AI Assistant Embedded on a Portfolio Site

Status: Live demo Demo: portfolio.robiriu-dev.my.id

Portfolio AI Assistant

Executive Summary

An AI chatbot embedded on a personal portfolio website that answers visitors' free-form questions about the owner — skills, projects, experience, and availability — grounded only in the site content so it never invents facts, and gently nudges interested visitors (recruiters, potential clients) toward making contact. The assistant is a floating widget that appears on every page and drops into any existing site.

How It Works

  1. The portfolio content (about, skills, projects with metrics, experience, contact, availability) is compiled into a knowledge base.
  2. A system prompt constrains the assistant to answer only from those facts, in the third person, concisely, and to direct hiring intent to the contact channels.
  3. A floating chat widget (bottom-right, on every page) sends the conversation to a Gemini-backed endpoint and renders the reply, with suggestion chips to start the conversation.

Key Features

  • Grounded, no hallucination — answers come only from the profile knowledge base; if something is not covered, the assistant says so and offers to connect the visitor directly.
  • Hire-intent aware — when a visitor shows interest, it surfaces availability and the contact route.
  • Embeds anywhere — a self-contained widget that overlays an existing portfolio or marketing site.
  • Configurable per person — swap the knowledge base for any individual's CV/site content, or auto-ingest existing pages into a small vector store for retrieval.

Technology Stack

Layer Technology
LLM Gemini 2.5 Flash via Vertex AI
Grounding Profile knowledge base injected into the system prompt
Frontend / API Next.js 15 (App Router), TypeScript, Tailwind CSS
Widget Floating, page-wide chat component with suggestion chips
Deployment pm2 + nginx, Let's Encrypt SSL on a VPS

Skills Demonstrated

  • Grounded conversational AI with strict anti-hallucination constraints
  • Embeddable widget UX design
  • Knowledge-base modelling from unstructured profile content
  • Vertex AI integration via a GCP service account

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