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Where benchmark results live once they exist.

Leaderboards and accuracy comparisons are what technical evaluators look for, and showing real fundamental innovation is the point of having the section.

Status No results published
Architecture, in the meantime

What the engine actually is, end to end. Every stage below streams; nothing waits for the stage before it to finish.

On this page Architecture No results published What is published Four innovations The standard Live demo
Alebex Voice Engine

Architecture, in the meantime

What the engine actually is, end to end. Every stage below streams; nothing waits for the stage before it to finish.

Telephony
8 kHz mulaw over media streams
Browser voice
16 kHz PCM

One pipeline serves both, with zero code forks.

01
Audio in
02
Noise reduction
Per frame
03
Voice activity detection
04
Streaming speech-to-text
05
Turn control
Turn end + barge-in
06
LLM
Token stream, gated
07
Streaming text-to-speech
Per sentence
08
Audio out
Interruptible

Alebex Voice Engine — the stream, in order.

No results published

No benchmark results are published here yet.

Nothing is claimed here that is not measured. Until figures exist that Alebex will stand behind in public, this section carries architecture rather than scores.

Available today
What is published

The engine’s five published response timings.

These are the engine’s published response timings, written the same way on every page that carries them.

MeasurementValueNote
Natural human turn-taking200 ms
Typical single-LLM voice AI1,500 msWaits for the full generation before speaking
Alebex, first word500 ms
Alebex, full answer1,500 ms
Anti-hallucination verdictunder 50 msRuns alongside synthesis, not in front of it

A benchmark travels with its method, its samples and whoever ran it. These carry none of that, because they are not one — they are what the runtime is built to do, and they are described on the pages that describe it.

Four documented innovations

Each of these is a piece of the engine that had to be built rather than assembled, and each is substantial enough to carry its own write-up.

01 Context-aware linear streaming speech-to-text Full-context transcription at linear cost: re-decoding’s coherence at chunking’s price, any turn length.
02 The parallel hallucination gate Latent-space check plus NLU confirmation riding the synthesis window, verdict in under 50 ms.
03 Barge-in classification A learned classifier on raw audio (WavLM-based) that separates real interruptions from backchannels and line noise in milliseconds.
04 Multimodal intent understanding An LLM decoder that consumes audio and text together and writes the intent label: open vocabulary, dynamic per deployment, capturing the hesitation and tone a transcript flattens.
The standard results are held to

Four rules, applied before anything is published on this page.

01 A result goes up only when there is a real figure behind it.
02 The method, the samples and who ran the measurement travel with the number.
03 No superlatives. The numbers and the architecture make the claim; the copy does not assert it.
04 No uptime, concurrency or call-volume claims. Those are not measured here yet.
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