A dating app engineered for real conversations
Swipe-based matching, realtime chat and trust & safety — built to turn matches into conversations rather than dead ends.
- React Native
- Node.js
- PostgreSQL
A retrieval-grounded support assistant that cites its sources, knows when to escalate, and never invents an answer.
Support was drowning in questions already answered in the documentation. We shipped an embeddable assistant that retrieves from the client's own content, answers with citations, and hands off to a human the moment it is unsure — a distinction most chatbot projects skip, and the reason most of them erode trust.
The client had tried an off-the-shelf bot and switched it off within a month: it answered confidently from general knowledge, contradicted the docs, and gave customers no way to reach a person.
Site pages, help centre articles and release notes are chunked and embedded. The model only sees retrieved passages, and is instructed to say it does not know when they do not cover the question.
Each answer links the exact source sections it drew from, so customers can verify and support can spot documentation gaps.
Weak retrieval scores, repeated rephrasing or explicit frustration route the conversation to a human with the full transcript attached — no dead ends.
A crawler re-indexes changed pages on a schedule and on deploy, so the assistant never answers from last month's docs.
An analytics view surfaces the questions that retrieved nothing — a direct backlog of documentation to write.
Swipe-based matching, realtime chat and trust & safety — built to turn matches into conversations rather than dead ends.
Analysts ask a question in plain language; the system writes verified, read-only SQL against a governed warehouse and returns the answer with its workings.
A shared operational core — identity, roles, scheduling, billing, reporting — with vertical modules for academics and for clinical care, plus a mobile app for everyone outside the office.
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