Borry

Borry

Human to text: everything people say, not only the words.

We measure voice, face and body with about 200 open models and the formulas of behavioural science. The LLM does the interpreting.

We bring the signal, not the verdict.

Signal tracks beyond the words Illustration: a transcript track made of grey word blocks, above tracks for voice, face, body motion and turn-taking that the transcript does not contain. Not real data. Words Voice Face Body Turns time
Words are what an LLM gets today. The other tracks are what Borry measures. Illustration, not real data.

The problem

LLMs get a transcript. People read much more.

Today an LLM is given a transcript. People read posture, faces and voices.

These are patterns that science has described, not magic.

A mother sees a bad day at school in a look.

An experienced doctor notices what the patient did not say.

How it works

From a recording to claims you can check.

01

Record and extract the raw signals

Record a meeting, a call or a video. About 200 audio and video models extract the raw signals:

  • voice and prosody
  • loudness
  • laughter and fillers
  • turn-taking
  • facial action units from several FACS detectors
  • gaze
  • head and body motion
02

Compute the formulas of behavioural science

Our research engine has read about 2,100 behavioural-science papers and extracted over 2,300 formulas, plus about 500 rules that combine facial action units, each stored with its source and page.

Each one is computed on the recording. Each result is labelled:

directapproximatehint
03

An LLM says what happened, and when

An LLM reads all the readings and results and says what happened when. Every claim cites the exact data row it rests on.

«Motion is generally lower between about 26 and 46 s.»video.yolopose.bins#15

First result

Transcript only vs. all the data.

Same 50-second video, same model, three analyses each, scored by a blind judge.

Transcript only All data
Claims per analysis
33
72
Claims with a measurement
8%
59%
Claims with a time
37%
43%
Claims supported by the data
96%
96%
Preferred by the blind judge
0/6
6/6

Read this as a first step. One video, one person; tests on many public videos and on labelled datasets are next. The judge is another call of the same model as the analysts, and there is no ground truth yet.

Demo

See how it works.

A short walk through the product and the test above.

Where it goes

Wherever a machine needs to understand people.

AI coaches
Therapy support
Robotics
Companions
Research
New training data for LLMs Multimodal, measured, ordered.

Team

Serial founders, building together since 2016.

Calogero MandracchiaCo-founder
Raffaele NicodemoCo-founder

They met in 2016 at Deloitte Italy's Blockchain Lab. Since then they have founded together:

  • Efebia (2018): their first company, from blockchain startup to software house and startup incubator.
  • Zipaki: the “Spotify of Italian comics”.
  • HibeTV: a “Netflix” where the stars are celebrities, together with their fans.
  • Deepen: long-form articles read aloud by professional voice actors.
  • Checksig: a cold vault for Bitcoin.
  • Terminus: a US foundation for an anonymous voting system on a fork of Monero.

Some are still running; others they have closed or left.

Contact

Talk to us.

Write to the founders:

calogero@efebia.com