AI Customer Support Platform

A multilingual customer-service platform designed to help small businesses answer recurring questions, capture leads from conversations, and keep support activity organized in one place. The project is in development, with the core conversation and escalation logic designed first.

Small and growing businesses often handle customer support through a shared inbox or social messages, with no structured way to track what has been answered, what is still open, and what patterns keep repeating.

Recurring questions consume time that could go to higher-value work, response times are inconsistent, and there is no organized record of support activity or common customer topics.

A chatbot-first support layer that resolves common questions directly, captures structured information for anything it cannot resolve, and hands escalations to a human with full context attached. An admin view lets the business review conversations, update the knowledge base, and see recurring topics over time.

• Small businesses without a dedicated support team
• Service businesses that field repetitive pre-sale and post-sale questions
• Teams that need a lead-capture layer attached to support conversations

1. 1

   A visitor opens the chat widget and asks a question in their own words

2. 2

   The assistant matches the question against the knowledge base and FAQ flows

3. 3

   If the question is answered, the conversation is logged and closed

4. 4

   If the question needs a human, the assistant captures context and creates an escalation

5. 5

   An admin reviews escalations, open conversations, and recurring topics from a dashboard

• Chatbot flows for FAQs, lead capture, escalation, and service requests
• Conversation history and admin review
• Knowledge-base search
• Analytics on volume and recurring topics

A Next.js front end handles the chat widget and admin dashboard, calling a Python service that manages conversation logic and OpenAI API calls. PostgreSQL stores conversation history, FAQ content, and escalation records, with the whole stack designed to deploy on Vercel and a managed Postgres provider.

• Incoming messages are matched against FAQ and knowledge-base entries before falling back to a generative response
• Messages containing service requests or contact details trigger a lead-capture flow
• Unresolved or sensitive topics are flagged for escalation with the conversation context attached

• Conversation data is scoped to authenticated admin access only
• API keys and provider credentials are kept in environment variables, never in client code
• Escalation flows are designed to avoid storing sensitive personal data beyond what is needed to resolve the request

• Keeping chatbot responses accurate and on-topic without over-promising what the assistant can resolve
• Designing an escalation path that captures enough context without adding friction for the customer
• Structuring the knowledge base so it stays easy for a non-technical admin to update

• Frees time from answering repetitive questions manually
• Keeps a structured record of support activity instead of scattered messages
• Surfaces recurring topics that can inform product or content decisions

• The platform is in development and has not yet been deployed for a live business
• Multilingual coverage and escalation rules are still being refined
• Analytics views are planned but not yet finalized

• Expand knowledge-base search with better source citations
• Add role-based access for larger support teams
• Introduce satisfaction feedback collection after resolved conversations