Assistants fail when their training data does not match how people actually talk. We build conversational AI training data across intents, entities, multi-turn dialogue and multilingual utterances — labeled by native speakers and reviewed through four-stage QA. It plugs into our NLP annotation services, voice data and RLHF and LLM fine-tuning so chat and voice assistants understand and respond well.
Build a conversational datasetWe build the labeled dialogue and feedback data conversational systems need to interpret intent and reply well.
Conversational AI training data is more than transcripts — it is intent labels, dialogue acts, slot and entity tags, multi-turn context and preference judgments that teach assistants what a helpful reply looks like. Graveiens builds this conversational AI training data with trained annotators and SMEs across 25+ languages, then verifies it through a four-stage QA workflow so edge cases and code-switching are handled correctly. Combine it with NLP annotation services, audio transcription and LLM fine-tuning to take an assistant from raw logs to a reliable production model.
Scope a data programCompliance-first delivery and a pay-on-approval model that de-risks every engagement.
Native speakers across 25+ languages.
Calibrated annotators and QA.
Compliant data with audit trails.
Invoiced only for approved deliverables.
Send us a sample task. You only pay for deliverables you approve.
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