Documents and notes decision

Slite

A single engineer can build a narrow self-maintaining KB (connectors, indexer, LLM drafts, review UI) in a few months, but reproducing Slite’s enterprise compliance, broad integrations, MCP API surface, and customer trust is costly—so keeping Slite is reasonable for teams that need those guarantees.

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Subscription$10/month ✓ verified
Initial build80 hours
Monthly upkeep12 hours + $300
Evidence3/3 runs agree

Open-source builds that already do this

Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All Slite alternatives, with the arithmetic →

What a replacement has to do

  • Ingest and index team docs + external signals, detect drift via rules/LLM, draft suggested doc updates, present cited suggestions to humans for verification, update canonical storage when approved.

What it still won’t have

  • Enterprise compliance guarantees (SOC 2 / HIPAA attestation, EU hosting)
  • Prebuilt, tightly integrated connectors and MCP API surface
  • Built-in agent workflows, quotas, and seat-based billing
  • SLA, priority support, and dedicated account management

What remains hard

  • Compliance and regulationSOC 2 Type II, HIPAA, GDPR. EU-hosted.
  • Brand trust3,000+ leading companies trust Slite as their single source of truth.
Read the build prompt

First-year cost

Keep paying

Paying ischeaper in year one.

On cash alone, building overtakes the subscription at 31 seats.

Paid seatsseats

Money you would actually spend

Keep paying

Subscription price × seats × 12

Build it

AI build APIs + hosting

Time you would spend

What you would spend

What we assumed

The verdict above measures whether you could build it. This one is only about money.

Runnable build prompt

Not run yet
Build a self-maintaining team knowledge base using Node.js + Next.js frontend, PostgreSQL for canonical storage, a vector DB (e.g., Weaviate or Pinecone) for search, and OpenAI-compatible LLMs for drafting. Implement: connectors to GitHub, Slack, and Linear (webhook receiver and periodic sync), document parser and indexer, LLM pipeline that detects drift and generates suggested edits with source citations, a review UI showing suggested edits and verification state, permission-aware search honoring workspace roles, and an API to read/append docs. Out of scope: enterprise SLA, HIPAA attestation, and multi-region EU hosting. Include error handling, logging, basic unit/integration tests, and deployment scripts for a single-region cloud VM plus managed vector DB.
How we checked5 sources · 3/3 runs agreed · evidence score 64

How the score was reached

  • Partly verdict base52
  • An open-source build was found+5
  • 5 cited sources+3
  • Price verified on pricing page+3
  • 3/3 assessment runs agreed+4
  • Hard moats found in the evidence-3
  • Evidence score64

The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.

How scoring works →

Cited sources · 5

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

✓ Price read off the page✓ 3 independent runs, one answer✓ Citations limited to fetched pages! 2 moats quoted from the page