DeepAleph / jevreel

# jevreel
**A zero-embedding approach to video intelligence, using Jev.**
jevreel lays a video out on labelled contact sheets, one frame per second and sixty frames to an
image, and has Jev, TypeSafe's fast decision model, judge every sheet in
parallel. The best windows are re-checked on full frames. Your agent (Claude Code, Codex, Cursor,
Claude Desktop, anything that speaks MCP) gets back moments, times and probabilities, then looks
closer itself. No vectors are computed or stored anywhere.
## See it work
Five unedited Claude Code sessions using the jevreel MCP server and skill on Blender's open movie
*Tears of Steel* (12:14, (CC) Blender Foundation,
CC BY 3.0). Each clip is drawn from its
recording: the agent's tool calls, what Jev answered for every sheet and frame, the hits, the cost,
and the agent's own answer, quoted. Click a clip for the full-resolution video.
| | Asked | What happened | Jev |
| - | - | - | - |
| 1 | Is there a moment where someone drinks from a juice box? | 13 sheets screened, 6 windows verified on full frames, 2 kept: **2:28.5** (p 0.95) and 12:06.5 (0.86) | 19 requests, $0.0054 |
| 2 | When does Thom apologise to Celia, and what does he say? | Speech mode over the captions: 6:47 *"I'm sorry."* (0.85), and a blurted *"Sorry!"* at 5:02 the agent set aside | 10 requests, $0.0003 |
| 3 | Somebody reads a newspaper early on. What does the headline say? | First 5 minutes only: verified at **1:20.5** (0.94); the agent read *ROBOTICIST CREATES KILLER ROBOTS* off the frame | 8 requests, $0.0024 |
| 4 | `captain.jpg` is a character. List every scene he's in. | Search by photo: 8 moments, 6 verified at 0.86–0.97, 2 more confirmed by the agent with `zoom` | 19 requests, $0.0064 |
| 5 | Map the film: robots, guns, holograms, settings, and what kind of film. | The agent wrote its own 4-question lens, read the per-minute rows back for free, and asked one whole-film question | 14 requests, $0.0051 |
### 1. Find a three-second moment
### 2. Search the dialogue
### 3. Read what's on screen
### 4. Find a person from a photo
The reference photo is a crop of the frame at 9:18, so the 9:17 hit is its own source; the other
seven are what the search found.
### 5. Map a whole film
The session starts with two failed calls, left in: a shell command the recording didn't allow, then
a wrong folder. The error names the folder relative paths resolve from, and the next call works.
## Why a decision model instead of embeddings
Multimodal embedding models already search video well: EmbeddingGemma 2 (October 2026) puts text,
images, video frames and audio in one vector space and runs on a phone. jevreel takes a different
route to the same goal, with different trade-offs.
- **Your question, not a fixed notion of "similar".** An embedding index decides at index time what
 counts as close. A decision model judges the question you ask now, and returns a probability your
 code can threshold, sort and gate on.
- **Every second, cheaply.** Jev charges for inpu