting point | Blank page, manual writing | Video, PDF, SOP, site |
| Pricing basis | Per seat | Per workspace |
| Multi-tenant portals | Limited | Unlimited clients, one base |
| Deployment | Cloud only | Cloud or on-prem BYOM |
| Funding | Often VC-backed | Bootstrapped |
The interesting thing about Docsie is how little it tries to be exciting. It picked a problem people find tedious - documentation - and refused to leave any of it undone. Convert the source material, manage the review, deliver it everywhere, and for the customers who cannot use the cloud, run the whole thing on their own machines. Then it funded that expansion out of revenue rather than a raise, which quietly changes the incentives: the roadmap answers to paying users, not to a next round.
There is a discipline in that restraint worth noting. Plenty of companies in this category chase the demo-friendly feature and let the unglamorous plumbing rot. Docsie went the other way, spending its build cycles on version control, approval flows and translation - the parts a technical writer actually lives in - and treating the flashy AI conversion as the front door rather than the whole house. That ordering is easier to hold when the people paying the bills are customers rather than a board.
Documentation is also having a moment it did not ask for. AI chatbots are only as good as the source of truth they answer from, and a lot of companies are discovering their source of truth is a folder of unwatched videos. Docsie is positioned squarely at that gap - the layer that turns raw knowledge into something an AI can be trusted to quote. Whether that becomes a large market or a durable niche, it is a real one, and Docsie got there without borrowing against it.