ASK Studios now ships a live AI assistant on the website. This post walks through why we built it, the architecture we chose, trade-offs we made, and the next steps (including RAG/knowledge bases and analytics).

Why we built a site-wide assistant

We wanted a visible, practical example of our capabilities — something clients can interact with right away. A chatbot:

• Demonstrates real-time product thinking
• Lets visitors self-qualify before reaching out
• Acts as a center-piece for AI & automation conversations

Architecture (high level)

The assistant uses a simple, modular architecture that’s easy to iterate on:

• Frontend: small React widget using Framer Motion for smooth animations and a minimal footprint.
• API: server-side /api/chat route in Next.js that proxies to an LLM (OpenAI or AIMLAPI) and handles basic routing, rate-limiting, and logging.
• Future: a vector DB for RAG (pgvector / Supabase / Pinecone) so replies are grounded in our docs and case studies.

Trade-offs we considered

• Direct LLM calls vs RAG — direct calls are faster to ship but can be costly for large context. RAG is the scalable option for precise, up-to-date answers.
• Rule-based fallback — we included simple rule responses for product names so the demo is helpful even without perfect LLM connectivity.
• Privacy & safety — all user messages are handled server-side; sensitive data is treated carefully (with opt-out in future releases).

Implementation notes

Frontend

• The chat UI is implemented as a floating widget that loads only when needed.
• UX choices: keep messages short, present "Suggested topics", and show contact CTA when users indicate intent.

Backend

• /api/chat accepts a small set of messages, attaches a short system prompt, and calls the LLM.
• To reduce cost, we:
  1. Keep the system prompt brief.
  2. Limit message history to the last N turns.
  3. Plan to add RAG for long-form product docs.

Next steps & roadmap

1. Add RAG + vector search

   • Create a knowledge base of product docs, portfolios, and FAQ.
   • Retrieve only relevant chunks per question to save tokens and increase accuracy.

2. Assistant analytics

   • Track intents, top questions, and conversion (how often visitors contact us after chatting).

3. Expand knowledge base

   • Add deep product pages and case studies so the assistant can cite specifics.

TL;DR

This assistant is a small, high-impact demo that shows clients we can:

• Deliver product UX
• Integrate LLMs responsibly
• Plan for scale with RAG and analytics

If you want a custom assistant for your product (support bot, onboarding helper, or internal dashboard assistant), let’s talk — contact us at info@askstudios.net.