“Can we add AI to it?”
I hear this on at least half of my discovery calls. The founder has an MVP idea — a marketplace, a SaaS tool, a booking platform — and somewhere in the conversation, they ask about AI. Sometimes it’s specific (“I want AI to write the product descriptions”). More often, it’s vague (“It should have AI somehow”).
The problem isn’t the ambition. It’s that “AI” covers everything from a $20/month API call to a $500K machine learning pipeline. Without knowing which category your feature falls into, you can’t budget for it, scope it, or decide whether it belongs in your MVP at all.
Here are the four levels of AI integration, ordered from simplest to most complex. Each level includes what it actually involves, what it costs, and whether it makes sense for a first version.
Level 1: AI-Assisted Content Generation
What it is: Your product calls an AI API (usually OpenAI’s GPT-4o or Anthropic’s Claude) to generate, summarize, or transform text. The AI handles a specific content task that would otherwise require manual work.
Real examples:
- An e-commerce platform that auto-generates product descriptions from a photo and bullet points
- A legal tech tool that summarizes long contracts into plain-English summaries
- A job board that rewrites candidate bios into polished professional profiles
- A CRM that drafts follow-up emails based on meeting notes
How it works technically: Your app sends a text prompt to an API endpoint. The API returns generated text. Your app displays it to the user, usually with an “edit before publishing” step. The integration is a single API call per request — 10-20 lines of backend code.
Cost:
- Build time: 1-3 days to integrate into an existing product
- API cost: $20-$100/month for most MVPs (OpenAI charges roughly $0.01-$0.03 per 1,000 words of input/output on GPT-4o-mini)
- Monthly cost scales linearly with usage — 10,000 generations/month might cost $50-$150
MVP or v2? This is MVP-safe. It’s cheap to build, cheap to run, and often the feature that makes users go “oh, that’s useful.” If your product involves any kind of content creation or transformation, Level 1 AI is a strong differentiator at minimal cost.
Level 2: Smart Search and Recommendations
What it is: Instead of keyword matching (searching for the exact words a user types), your product understands what the user means and surfaces relevant results. This powers features like “customers who bought X also bought Y,” “articles related to what you just read,” or “find similar properties.”
Real examples:
- A marketplace that recommends listings based on browsing behavior, not just category filters
- A learning platform that suggests the next course based on what the student has already completed
- An internal knowledge base that finds relevant documents even when the search terms don’t exactly match
How it works technically: Text (product descriptions, user queries, documents) gets converted into numerical vectors — called embeddings — using a model like OpenAI’s text-embedding-3-small. These vectors are stored in a vector database (Pinecone, Weaviate, or Supabase’s pgvector extension). When a user searches or views an item, the system finds the nearest vectors — meaning the most semantically similar content.
Cost:
- Build time: 3-7 days for a basic recommendation or semantic search system
- Infrastructure: Supabase pgvector is free on the free tier (up to 500MB). Pinecone’s free tier handles 100K vectors. For most MVPs, vector storage is effectively free.
- Embedding API cost: ~$0.02 per million tokens with OpenAI’s small model. Negligible for most applications.
MVP or v2? It depends. If search or discovery IS your product’s core value (like a marketplace or knowledge base), add it to the MVP. If it’s a nice-to-have improvement on top of a product that works fine with basic filters, defer to v2. The 3-7 day build time is meaningful when you’re working with a 21-day sprint.
Level 3: Workflow Automation with AI Agents
What it is: AI handles multi-step tasks that would otherwise require a human. The AI doesn’t just generate content — it makes decisions, takes actions, and routes work through your system.
Real examples:
- An email triage system that reads incoming emails, categorizes them (support, sales, spam), drafts responses, and routes them to the right team member
- A lead scoring system that analyzes form submissions, enriches them with public data (LinkedIn, company website), and assigns a priority score
- A content moderation system that reviews user-generated posts, flags violations, and auto-removes clear spam while queuing edge cases for human review
- An inventory management system that monitors stock levels, predicts demand based on historical patterns, and auto-generates purchase orders
How it works technically: This typically involves chaining multiple AI calls together with business logic. You might use a framework like LangChain, or build custom pipelines that combine API calls, database queries, and conditional logic. Tools like n8n (open source, self-hosted) or Make.com (cloud-based, starts at $9/month) provide no-code/low-code automation builders that can orchestrate AI steps.
Cost:
- Build time: 1-3 weeks for a reliable automation pipeline
- Infrastructure: Depends heavily on volume. Low-volume automations (100-500 tasks/day) run on API costs alone ($50-$200/month). High-volume systems need dedicated infrastructure.
- Ongoing: AI agents require monitoring and tuning. Budget 2-4 hours/month for maintenance once live.
MVP or v2? Almost always v2. Workflow automation requires your product to have real users generating real data before you can build meaningful automations. Building an AI email triage system before you have 50+ emails/day flowing through the system is premature optimization. Ship the MVP with manual workflows, then automate the bottlenecks.
Level 4: Core AI Product
What it is: The AI IS the product. Remove the AI and there’s nothing left. The entire value proposition depends on a model performing a specific task well.
Real examples:
- An image generation tool (like Midjourney)
- A medical diagnosis assistant that analyzes symptoms and suggests conditions
- A financial forecasting tool that predicts revenue based on historical data
- A voice cloning product that generates speech in a specific person’s voice
- A code review tool that analyzes pull requests and suggests improvements
How it works technically: This can range from fine-tuning an existing model (like GPT-4) on domain-specific data, to training a custom model from scratch, to building complex pipelines that combine multiple models. The technical approach depends entirely on the use case.
Cost:
- Build time: 4-12 weeks minimum for a functional prototype
- Infrastructure: $200-$2,000/month for model hosting and inference, depending on scale
- Data: You may need labeled training data, which can cost $5K-$50K+ to collect and annotate
- Ongoing: Models degrade over time as the world changes. Budget for continuous evaluation and retraining.
MVP or v2? If AI is your product, it’s obviously in the MVP — but scope aggressively. Start with the narrowest possible use case. Don’t build a “general AI writing assistant.” Build “an AI that writes real estate listing descriptions from property photos and specs.” Narrow scope means you can validate the AI’s quality with less data, less training, and less time.
The Decision Matrix
| Level | Build Time | Monthly Cost | MVP? |
|---|---|---|---|
| 1. Content generation | 1-3 days | $20-$100 | Yes |
| 2. Smart search | 3-7 days | $0-$50 | If core to product |
| 3. Workflow automation | 1-3 weeks | $50-$200 | No — v2 |
| 4. Core AI product | 4-12 weeks | $200-$2,000+ | Only if AI IS the product |
The Founder’s AI Checklist
Before adding AI to your product, answer four questions:
1. What specific task will AI perform? Not “make it smarter.” What exact input goes in and what output comes out? If you can’t describe the input-output pair in one sentence, the feature isn’t scoped yet.
2. What happens when the AI is wrong? Because it will be wrong. Does the user see the error and correct it (acceptable for content generation)? Does it silently break a workflow (dangerous for automation)? Does it give a wrong medical diagnosis (potentially catastrophic)?
3. Can a human do this task manually for the first 100 users? If yes, ship the MVP without AI, validate demand with manual operations, then automate with AI once you understand the edge cases. This is called the “Wizard of Oz” approach and it’s how many successful AI companies started.
4. Is AI the differentiator or the convenience? If AI is what makes your product unique (Level 4), invest in it from day one. If AI is a convenience feature on top of a product that works without it (Levels 1-3), build the product first and add AI when you have users to test it on.
What to Do Next
If you’re considering AI features for your product, start by writing down the specific input-output pairs for each AI feature you want. Then map each one to a level (1-4) and decide: MVP or v2?
For most founders, the right move is to scope an MVP without AI, validate demand with real users, then add Level 1 or Level 2 AI features in the first update. The founders who try to build Level 3-4 AI into their first version almost always run over budget and over timeline.
If you’re building a product with an online presence, make sure your website itself is pulling its weight too — here’s how to decide whether you need a mobile app or a web app.
Ready to scope your product and figure out where AI fits? Book a free discovery call and we’ll map it out together.