A quick look at how Graveiens AI partners with teams to deliver human data for AI models.
Assistants fail when their training data does not match how people actually talk. We build conversational datasets 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.
This 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 AI builds these datasets 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 programGreat assistants are trained on great conversations. Graveiens AI builds conversational-AI data — intent and slot labelling, dialogue collection, response ranking and evaluation — so your chatbots, voice assistants and support models understand users and reply well.
We create and label multi-turn dialogue to your taxonomy across 25+ languages, and hold every batch to a measured accuracy bar through our four-stage quality workflow.
Talk to our conversational-AI teamFrom intents and dialogue to response evaluation.
Intent, entity and slot annotation for understanding models.
Scripted and natural multi-turn conversations for training.
Preference and quality scoring to improve assistant replies.
Conversation data and localisation across 25+ languages.
Representative programmes we support.
Intent and dialogue data for spoken assistants.
Intent, tone and safety data for service bots.
Preference and evaluation data for generative assistants.
Assistants fail on the utterances they never saw, so we deliberately cover paraphrase, edge cases and languages, and keep labels consistent with gold-standard checks and four-stage review. Pay-on-approval keeps a first pilot low-risk.
Start a conversational-AI pilotHow our conversational AI training data services compare with other providers on focus, languages and QA.
| Provider | Core focus | Modalities | QA / accuracy approach | Engagement model |
|---|---|---|---|---|
| Graveiens AIUs | Conversational AI data — intents, entities, dialogue, utterances | Text, dialogue, audio, multilingual | Linguist and SME review vs a gold standard, ISO 9001:2017 | Pay-on-approval pilots, managed programs |
| Macgence | Chatbot and dialogue training data | Text, dialogue, audio | Managed crowd with human QA | Project-based managed teams |
| Cogito Tech | Chatbot and intent annotation | Text, dialogue | Human-in-the-loop QA | Managed teams |
| Shaip | Healthcare dialogue and voice data | Audio, text, dialogue | Domain-expert QA | Off-the-shelf datasets plus services |
| iMerit | Dialogue and intent data operations | Text, dialogue | Expert-in-the-loop QA | Dedicated managed teams |
| Sama | Dialogue and multimodal annotation | Text, image, dialogue | SamaAssure QA | Managed workforce |
| Surge AI | Dialogue and preference data for LLMs | Text, dialogue | Expert human raters | API plus managed service |
Compliance-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.
Book a pilot