Clean
Preserve the source, replace identifying references, detect sensitive spans, and apply the approved privacy treatment to transcript and audio.
Independent rechecks before releaseReal-world voice data
Gauss² turns sensitive, operational audio into privacy-reduced, structured, annotated, and quality-verified data for voice AI training and evaluation.
01 The premise
Real conversations contain what voice systems struggle with most: overlap, hesitation, correction, noisy channels, objections, transfers, failed attempts, and ambiguous outcomes. They also contain sensitive details and recognizable human voices.
We turn private conversation records into documented data products built around a named training or evaluation use.
02 Capabilities
One continuous process. No unexplained handoffs.
Preserve the source, replace identifying references, detect sensitive spans, and apply the approved privacy treatment to transcript and audio.
Independent rechecks before releaseCreate timestamped speech, speaker roles, readable turns, attempt sequences, and objective interaction measurements.
Timing, overlap, latency, interruptionsMap conversation stages and evidence-linked events—from needs and objections to disclosures, commitments, and next steps.
Exact evidence for every material labelChallenge unsupported conclusions, isolate uncertainty, route high-risk material to human review, and bind approval to the exact release.
Versioned, reviewable, reproducibleAutomation reduces review burden. It does not remove accountability.
03 Use cases
Turn-taking, interruption handling, latency, intent, objection response, escalation, and task completion.
↗Stage detection, structured summaries, question–answer links, coaching signals, and outcome evaluation.
↗Telephony transcription, word timing, diarization, role assignment, overlap, accents, and difficult audio.
↗Disclosure detection, script adherence, escalation, unsupported claims, and buyer-specific review rubrics.
↗04 The delivery
Every release is versioned, documented, and scoped to a defined use. Raw access is never treated as the product.
Privacy-reduced, resynthesized, or transcript-only data
Literal and readable-turn transcripts
Pseudonymous conversation and attempt identifiers
Speaker, timing, overlap, latency, and quality metrics
Evidence-linked stages, events, and relationships
Verified outcome joins when an approved source exists
Protected evaluation splits and acceptance tests
Dataset card, label guide, provenance, and quality report
05 Rights by design
Structured turns, timestamps, annotations, and metrics without distributable voice.
Redacted or resynthesized audio when acoustic and interaction signals are required.
Only when permissions, buyer use, retention, and security terms explicitly allow it.
06 How we work
A first engagement is a scoped data and evaluation pilot—not a promise to process every available recording.
Name the behavior, model decision, or failure mode the data must support.
Align sources, voice treatment, privacy, retention, and excluded uses.
Choose the conversations, labels, metrics, format, and acceptance criteria.
Include the success, failure, difficult audio, and ambiguity that matter.
Scale only after the sample proves useful for the intended purpose.
07 About Gauss²
We turn private, messy human conversations into dependable training and evaluation data while preserving evidence, permissions, and quality from source to release.
Gauss is our family name. The square represents two brothers building the company together—and the value created when real-world information becomes structured, dependable data.
08 Start a conversation
Tell us the conversation, behavior, or failure mode your current data does not cover. You will hear directly from a founder.
hello@gauss2.com