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
How do you guarantee quality forchatbot training?
Every project runs through multi-tier QA: annotators are benchmarked against gold-standard tasks before production, batches are statistically sampled against agreed accuracy targets, and ambiguous cases are escalated and documented in a living labeling guide. You receive accuracy reports with every delivery.
Can we start with a small pilot before committing?
Yes — we recommend it. A paid pilot batch on your real data lets you evaluate our quality, turnaround, and communication before scaling. Pilot learnings become the project's labeling guide.
How is our data kept secure?
Client data is encrypted in transit and at rest, access is limited to the assigned project team under NDAs, and we support VPN-restricted or client-hosted workflows where data cannot leave your environment. Retention and certified deletion terms are set per engagement.
What tools and output formats do you support?
We work in your annotation platform or ours, and deliver in the format your pipeline expects — COCO, YOLO, Pascal VOC, JSON, CSV, or a custom schema — with delivery via API, cloud bucket, or scheduled export.
Ready to scale yourchatbot training?
Start with a pilot batch — see our quality on your data before you commit.