Why Autoregressive LLMs Fail at Inbox Zero

Running 10,000 emails through a 70B+ LLM takes hours, burns fortunes on sequential output tokens, and hallucinates schemas. TypeSafe Jev replaces sequential token generation with a parallel probabilistic sampler—delivering typed decisions in 100ms for pennies.

INFERENCE SPEED

70ms – 140ms

OUTPUT TOKENS

$0.00 (FREE)

OUTPUT FORMAT

0% Parse Errors

SAFETY GUARD

Calibrated RLCD

Autonomous Inbox ZeroTypeSafe Jev System One

Dataset:

Mailbox Views

Engine:Jev System One

Latency:~80–120ms

Schema Invariant:100% Bound

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No emails in this view.

Try selecting "All Mail" or clicking "Sweep Emails with Jev".

Select an email from the list to view reader pane and telemetry.

Under the Hood: Computational Mechanics (O(1) vs O(N))

JEV (SYSTEM ONE)Single Parallel Pass

Input State (Subject + Body)~300 tokens

Parallel RLCD Evaluator

Computes all question logits simultaneously in 1 forward pass

Typed Probabilities & Confidences

Latency: 70ms – 200ms • Zero Output Tokens • No Parse Errors

FRONTIER LLM (SYSTEM TWO)Autoregressive Loop

System Prompt + State + Schema~900 tokens

Token-by-Token Decoding (O(N) forward passes)

Generates "{" then "category" then ":" then "refund"...

JSON String Output

Latency: 2,500ms – 30,000ms • Expensive Output Tokens • Schema Drift Risk