Connect all your documents, databases, and tools — Slack, Notion, GitHub, Jira, Linear, Fathom. Ask questions naturally and get precise answers with sources.
Free for early adopters · No credit card required
Enterprise Refund Policy
Full refund within 30 days…
Refund Request Process
Standard procedures for…
Features
Slack messages, Notion pages, GitHub issues, Linear tickets, Jira projects, Asana tasks, Fathom transcripts — one query, one ranked answer list.
Every recall returns the source — channel + timestamp, page URL, issue link. Click straight back to the original.
Semantic vector search + Postgres FTS, fused via Reciprocal Rank Fusion. Catches both 'what did finance commit to' (semantic) and 'the SCRUM-1247 ticket' (keyword) in one query.
Incremental sync with content-hash deduplication. Re-syncing unchanged content costs zero embedding calls. Manual 'Sync now' button when you can't wait.
Each customer gets a Postgres-row-level-secured namespace. No cross-tenant leak. Your data, your namespace, your provenance trail.
Cloud is convenience, not a moat. The underlying memory library is open source — point HostedClient.base_url at your own deployment if Cloud ever goes away.
Integrations
Connect via OAuth in 30 seconds. Pick the channels, pages, repos, or projects you want indexed. Sync runs in the background — content shows up in search automatically.
Slack
Connected
Notion
Connected
GitHub
Connected
Linear
Connected
Jira
Connected
Asana
Connected
Fathom
Connected
Google Drive
Coming soon
How it works
OAuth into Slack, Notion, GitHub, Jira, Linear, Asana, Fathom. Pick exactly which channels, pages, repos, or projects to index.
Incremental, deduped against content hash. The first sync backfills history; later syncs only fetch new items. Manual 'Sync now' anytime.
Hybrid recall (vector + FTS via RRF) returns ranked results. LLM synthesises an answer with [N] citations linking back to the source.
For developers
Most teams glue together pgvector, an embedding model, a chunker, a re-ranker, a sync worker, and per-tenant isolation themselves — then discover they also need hallucination detection. Extremis ships the whole stack as one API call.
mem.remember() + mem.recall()# pip install extremis from extremis import HostedClient mem = HostedClient(api_key="extremis_sk_…") # write mem.remember("Customer wants pricing in writing") # read hits = mem.recall("what did the customer want?") for h in hits: print(h.memory.content) print(h.reason) # similarity 0.87 · used 5× · 3d
Extremis Cloud
We run the embeddings, the sync workers, the vector index, the hallucination detection, and the dashboard. You bring the API key.
Free
for early adopters
$49
per month, per workspace
Custom
contact us
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Benchmarks
Reproducible on the public benchmark. See methodology →
94.4%
Retrieval R@5
top-5 includes the answer session
38.8%
QA Accuracy
claude-haiku-4-5 as answerer
~35ms
p50 recall latency
local model · MPS · varies in prod
Stop hunting through Slack, Notion, GitHub, and Jira. Connect once, search forever.