Archively gives you an LLM grounded in your own company documents — Slack, Notion, Drive, Gmail, Jira, Confluence — so every answer comes from your data, with the original source linked. Built for B2B SaaS teams who tried using a public chat tool for internal questions and learned the hard way that confident-sounding wrong answers are worse than no answer at all.
Ask a public chat tool about your refund policy and it will give you a confident, well-written, completely made-up answer. The model has no idea what your team actually decided. It will tell you anyway — and the team will believe it.
Archively connects to your stack — Slack, Notion, Drive, Gmail, Jira, Confluence, HubSpot, Intercom — and grounds every LLM response in your real documents. Ask a question in plain English and get an answer pulled directly from the right thread, page, or ticket, with the source linked under every claim. Re-indexing is continuous, so the model always reflects the current state of your tools, not last quarter's snapshot. Permissions from the source tools are honoured. Private content is never used to train external models.
Built for B2B SaaS teams who want LLM speed on internal questions but can't afford LLM confidence about facts the model doesn't actually know. These are the moments Archively pays for itself.
Real numbers, real customer commitments, real shipped features — pulled from your actual tools, not guessed by a model that's never seen them. Every answer comes with a source link so you can verify it in two seconds, not twenty minutes.
"How does our deploy process work?" "What's our PTO policy?" "Why did we pick this database?" Answers come from the actual RFCs, runbooks, and policy docs your team wrote — not a fabrication that sounds plausible but isn't true.
Stop pinging senior staff for the same five questions. Point the team at Archively and they get the same answer that would otherwise come from interrupting somebody — sourced, current, and verifiable. Senior staff get their afternoons back.
— Common questions
An LLM trained on internal documents is a large language model connected to a company's own files — Notion pages, Slack threads, Google Docs, emails — rather than public internet data. Archively builds this using retrieval-augmented generation (RAG): relevant document chunks are retrieved and passed to the model as context, producing answers grounded in your data. Most teams are operational within one day of connecting their tools.
ChatGPT knows nothing about your company's actual documents, decisions, or history. An internal LLM is connected to your private data — Notion, Slack, Drive, Gmail, Confluence — and returns answers specific to your organisation, with source citations. You can ask "why did we descope that feature in Q3?" and get a real answer drawn from your own records.
No. Archively uses retrieval-augmented generation (RAG), not fine-tuning. Your documents are indexed in a private vector database. When a query arrives, relevant chunks are retrieved and passed to the language model as temporary context. Your data never updates the model's weights and never enters a shared training corpus. You can delete your index at any time.
Archively indexes Notion, Slack, Google Drive (Docs, Sheets, Slides), Confluence, Gmail, Jira, Microsoft Teams, GitHub, HubSpot, and Intercom. File types include PDF, DOCX, and plain text. Structured records like Jira tickets and HubSpot contacts are indexed as searchable text. New content syncs automatically — no manual re-uploading when documents change.
Archively enforces the permission model of each connected source at query time. If a user cannot access a Confluence space in Confluence, results from that space will not appear in their Archively queries. Admins can add role-based access within Archively itself — restricting which teams can query which data sources.
Most teams connect their first source in under 10 minutes. Initial indexing for a typical 30-person company — two years of Slack history plus a 500-page Notion workspace — completes in two to four hours. Subsequent syncs are incremental and run in the background automatically.
Every answer surfaces the source documents used to generate it so users can verify. If a source document contains incorrect information, the answer will reflect that — the system does not invent facts. Every query is logged so administrators can identify retrieval gaps over time and refine source quality.
Archively is in pre-launch. Join the waitlist to be first in line when access opens. One confirmation email. No newsletters. No drip sequences. Just a note when we're ready.
