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MERN · Python · Pinecone · LLM

AI Support Copilot for Knowledge Bases

A React front end and Node API over a Python retrieval service. Teams upload help-centre articles, PDFs and past tickets; agents get cited answers and suggested replies inside the ticket view.

ReactNode / ExpressMongoDBPythonPineconeRedis
Ticket view with a cited, suggested reply
Knowledge sources and indexing status
Quality dashboard: accepted vs edited replies

Screens are illustrative layouts of the product, not client screenshots.

Overview

What it is

A support-team copilot. Companies connect their help centre, PDFs and past tickets; the system indexes them and, inside the ticket view, suggests a reply with the exact source passages it relied on, so an agent can approve, edit or discard it.

The problem

Why it was needed

Support agents spend most of their time searching old tickets and docs for the same answers, and generic chatbots invent answers that can't be checked. Teams needed suggestions they could trust and trace back to a source.

Citedevery suggested answer links to its source passage
Per-workspaceseparate index and access for each customer
Asynclarge uploads processed in the background

Features

What We Built

Document ingestion

Help-centre pages, PDFs and past tickets are chunked, embedded and indexed through queued jobs.

Cited answers

Replies show the passages they were built from, with links back to the original document.

Reply drafting

Agents get a draft in the ticket view and can accept, edit or discard it in one click.

Workspace isolation

Each customer's content lives in its own index with role-based access.

Feedback loop

Accepted and edited replies feed a quality dashboard that shows where the knowledge base has gaps.

Architecture

How It Fits Together

  1. React agent UIticket view and reply panel
  2. Node / Express APIauth, workspaces, tickets
  3. Job queueingestion and re-indexing
  4. Python retrieval servicechunking, embeddings, ranking
  5. Pineconeper-workspace vector index
  6. LLMgrounded reply drafting

Retrieval and generation sit behind the API, so the app stays fast while indexing runs in the background.

Roadmap

From Idea to Launch

A typical delivery roadmap for a project of this kind.

  1. 1

    Discovery

    1 week
    • Review the help centre, ticket volume and common questions
    • Choose chunking and retrieval approach
    • Define success: accepted-reply rate and handling time
  2. 2

    Foundations

    1-2 weeks
    • Workspace and user model, auth and roles
    • Ticket view in React
    • Queue and storage setup
  3. 3

    Retrieval engine

    2-3 weeks
    • Ingestion jobs for docs, PDFs and tickets
    • Embedding and Pinecone indexing per workspace
    • Citation-aware answer generation
  4. 4

    Agent experience

    2 weeks
    • Reply drafting inside the ticket view
    • Feedback capture and quality dashboard
    • Prompt and retrieval tuning on real tickets
  5. 5

    Launch

    1 week
    • Pilot with one team, then widen
    • Monitoring, rate limits and cost tracking
    • Handover documentation

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