Real human phone calls for voice AI
Orloo runs consented phone conversations and turns them into datasets, eval reports, and model-improvement signal for voice AI teams.
Human-labeled phone data
Built for the phone conditions public datasets miss.
Voice model quality is not only WER, benchmark latency, or isolated audio samples. Real calls include background noise, accents, degraded audio, interruptions, emotional shifts, awkward pauses, and callers who change direction mid-flow.
Orloo turns those production-like conditions into consented phone call datasets your team can use to evaluate, compare, and improve voice AI systems.
Real phone call datasets
Scenario-designed conversations
Model and prompt comparisons
Telephony and latency labels
Accent, language, and caller coverage
Audio, transcripts, and metadata
How it works
From phone scenario to labeled dataset.
Define the dataset you need
Share the call type, caller profiles, target scenarios, languages, environments, and model behaviors your team wants to capture.
Run real phone conversations
Orloo turns those specs into structured call tasks and matches them to real callers by language, accent, device, audio environment, and scenario fit.
Receive a labeled dataset
Your team receives consented audio, transcripts, metadata, perception labels, evaluator notes, and an eval report.
Real caller network
Phone data needs real callers, not just static recordings.
The hard part is not only expert knowledge. It is whether voice AI works with ordinary people calling from different devices, accents, schedules, and environments.
Matched by real-world call fit.
Orloo assigns tasks by the caller traits that matter for phone dataset quality: language, accent, device, audio quality, environment, availability, and prior reliability.
What you get
Dataset artifacts for model evaluation and improvement.
Real phone conversations
Consented callers complete controlled but natural phone conversations designed around your dataset gap.
Audio and transcripts
Full call audio and speaker-turn transcripts for every interaction, ready for model, research, and product review.
Metadata and labels
Caller profile, device, environment, scenario, turn-taking, interruption, latency, trust, intelligibility, and task labels.
Evaluator notes
Structured notes on where the call felt natural, awkward, delayed, confusing, brittle, or trustworthy.
Model-eval memo
A concise summary of recurring perception failures, likely causes, and high-signal examples for improvement.
Version comparisons
Compare model, prompt, or voice-flow versions against the same phone scenarios and human labels.
Example output
A compact dataset with the moments humans actually felt.
Each run can return phone call audio, speaker-turn transcripts, structured metadata, perception labels, evaluator notes, and a short memo connecting human signal to model behavior.
Sample model-eval note
Callers rated the voice as natural during scripted turns, but trust dropped after barge-in attempts because the model paused too long, repeated the prior state, and recovered without acknowledging the interruption.
Ready to generate phone call data?
Get in touch