We recommend focusing on five areas where AI can move the needle early:

• Language & Interaction (LLMs, chat, summarization)
  
  
• Automation & Integration (workflow tools)
  
  
• Decision-Making Agents (agentic workflows)
  
  
• Visual & Content Creation (images, video)
  
  
• Scalable Infrastructure (MLOps, retraining, deployment)
  
  

Below, we map each category to a best-in-class tool and a practical example. Let’s get to it.

1. OpenAI GPT-4o, The Most Reliable LLM for Early-Stage Products

Whether you’re building an AI assistant, support chatbot, or summarization feature, GPT-4o offers the best mix of capability, affordability, and ease of integration.

Use it for:

• Customer Q&A, onboarding flows
  
  
• Content generation (e.g. landing page copy)
  
  
• Semantic search, document summaries
  
  

Example:
Launching a B2B tool? Integrate GPT-4o as a support bot that answers onboarding questions and highlights key features. You save on headcount and collect data on what users struggle with most.

2. Make.com, AI-Powered Automation Without the Dev Overhead

Make.com (formerly Integromat) is ideal for stitching together LLMs, webhooks, and databases without writing thousands of lines of code.

Use it for:

• Connecting GPT with your backend (e.g., to auto-tag user input)
  
  
• Automating data flows (e.g., AI-enhanced form responses)
  
  
• Building scalable internal workflows (e.g., AI triage of leads or support tickets)
  
  

Example:
If you’re building a hiring platform, use Make to route job seeker profiles through GPT for classification, then tag them in Airtable. No need for a data science team, just a clear process and good prompts.

3. Flowise, Agent Workflows, Made Simple

Flowise is a visual framework that lets you chain together AI agents, tools, and data flows. Think of it like Zapier for LLMs but smarter and designed for multi-step logic.

Use it for:

• Automating complex workflows like vendor selection or financial analysis
  
  
• Creating agents that perform multi-step reasoning
  
  
• Running decision chains in customer support or procurement
  
  

Example:
Building a procurement platform? Use Flowise to auto-source suppliers, compare pricing and delivery terms, and surface a shortlist before a human ever gets involved.

4. Midjourney, Visual Assets Without a Designer

Early-stage startups need branding, mockups, and visual polish but hiring designers is often out of scope. Midjourney helps you generate sharp, on-brand visuals in minutes.

Use it for:

• Generating UI mockups for investor decks
  
  
• Building social media and ad creatives
  
  
• Creating landing page visuals without Photoshop
  
  

Example:
Launching a fashion MVP? Use Midjourney to generate product renders in multiple styles and colors then use them in A/B tests to validate what clicks.

5. HeyGen, Video Content at Scale

From onboarding to marketing, video is king but production is expensive. HeyGen creates AI-powered video avatars from text, so you can launch with professional-grade content from day one.

Use it for:

• Customer onboarding flows
  
  
• Product explainers and updates
  
  
• Scalable, multilingual support videos
  
  

Example:
Building a fintech MVP? Use HeyGen to explain your app’s features in a 60-second video personalized by segment (e.g., freelancers vs. small biz). No camera crew required.

6. OpenAI + Vector Store, Smart Document Search with RAG

If your app deals with a large corpus of content, docs, policies, product manuals combine GPT with a vector store (like Pinecone or Weaviate) to create powerful semantic search.

Use it for:

• Legal clause lookup
  
  
• Internal policy assistants
  
  
• Context-aware chatbots that pull from your own content
  
  

Example:
Building a compliance tool? Let users ask questions like “What’s the latest update on GDPR data retention?” and serve them answers grounded in your actual docs.

7. AWS SageMaker, MLOps That Grows With You

When you’re ready to go from “clever hack” to “scalable product,” SageMaker offers the infrastructure to train, test, deploy, and monitor real models in production.

Use it for:

• Deploying ML models at scale (fraud, churn, personalization)
  
  
• Auto-retraining models with fresh data
  
  
• Monitoring AI performance over time
  
  

Example:
Launching a healthtech MVP that predicts patient no-shows? Train and deploy your model with SageMaker, and scale with confidence as your data grows.