Why We Decided to Add AI to Our SaaS Product

Integrating AI into a SaaS product used to feel like a moonshot. Today, with tools like OpenAI and Supabase, it’s fast, accessible, and surprisingly affordable — if you know where to start.

For us, the goal wasn’t to chase hype. We wanted to add AI to our SaaS product in a way that delivered actual value to users. That meant answering one simple question: what pain point could AI solve right now?

We didn’t start by thinking, “Let’s do GPT.” We started by listening to our users. What kept coming up was a mix of things that AI could handle better than static rules or hardcoded flows:

• Users are searching for help but not finding answers.
• A support backlog that never seemed to shrink.
• Repetitive onboarding and walkthroughs that drained dev time.

That’s when we realized a lightweight, AI-powered layer could automate just enough of these tasks to make a big difference.

We didn’t want to rebuild our backend. We didn’t want to hire a team of ML engineers. So our goal was clear: ship a working AI feature fast, with tools we already trusted.

That led us to this stack:

• OpenAI for natural language understanding and generation.
• Supabase for storing structured prompts, user data, and logs.
• Next.js for the front end, already part of our system.

This post breaks down exactly how we did it and what we learned.

If you want to add AI to your SaaS product, this walkthrough will show you how to do it with clarity, speed, and minimal cost.

How to Add AI to a SaaS Product—Architecture, Tools & Setup

When we set out to add AI to our SaaS product, our biggest rule was this: no overengineering. We wanted a clean setup that was fast to build, simple to maintain, and flexible enough to grow.

So we built around three core pieces:

Step 1: OpenAI: The Brain Behind It All

We chose OpenAI’s GPT-4 Turbo as our engine. It’s powerful, stable, and offers a simple API. Here’s how we used it:

• User sends a question or request → we format it as a prompt.
• Prompt is sent to OpenAI API with relevant context (e.g., user role, recent activity).
• Response comes back in seconds, and we format it in-app.

For example:

We wrapped this in a utility function with retries, logging, and a failover message — just in case the API ever misbehaves.

Step 2: Supabase: Lightweight Backend for Speed

Supabase made it stupidly simple to manage structured data and user interactions.

We used it for:

• Storing prompt logs (helpful for debugging or analytics)
• Caching repeat queries to reduce API calls
• Pulling product data or settings to feed into AI prompts

Why Supabase?

• Instant Postgres database
• Built-in auth (tied into our existing user system)
• REST & GraphQL APIs without extra setup

This meant we could ship features without spinning up new infra.

Step 3: Frontend Setup to Support AI in a SaaS Product

Since our product already runs on Next.js, we integrated the AI feature as a simple new component.

We used:

• A chat-style UI for the support bot (feels familiar)
• API routes in Next.js to act as middleware between frontend and OpenAI
• Debounce and feedback indicators to keep UX smooth

Bonus: Context Injection

One key trick: we didn’t just send user messages to the AI. We added smart context like:

• Account type
• Feature usage history
• Last error logs (when relevant)

This let us tailor the AI response without fine-tuning a model. Pure prompt engineering.

By keeping the architecture lean, we were able to add AI to our SaaS product in under 48 hours — and it actually worked on day one.

Next up: what went right, what broke, and how we made it better.

What Worked, What Didn’t & Lessons from Adding AI to Our SaaS Product

Shipping fast is fun — but only if it doesn’t turn into a support nightmare the next day. Luckily, our first version of adding AI to our SaaS product actually worked pretty well. Still, a few things caught us off guard.

Here’s what we got right — and where we had to fix things fast.

What Worked Well

1. Shipping Fast with Clear Constraints

We gave ourselves 48 hours and stuck to it. That forced us to make decisions quickly:

• Use what we already knew (OpenAI, Supabase, Next.js)
• Don’t worry about perfect — aim for usable
• Build in small loops, test early, iterate

That speed gave us momentum and clarity. We didn’t get stuck “architecting for scale” before we had users even touching the feature.

2. Prompt Engineering > Model Tuning

Instead of fine-tuning, we leaned into structured prompting. Example:

“You are a product assistant helping SaaS users troubleshoot issues. The user is on a free plan and has just tried to export data but failed.”

Adding context like plan type, last action, and known pain points made the AI feel smart — without any fancy machine learning pipelines.

3. Built-in Feedback Loop

We added two simple things that made a big difference:

• Rating buttons after every AI response
• A “Still need help?” button that routed to human support

This gave us instant signals on where responses sucked — and helped users feel supported even when AI dropped the ball.

What Didn’t Go So Smoothly

1. Token Management & Cost Surprises

We didn’t think much about token limits at first. Then we saw some prompts blowing past 4K tokens — and response times spiked. Our bill? Not massive, but higher than expected.

Fix: We now trim history context and strip unnecessary metadata before sending anything to OpenAI.

2. Generic Answers When Context Was Missing

When we forgot to pass user-specific data (like plan or feature usage), the AI gave vague, unhelpful answers. It acted like ChatGPT out of the box.

Fix: We made context injection mandatory, and fallback prompts more informative (e.g., “We couldn’t fetch your account info right now, but here’s a general guide…”)

3. User Expectations Were High

Some users assumed the AI was a full-blown expert. When it said, “I don’t know,” they got frustrated.

Fix: We made the UI more transparent — labeled the bot clearly as “AI Assistant (Beta),” and added short onboarding tips like “Ask questions about product features, billing, or usage.”

Final Takeaways

If you’re looking to add AI to your SaaS product, here’s the truth: it’s easier than ever technically, but success comes from product thinking, not just dev work.

• Keep scope tight
• Design for real user problems
• Handle edge cases from the start
• Be honest about what AI can and can’t do

Our 48-hour build didn’t just ship — it stuck. That’s because we focused on usefulness first, not hype.

What’s Next After You Add AI to Your SaaS Product

Shipping an AI-powered feature is only the beginning. Once you add AI to your SaaS product, the next step is to make it actually feel smart — consistently, across edge cases, and with real personalization.

Here’s how we’re evolving ours beyond the MVP.

From One-Off Prompts to Memory & Context

In the initial version, each prompt stood alone. The AI had zero memory. That worked okay for quick support questions, but for deeper use cases, users wanted it to:

• Understand their context across sessions
• Remember recent actions or past questions
• Recommend next steps based on history

We’re now building a short-term memory layer using Supabase + embeddings. It stores:

• The last 3–5 interactions
• User metadata (plan, feature use, last error)
• Any preferences or flags (e.g. “power user,” “needs onboarding help”)

This makes the AI feel far more intelligent without fine-tuning a model.

Vector Search + RAG: Smarter Answers at Scale

As content grew (docs, changelogs, support articles), hallucinations started popping up. We wanted to fix that without stuffing everything into prompts.

So we’re now adding:

• Vector search using pgvector (native to Supabase)
• RAG (Retrieval-Augmented Generation) is set to pull in only relevant chunks

Example: Instead of dumping the full changelog into every support prompt, we query embeddings based on what the user asks. Clean, fast, reliable.

This combo — vector search + RAG — is how modern apps add AI to SaaS products at scale without ballooning costs or complexity.

Experimenting with Personalized AI Experiences

Beyond support, we’re testing AI to power:

• Onboarding assistants that adjust based on user role and goals
• Feature discovery nudges triggered by in-app behavior
• Data insights pulled from the user’s actual product usage

Each of these flows uses the same core: GPT-4 Turbo, user context, and a lightweight logic layer.

Tracking Real Usage & Feedback

To improve intelligently, we’re tracking:

• Prompt response times
• Cost per interaction
• Thumbs up/down rates
• Drop-off points in multi-turn chats

It’s not just about adding AI. It’s about making sure that what you add actually helps.

The Playbook Going Forward

If you want to do more than just add AI to your SaaS product, here’s the mindset:

• Automate the annoying stuff first. Support, onboarding, repetitive Q&A.
• Make it contextual. Use user data to power smart answers.
• Improve in loops. Track what works, kill what doesn’t, and ship updates fast.

We didn’t wait for the perfect setup. We shipped, learned, and now we’re building the smart layer on top, one use case at a time.

Conclusion: Adding AI Isn’t the Goal—Making It Useful Is

You don’t need a research team or a six-month roadmap to add AI to your SaaS product. You just need a real use case, a fast feedback loop, and a stack you trust.

We shipped our first AI-powered feature in 48 hours using tools we already used — OpenAI, Supabase, and Next.js. No magic. Just product thinking, clean prompts, and a focus on what actually helps users.

Since then, we’ve been layering on smarter systems — context memory, vector search, personalized flows — to make the AI better every week.

If you’re building a SaaS product in 2025, AI isn’t optional anymore. But it doesn’t have to be overwhelming. Start small, stay useful, and ship fast.