From how work is made to a trust score.

Typing, clicking, or drawing — Sorsh turns the way work is produced into a decision you can act on. Scroll to see the pipeline.

AKalice-kclear
99
trust score
SCROLL

One engine. Every task type.

⌨️
Text / Q&A
Tabbed out and pasted from an LLM?
🎧
Transcription
Actually listened, or pasted an auto-transcript?
🖼️
Image labels
Looked at the image, or clicked through blind?
⚖️
RLHF preference
Read both answers, or snap-picked?
🔲
Bounding boxes
Drawn by hand, or auto-placed by a model?
01 / INSTRUMENT

We watch how the work is made.

A tiny SDK drops into any labeling tool and records the signal of work — keystrokes, pastes and focus for text; pointer, dwell, playback and box-draws for media — never the content itself.

Type · Paste · Tab-away · Click · Scroll · Play · Draw

keydown · 142ms
focus lost
paste · 480 chars
edit churn · 0.0
02 / SCORE

Three signals, one trust score.

Behavioral catches cheating. Quality catches low effort. An LLM judge corroborates. Either strong signal can flag on its own, so a pasted answer drops fast.

behavioral · quality · content → composite

Transformer models use attention mechanisms instead of recurrence for several important reasons. Firstly, it is important to note that recurrent networks process sequences sequentially...
Behavioral
Content
Quality
HIGH RISK
100
03 / GATE

Caught before the client sees it.

Sorsh sits as the delivery gate. Contaminated batches are pulled out of the pipeline before they ship, so your reputation with the lab stays intact.

2 contaminated batches blocked this run

gate
batch #A1
clear
batch #A2
contaminated
BLOCKED
batch #A3
clear
batch #A4
contaminated
BLOCKED
0
clean batches delivered
04 / ACT

One queue. Clear actions.

Every contributor lands in a ranked queue with the next move spelled out: retrain, remove, or hold payment. No spreadsheets, no Slack archaeology.

riskiest first, with the reasons attached

10
dmitri-p
remove / withhold payment
high risk
25
frank-s
retrain & spot-check
high risk
58
grace-l
review next batch
review
99
alice-k
ok
clear

See it on your own data.

Try the live playground, or join the waitlist for a trust audit on a sample of your real pipeline.