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TL;DRLong chat sessions lost context and gave thin analytics → an OpenAI + LangChain engine with no-code flows and multi-channel embeds → 10+ live deployments, −40% support load per the client.
- Role
- Full-Stack Developer
- Client
- AI product startup
- Year
- 2024
- Stack
- React, TypeScript, Node.js, OpenAI API
In one paragraph
A chatbot product with no-code flows, multi-channel embeds and conversation analytics. Support teams handle fewer repetitive questions, and the product learns from every new deployment.
Context
Brainwave is a conversational assistant for websites, apps and support desks, built to answer in real time with context from the client's own content.
Problem
Long sessions lost context and slowed responses, and free-form conversations were hard to turn into analytics a support team could act on.
What I did
- 01
LLM engine
OpenAI with LangChain for contextual answers; context windows trimmed per session to keep latency low.
- 02
No-code flows
Admins build and deploy conversation flows without a developer.
- 03
Multi-channel
One assistant embedded on websites, in mobile apps and inside CRM tools over WebSockets.
- 04
Analytics
Intent tagging, sentiment and FAQ suggestions from real conversations.
Results
−40%
support response load for clients, per the client
10+
live deployments
Stack
- React
- TypeScript
- Node.js
- OpenAI API
- LangChain
- MongoDB
- WebSockets