Retail & E-commerce
Order, returns, and product-question intents mined from real shopper conversations to train support and shopping assistants.
NLP & Gen AI
Chatbot Training
Intent data, dialogue examples, and response evaluation that move conversational AI from “technically working” to genuinely helpful.
Overview
Conversational AI tends to break in one specific place: the gap between how a designer imagines people will phrase things and how people actually type or speak when they're frustrated, in a hurry, or unclear about what they want. Closing that gap is a data problem. We build the material that does it — intents and slots pulled from real user utterances rather than invented ones, dialogue and rephrasings that cover the messy ways the same request gets made, and human scoring of the bot's own replies for whether they were correct, appropriately toned, and actually resolved the issue. That work spans the languages and channels your users show up on.
What's included
- ✓Intent and slot labeling grounded in real user language, not assumed phrasing
- ✓Utterance collection and paraphrasing to cover the many ways one request is asked
- ✓Multi-turn conversation authoring and annotation, including topic switches and interruptions
- ✓Response scoring on correctness, tone, and whether the task was actually completed
- ✓Labeling of fallback, misunderstanding, and hand-off-to-human moments
- ✓Native-speaker coverage so intent and nuance survive across languages
Use cases
Financial Services
Carefully reviewed conversational flows for sensitive tasks like payments and account queries, where a wrong answer carries real risk.
Logistics
Tracking, delivery, and scheduling dialogue data to power customer-facing and internal operational assistants.
Insurance
Intent and flow annotation for claims, quotes, and policy questions to support guided self-service journeys.
Frequently asked questions
Do you build intents from real user language or assumed phrasing?
From real utterances wherever possible — the gap between how designers think users talk and how they actually talk is exactly where bots fail, so we ground intents and slots in genuine user language.
Can you evaluate our bot's responses, not just build training data?
Yes — we score responses on correctness, tone, and whether the task was actually resolved, including fallback and hand-off moments.
Can you cover multiple languages and channels?
Yes, with native-speaker coverage so intent survives across languages, and annotation across the channels your users actually use.
Can we start with a small pilot before committing?
Yes — a pilot on your real conversations is the clearest way to judge quality before scaling.
Ready to scale your chatbot training?
Start with a pilot batch — see the quality of the data before you commit.