Conversational AI & Voice Data Annotation

Conversational AI

Data that teaches assistants to actually understand

Synnth delivers the speech and dialogue datasets behind chatbots, voice assistants, and IVR systems — natural conversations, real accents, and intent-labeled data in 40+ languages.

Overview

Conversational AI fails in the gap between what a script says and how people actually talk — interruptions, filler words, code-switching, regional accents, and ambiguous intent. Synnth collects and annotates conversational data that reflects real usage, sourced from native speakers across 40+ languages and dialects, so your models generalize past the lab.

What we do

Data collection & annotation across every AI modality

From sourcing raw data to delivering production-ready labeled datasets — Synnth covers the full pipeline across the four core data types that power modern AI.

Data collection

Scripted and unscripted conversational recording
Wake word and command phrase capture
Telephony and far-field / smart-speaker session recording
Native-speaker recruitment across 40+ languages and dialects
Multi-turn dialogue and role-play scenario collection
Prompt-response generation for LLM fine-tuning

Annotation

Intent and entity labeling
Dialogue act and turn-taking annotation
Verbatim transcription with disfluency and filler-word tagging
Speaker diarization
Sentiment and emotion tagging
Language and accent identification
Slot-filling and multi-turn context annotation
Response quality and helpfulness rating for RLHF-style pipelines

Why Synnth for conversational AI

Every Synnth project follows the same structured pipeline, regardless of data type, language, or volume. That consistency is not bureaucratic — it is the mechanism through which we maintain quality at scale.

Native-speaker sourcing, not synthetic voices. Recordings come from consented native speakers with real accents, background acoustics, and speech patterns — the variation your model needs to handle in production, not a studio-clean approximation of it.

Low-resource language coverage. Beyond the major world languages, our network extends into regional dialects and low-resource languages that most annotation vendors can’t staff.

Full pipeline, one vendor. From recruiting speakers and running recording sessions through to intent labeling and QA — a single team owns the pipeline end to end, which means fewer handoffs and faster iteration on your labeling guidelines.

48-hour pilot batches so you can validate a labeling schema against your model before committing to full-volume production.

Delivery formats

2,000+ expert annotators, matched to your domain

JSON, CSV, or your NLU platform’s native schema (Rasa, Dialogflow, custom intent taxonomies) — plus raw audio in your required sample rate and format.

Ready to build AI on data you can trust?

Whether you need a small pilot batch or an ongoing production annotation pipeline — tell us what you’re building and we’ll tell you exactly how we can help.

FAQs

Common questions

Everything you need to know before starting a AI Data collection or annotation project with Synnth.

💡 Can’t find your answer here? Talk to our team — we typically respond within one business day.

Can you collect data in languages beyond the major world languages?

Yes, we support 40+ languages including regional dialects and lower-resource languages through our native-speaker recruitment network.

Do you provide both scripted and natural conversational data?

Yes. We run scripted prompt-response sessions as well as unscripted, natural dialogue collection, depending on what your model needs.

Can you support RLHF-style response ranking and quality annotation?

Yes. We annotate response quality, helpfulness, and preference rankings for teams fine-tuning conversational models with human feedback.

Get started

Start your AI Data collection or annotation project today

Tell us your use case, action taxonomy, environment, and volume. Our team responds within one business day with a scoping plan and no-obligation quote.