The Gap in Speech AI

Scraped data captures words. Sonexis captures the language.

Scraped / Synthetic ✗

"Namaste, aapka swagat hai. Kya main aapki sahayata kar sakta hoon?"

→ Overly formal, unnatural grammar
→ Zero overlapping speech
→ Studio-perfect, sterile acoustics

Sonexis Conversational ✓

"Sun, abhi scene clear hai. Ticket book karun ya kal ke liye hold karein?"

✓ Fluid code-mixing and natural language switching
✓ Back-and-forth corrections, fillers, and interruptions
✓ Ambient, real-world noise layers

Dataset Delivery

What buyers receive

Each delivery is scoped to your brief and can include audio files, transcript status where transcription is scoped, speaker and language metadata, scenario context, a consent reference, QA status, and a delivery manifest. Known limitations are noted where relevant.

Audio Sample

▶

Hinglish Call Center Query

Preview availability depends on scope review

Speaker Turns

S1: Kya aap mera refund initiate kar sakte hain?

S2: Sure sir, let me check your order details.

S1: Jaldi kijiye, main hold pe hoon.

Metadata Snapshot

{
  "language": "hinglish",
  "environment": "office_noisy",
  "overlap": true,
  "speaker_id": "IN_NORTH_042"
}

Production Pipeline

From requirements sign-off to first delivery, on a timeline agreed at project kickoff. Timelines scale with language count, volume, speaker requirements, and QA depth.

01

Define Use Case

Identify your specific language, domain, and acoustic requirements (e.g., in-car Hindi ASR).

02

Scripting & Scenarios

Designing prompts that trigger natural, unscripted responses and diverse linguistic patterns.

03

Contributor Sourcing

Sourcing the right speaker profiles: native speakers across your target language, region, and demographic.

04

Managed Recording

Quality-reviewed recordings in controlled real-world settings with trained session supervision.

05

Multi-layer Annotation

Verbatim transcripts, timestamping, speaker tagging, and project-scoped annotation fields where agreed.

06

QA & Validation

Multi-step verification with human-audited QA review, scoped to the languages in your project.

07

Secure Delivery

Delivery

Per Brief

Discuss Your Requirements →

Start Building

Get structured conversational voice data delivered in standard ML-ready formats.

1

Review Dataset Specs

Browse our schema structure, annotation layers, and coverage specs for your target language and domain.

2

Define Requirements

Tell us your language, domain, speaker profiles, edge cases, and volume requirements.

3

Receive Structured Data

Get structured, delivery-ready data with timelines agreed at project kickoff. We support both one-time dataset creation and ongoing collection pipelines.

Language Coverage Matrix

Language	Scenario examples	Code Mixing	Annotation	Status
Hindi	Customer support, onboarding, product discovery, general conversation	✓ Full	Verbatim, POS, scoped tags	BUILT TO ORDER
Hinglish	E-commerce, support, product discovery, voice agent testing	✓ Native	Language ID, scoped tags	BUILT TO ORDER
Punjabi	Family, rural services, local support, advisory conversations	~ Partial	Verbatim, Timestamped	BUILT TO ORDER
Marwadi	Commerce, local trade, rural conversation, support flows	– None	Verbatim Only	BUILT TO ORDER
Indian English	BPO, support, enterprise workflows, meeting-style conversation	– None	Verbatim, scoped tags	BUILT TO ORDER
Tamil	Support, Daily Life	~ Partial	Verbatim, Timestamped	ON REQUEST
Marathi	Commerce, General	~ Partial	Verbatim, Timestamped	ON REQUEST
Multilingual / Code-Mixed	Buyer-defined scenarios after scope review	✓ Native	Per-token Lang ID, Full	ON REQUEST

Domains are scoped per buyer brief. Regulated or sensitive domains require separate consent, QA, legal, and delivery review.

The Methodology Behind the Data

We don't just record audio. We design linguistic interactions. Our managed collection process is built to improve the signal value of every approved recording for your training, fine-tuning, or evaluation workflow.

Explore Our Methodology →

🎙

Controllable Noise

Recorded across a range of real-world acoustic environments.

👥

Demographic Depth

Speaker profile coverage based on consented, project-relevant requirements.

📄

Rich Annotation

Project-scoped tagging layers such as speaker labels, intent tags, QA notes, or other agreed annotations.

🛡

Privacy Review

Privacy review available based on project scope.

Ethical AI, by Design

Transparency is our default. Every contributor is a partner.

Consent Architecture

{
  "contributor_id": "SNX_9921",
  "explicit_consent": true,
  "usage_scope": [
    "ASR_TRAINING",
    "SENTIMENT_ANALYSIS"
  ],
  "fair_compensation": true
}

Explicit Consent

Contributors agree to task-specific data use terms before collection. Consent scope is defined around the approved project use.

Fair Compensation

Contributors are paid directly for approved recordings.

Privacy First

We work to remove personal identifiers from transcript and metadata layers, with human review to reduce PII exposure.

Data Rights

Contributor withdrawal and removal requests are handled according to the agreed consent and data management process.

Real conversational voice data for AI systems in Indian and multilingual markets