Stop letting dataset gaps stall your AI roadmap. Whether you need a specific regional dialect recorded in a moving car or 10,000 hours of ultra-precise multi-speaker transcription, we build the exact audio pipelines your models need to ship safely.
In-car systems, call centers, assistants, and healthcare devices all fail differently. Your training data has to reflect that reality. Lab-quality datasets create production failures.
Train automotive voice models against the conditions they actually encounter on the road, including cabin noise, regional accents, moving vehicles, and overlapping passenger speech.
Built for in-car voice systems shipping across European regions.
Production-oriented datasets for training and evaluating multilingual STT models against real-world speech variation, overlapping speakers, and noisy recordings.
Coverage extends to harder-to-source dialects, including Nordics and regional varieties.
Synthetic speech demands absolute acoustic isolation. If a voice actor shifts position or changes microphones between sessions, your generative model picks up the variance. We build tightly controlled studio environments to capture different emotions, keeping every physical recording variable identical. You get clean, uninterrupted training data that moves your voice apps straight into production.
Studio-quality environment control, take after take.
Polished audio scripts cannot prepare your AI for real customer support calls where people interrupt and speak over each other. We record genuine, multi-speaker conversations with cleanly separated audio channels. This trains your system to handle messy cross-talks and route callers accurately.
Customer support, call routing, accessibility research, and assistive devices.
We explicitly group speakers by localized dialect, age range, and gender profile. You get authentic metadata records that match the actual speech patterns of your end-users.
We drop remote collection shortcuts entirely. Our physical crews record directly on location to catch the authentic background noise profiles and microphone conditions where your product operates.
We build completely separate test files organized by background distraction, regional accent density, and dialogue difficulty. This tracks accurate performance comparisons so that you can launch with absolute certainty.
Three steps from your data spec to a delivered dataset your training pipeline can ingest.
We lock down speaker profiles, background noise constraints, and audio formatting details so the entire pipeline matches your production environment perfectly.
We match our recording method to your target environment. For complex acoustic profiles, we deploy local field crews with physical recording rigs. For broader scale, we route tracking through secure remote pipelines. Either way, the data matches your production conditions.
We run rigorous quality checks on every take. You get fully audited audio files and structured metadata tables ready to plug straight into your model.
For every language a project ships into, we scope the dialects, age bands, and gender splits that matter for the model and recruit native speakers per region.
Where this fits: any STT, TTS, or voice product launching beyond a handful of standard accents.
When remote-only collection cannot reach the speakers or reproduce the conditions, on-site capture keeps the dataset within spec.
Where this fits: automotive, in-field assistive, regional language launches, accessibility research, customer-site recording.
Eval design is part of the deliverable. We agree with the team on what an edge case looks like for the product, then construct the test slices that expose it.
Where this fits: teams treating evaluation as a first-class deliverable rather than a last-minute sanity check.
Send over your speaker profiles, language needs, and background noise conditions. Our team will design a custom recording plan and deliver a complete project workflow within two business days.
