# Revari Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/revari
> Markdown URL: https://aitinkerers.org/technologies/revari.md
> Technology record last updated: 2026-04-06T01:32:41Z
> Generated: 2026-09-20T13:50:36Z

Revari is a high-performance database engine designed for sub-millisecond vector search and real-time metadata filtering.

Revari delivers a specialized storage layer for AI applications requiring extreme low latency. By integrating SIMD-accelerated indexing with a custom Rust-based architecture, the engine handles millions of high-dimensional vectors (up to 1536 dimensions) while maintaining 99th percentile response times under 5ms. It eliminates the overhead of traditional wrappers by co-locating scalar metadata and vector embeddings in a single unified index (DiskANN-inspired), ensuring consistent performance even as datasets scale into the billions.

- Official technology site: https://revari.bet
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Building Bob's Cabins Across Three Agent Tools Without Losing My Mind](https://nashville.aitinkerers.org/talks/rsvp_iAJZ16pa6m0)

Revari: A Memory Dataplane for Heterogenous Agent Environments Every multi-agent workflow hits the same wall: switch tools and start over. Context vanishes between Claude, Codex, Cursor, or whatever your stack is. You end up re-explaining architecture decisions, copy-pasting summaries, manually bridging what one agent learned to the next. It's a tax on every tool switch, and it compounds. Revari eliminates that tax. It's a sovereign memory dataplane that sits below your agents, not inside any one of them. Persistent, hybrid retrieval (BM25 + vector + graph fused via RRF) running entirely on local hardware. Any agent reads and writes to the same shared memory. Swap agents mid-workflow and nothing is lost. Decisions, context, and intent carry forward automatically. That's the developer problem. Here's where the architecture goes further. Revari is built for DDIL (denied, degraded, intermittent, limited) environments and connectivity. Agents on edge devices persist memories locally via and reconcile when connectivity returns, over any network. The same memory layer that keeps your coding agents in sync also keeps autonomous systems coherent across factory floors, field hospitals, and forward-deployed environments. The open protocol underneath is post-quantum encrypted by default, with a novel gradient memory system that adapts to available bandwidth. Full context over Ethernet, compressed facts over WiFi, tags over LoRa. Starts with your dev workflow. Scales to infrastructure for agents that operate in the real world. Seeking development partners and seed funding.

- Event context: AI Tinkerers Nashville – Software Development in the Age of AI — 2026-03-25 — Nashville
- Public talk page: https://nashville.aitinkerers.org/talks/rsvp_iAJZ16pa6m0

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