The knowledge base that refuses to go stale

Ask anyone what they think of their company wiki and you will get the same tired sigh. Not because writing docs is hard, but because of what happens after. You write the onboarding guide, the deploy runbook, the pricing exceptions - and the moment you hit save, the rot begins. A process changes in Slack. A field name changes in Linear. Six months later a new hire follows a page that has been quietly lying to them since spring. Slite, a company that has spent nearly a decade on this exact frustration, decided the fix was not "write better docs." It was to build a knowledge base that maintains itself.

Slite is an AI-powered knowledge base for teams. On the surface it looks like a clean, structured wiki - somewhere to keep processes, decisions, onboarding and product knowledge in one place. Underneath sits the part that makes people lean in: an AI agent wired into the tools a team already uses, watching for the moment a document drifts out of step with reality, drafting the correction, and handing it to a human to approve. The pitch, in the company's own words, is a "company brain that stays in sync with reality - for humans to trust and AI agents to act on."

The ProblemEvery wiki starts dying the day you finish it

The uncomfortable truth about knowledge management is that the writing was never the bottleneck. Maintenance is. A knowledge base is only trustworthy if it is current, and staying current is unglamorous, endless work that no single person owns. So pages drift. Teams stop trusting the wiki, start pinging each other on Slack instead, and the whole point of documenting anything quietly collapses.

Slite's argument is that this problem just got more expensive. It is one thing for a human to read a stale doc and shrug. It is another when AI agents - the ones companies are now wiring into support, sales and engineering - start reading those same docs and acting on them. A confidently wrong knowledge base becomes a confidently wrong machine.

"The knowledge base stopped being a reading resource and became infrastructure. And infrastructure needs to be kept current."

How It WorksAn agent that watches, drafts, and waits

Here is the loop that makes Slite different from a folder of pretty documents. The Slite Agent connects to the tools where work actually happens - Slack, Linear, GitHub, Jira, HubSpot, Asana and more. When something changes there that contradicts what a doc says, the agent flags the drift, drafts an update, and surfaces it in a maintenance digest. Crucially, nothing goes live on its own. A person reviews every proposed change before it becomes truth.

The other half of the product is the part users touch every day: Ask, Slite's AI assistant. Type a question the way you would ask a colleague - "what's our refund policy for annual plans?" - and Ask reads across the knowledge base and 20+ connected tools, returns a synthesized answer, and shows the sources it pulled from. It is designed to understand the intent behind a question rather than just match keywords, and the citations are the point: an answer you can check is an answer you can trust.

Slite didn't bolt an "Ask AI" button onto a wiki. It pointed the AI at the oldest flaw in its own category - docs that go stale - and made fixing them the product.

Who Uses ItThe teams that got burned by their old wiki

Slite's natural customer is a growing team that has already felt the pain. Startups and scale-ups make up the core - the kind of companies where a dozen people become fifty in a year, where the person who knew how the billing edge cases worked has moved to another team, and where onboarding a new hire means either a good doc or three days of interrupting everyone. The company says thousands of teams have run on Slite over its history, with 3,000+ companies currently using it as their single source of truth.

Inside those companies the usual suspects show up: engineering keeping runbooks and architecture decisions, product tracking specs and launch plans, support maintaining answers that have to stay current, and operations holding the policies everyone forgets. The common thread is that these are teams whose knowledge changes constantly, which is exactly the condition under which a static wiki fails and a self-maintaining one earns its seat cost. Many of them do not arrive fresh - they migrate in from Notion, Confluence, ClickUp, Quip or OneNote, carrying the scars of a knowledge base that had quietly become fiction.

The FieldStorage is crowded. Maintenance is open.

Slite competes in a busy room. Notion, Confluence, Coda, Guru, Slab, GitBook, Nuclino and plain Google Docs all offer a place to write and store team knowledge. Where Slite tries to draw the line is emphasis. Most incumbents optimise for capturing and organising documents. Slite optimises for keeping them true. The teams it wins tend to arrive from somewhere else - the site cites migrations from Notion, Confluence, ClickUp, Quip and OneNote - usually after their old wiki became a graveyard nobody trusted.

The BusinessPriced per seat, sold on trust

Slite is straightforward B2B SaaS, billed per user per month. A Basic plan runs around $10 per user for the knowledge base and limited search; a Pro plan around $20 per user adds the Slite Agent and AI search across the connected-tool stack. Enterprise pricing is custom and layers on SSO, audit logs, SLAs and dedicated support. There is a 14-day trial with no credit card required. The model is honest about its own math - costs scale with every seat you add - which pushes Slite to keep justifying the price with maintenance work that a cheaper doc tool simply does not do.

The BackstoryFrom "satellite" to source of truth

The name is a small tell. Slite is a contraction of "satellite," a nod to the company's early days, and when it refreshed its brand it leaned back into that origin. The company was started in 2016 by Christophe Pasquier inside eFounders, the Paris startup studio, and went through Y Combinator's Winter 2018 batch. It raised a seed round that year and an $11M Series A in 2020, as remote work turned "write it down" from a nice-to-have into a survival skill.

Pasquier has been unusually consistent in public, writing for years about async work, documentation and how teams should communicate. That founder-led point of view is not just marketing texture - it is the same conviction that shows up in the product. Slite is a remote, async team building the tool it needs to run itself, which is the most reliable kind of dogfooding there is. The company has stayed deliberately small - roughly forty people - rather than chasing headcount, a choice that reads less like caution and more like a team that would rather ship a sharp product than manage a big one.

That restraint matters when you remember who Slite is up against. Its largest competitor, Confluence, belongs to Atlassian, a company with thousands of engineers. Notion is a household name with a valuation that dwarfs Slite's total funding. Competing there on breadth is a losing game, so Slite has done the opposite of breadth: it picked one job the giants under-serve and went deep. The wager is that maintenance is not a feature you can casually bolt on to a storage-first product - it is a different way of thinking about what a knowledge base is for.

The best product ideas name a problem you have felt but never quite said out loud. Slite's is this: your documentation stopped being something people read and became something machines act on. So it had better be right.

Where It FitsInfrastructure for the agent era

In 2026 Slite folded its standalone AI search product into the core and relaunched around a single idea: documentation is infrastructure. That reframing tells you where the company thinks the market is heading. As more teams point AI agents at their internal knowledge, the value of a wiki shifts from how nicely it stores information to how reliably it stays correct. Slite is not trying to be the biggest name in the category. It is trying to own the least glamorous, most load-bearing job in it - keeping the truth current, so both people and the agents they deploy can lean on it without checking twice.