How to create products people truly want in the era of AI: The complete guide
Software built with AI engineering is disrupting traditional software development.
It’s 2025, and the AI gold rush is in full swing. Funding for AI startups has hit record highs, nearly half of all venture capital in 2024 went to AI-driven companies.
There are tens of thousands of AI products launching worldwide.
Every week, another founder proclaims their app will “change everything” with machine intelligence. Yet amid this fervor, a timeless startup truth remains: you can build with the most advanced tech in the world, but if it doesn’t solve a real user problem, it’s going nowhere.
Six months ago, building a working web app meant weeks of setup, debugging, and iteration. Today, you can describe your idea to an AI and have a functioning prototype in under an hour. This isn't hyperbole, it's the new reality reshaping how we think about software development.
The tools that made this possible didn't exist a year ago. Cursor's AI pair programming has redefined what "coding" means. Replit Agent can autonomously architect entire applications. Lovable, V0, etc generate production-ready apps from natural language. Bolt creates full-stack apps with deployment pipelines included.
But here's what most developers are missing: this isn't just about coding faster. It's about fundamentally reimagining the entire product development lifecycle from initial research through deployment and iteration.
The developers who master this shift won't just build products faster; they'll build better products by spending less time on boilerplate and more time on what actually matters: understanding users and solving real problems.
Why AI-First Development changes everything
The Old Way: Weeks of Friction
Traditional development meant:
2-3 days setting up environments and boilerplate
Hours debugging configuration issues
Days building basic CRUD operations
Weeks on UI components and styling
More weeks on deployment and DevOps
Hundred hours of brainstorming and talking to users
By the time you had something to show users, you'd already invested months of work into assumptions that might be completely wrong.
The new way: Hours to working product
AI-first development flips this entirely:
Minutes to working prototype from idea
Hours to polished MVP with real functionality
Days to user testing and iteration
Weeks to market-ready product
This speed isn't just convenient, it's strategically transformative. You can now test 10 product concepts in the time it used to take to build one.
When you can iterate this fast, you enter a new category of product development:
Hypothesis-driven building: Test ideas before committing significant resources
Real-time user feedback loops: Build, test, iterate within the same day
Parallel experimentation: Try multiple approaches simultaneously
Fail-fast mentality: Discard bad ideas in hours, not months
In the new era of AI, building software is easier, faster, and cheaper than ever. One person can conjure up a prototype overnight with no-code tools and AI helpers that would have taken a team weeks in the past. The playing field is remarkably level.
The opportunity: you now have superpowers to discover what users need, test ideas rapidly, and iterate toward a great product.
The challenge: with great power comes great temptation to skip steps. It’s never been easier to churn out an AI-powered app that no one truly wants. As investor Lak Ananth put it, “AI hasn’t rewritten the rules. It’s made them harder to follow.” The fundamentals, a clear problem, real value delivery still apply, but the terrain is faster and more ambiguous.
So how do you harness AI’s superpowers without falling for the hype? How can founders, indie hackers, and product builders in 2025 create software people genuinely want? In this thought-leadership guide, we’ll explore exactly that. We’ll start from first principles – understanding your users – and walk through how modern AI tools can turbocharge each step of product development: from user research and idea validation, to rapid MVP building, to feedback analysis, all the way to the psychology of product-market fit. Along the way, we’ll share tactical tips, examples, and recommended tools.
By the end, you should have a clear game plan for leveraging AI not as a gimmick, but as a genuine amplifier of product insight and execution. Let’s dive in.
The six-step AI-native playbook
Step 1: AI-Powered User research and Empathy building
Great products start with empathy, a deep understanding of users’ needs, pains, and motivations. In the past, cultivating this understanding meant weeks of interviews, surveys, and observation. Today, AI can compress those weeks into hours. AI-powered user research tools make it possible to synthesize mountains of user data with unprecedented speed.
Instant Feedback Synthesis: Imagine you have 500 survey responses or support tickets. Instead of manually sifting through each one, you can now feed them to an AI to get an immediate summary of common themes. AI excels at surfacing a user pain point that might have taken the team much longer to notice on their own.
User research used to require surveys, interviews, and weeks of analysis before you could even start building. For engineering or product teams, this process can be exhausting which is why only 1% of companies do it properly, while 99% of successful companies make it a priority.
Use AI to conduct interviews and market research, rapidly synthesize insights, and generate testable hypotheses all in a fraction of the time.
Tools to Get Started:
Claude / ChatGPT – for market analysis
Example: “Analyze the pain points of small business owners managing inventory across multiple channels.”Perplexity – for real-time competitive research
Instantly surface what your competitors are building and how the market is evolving.Codiris – for AI-powered user research
Codiris doesn’t just code it talks to your users. It conducts interviews, sends surveys, and extracts actionable insights to help you build what people truly want.Actionable Framework:
Start with a broad problem space ("team communication is broken")
Use AI to generate 20+ specific pain points
Cross-reference with real market data using AI search tools
Identify the top 3 most addressable problems
Generate solution hypotheses for rapid testing
Pro Tip: Don't just ask AI for ideas, ask it to challenge your assumptions.Prompt: "What are 5 reasons why might fail, and how would you test those risks?"
Step 2: AI-Assisted Ideation and Design
The days when designing a user experience was a bottleneck are quickly becoming a thing of the past. Modern design tools now integrate AI to empower everyone—from seasoned designers to non-designers—to create compelling UIs and iterate at unprecedented speeds.
The Power of Prompt-to-Design
The core of this revolution lies in prompt-to-design capabilities. You no longer need to manually drag-and-drop every UI element or spend hours tweaking pixels. If you can describe your app in words, AI can draft it for you.
Tools that Lead the Way:
Figma Make (Beta): Announced at Config 2025, this generative AI tool turns text prompts into complete design systems, prototypes, and developer-ready code snippets.
Uizard: Known for its "Autodesigner," Uizard lets you describe what you want and instantly generates designs. Its Wireframe Scanner even converts sketches into interactive prototypes.
Stitch (formerly Galileo AI): Branded as "ChatGPT for UI design," Stitch generates beautiful, editable UI mockups from prompts and speeds up creative ideation.
Codiris Design: Transforms raw user input into a production-ready blueprint by orchestrating multiple agent workflows:
UX Flow Generation: Maps out user journeys step by step.
System Architecture: Designs backend infrastructure, APIs, and data logic.
User Story Drafting: Translates interactions into testable Agile narratives.
Real-Time Validation: Detects inconsistencies across UX and specs before coding begins.
These tools drastically reduce the cost and time to reach user-tested design iterations. You no longer need to choose between velocity and quality, AI gives you both.
Step 3: Rapid prototyping & development with AI code generation
This step is about translating your AI-assisted designs and research into real, functional software fast.
AI tools are no longer limited to code suggestions. They can scaffold entire applications, handle complex integrations, and deploy usable products within hours.
Less Boilerplate, More Business Logic: Let AI handle repetitive setup so you can focus on unique product value.
Accelerated MVP Creation: Build usable versions of your product in days, not months.
Design-to-Code Bridging: Tools now translate UI designs directly into working code, reducing friction between teams.
AI Tooling Landscape:
Cloud Dev Environments:
Replit, Bolt, V0.dev, Lovable — Full-stack generation from frontend to database.
Best for: Founders, designers, and PMs who want to quickly build functional prototypes.
Local Dev Assistants:
GitHub Copilot, Cursor, Windsurf, Claude code, codex, Integrated into IDEs for context-aware code generation.
Best for: Engineers aiming for control and production-grade quality.
Codiris-Specific Workflow: Codiris uses an agentic system to orchestrate:
UI code generation
Backend service setup
Infrastructure configuration
Validation across modules
Developers supervise, refine, and deploy with AI doing the heavy lifting.
This isn’t just about faster code. It’s about rethinking who writes it, how, and why.
Step 4: AI-Powered QA & Testing
Ship with confidence at the speed of AI.
Quality Assurance has historically been a bottleneck. Manual testing, flaky automation scripts, and last-minute bugs have slowed down even the best teams.
With AI, QA is no longer just about catching bugs. It’s about proactive quality—predicting failures, generating tests, and closing the loop with users faster than ever.
Automated Test Generation:
Use AI tools to generate test cases automatically from your codebase or user flows
Auto-update tests as your code changes—no more brittle scripts
Bug Detection Before They Happen:
Predict regressions with tools that analyze previous issues and usage patterns
Use LLMs to scan code for likely failure points before a single user sees them
End-to-End Testing at Scale:
Simulate thousands of real-world user journeys using AI agents
Visual regression tools enhanced with AI catch UI drift instantly
Continuous Feedback Integration:
Automatically triage bug reports, classify issues by severity, and route them to the right team
AI can summarize similar issues and suggest fixes based on historical data
QA isn’t just about safety nets anymore. It’s your launch accelerator.
With Codiris agents, quality becomes continuous, automated, and deeply integrated across your product pipeline.
Step 5: Deployment & DevOps Automation
From commit to production, in minutes.
AI isn’t just transforming how we build software—it’s transforming how we ship it.
Gone are the days of complex DevOps setups, fragile CI/CD pipelines, and praying nothing breaks on launch day. Today, AI-first infrastructure handles deployment with speed, safety, and scale.
Frontend & Full-Stack Deployment:
Vercel – Push-to-deploy frontend hosting, global CDN, and edge functions. Performance is monitored and optimized automatically.
Railway / Render – Unified deployment for backend, frontend, and databases. AI-guided scaling, uptime, and observability built-in.
AI-Enhanced DevOps Assistants:
GitHub Copilot Workspace – Auto-generates CI/CD pipelines, manages environments, and predicts test failures before they happen.
Monitoring Tools – AI-enhanced logging, observability, and anomaly detection help you catch and fix issues before users notice.
Why This Matters:
Shipping software used to be the scariest part of the process. Now, with AI as your co-pilot, it's a strength not a stressor.
Meet Your Codiris Deployment Agents:
Pre-Deploy Agent
Checks for unresolved bugs, performance regressions, security misconfigs, and incomplete coverage before launch.
“Are we really ready to ship?” This agent ensures the answer is always yes.Deploy Agent
Orchestrates CI/CD pipelines, manages staging and production environments, and handles rollouts across infrastructure like Vercel or Railway.
Push code. Watch it ship safely.Security & Compliance Agent
Scans for vulnerabilities, manages secrets, enforces GDPR/SOC2 compliance, and flags violations early.
Peace of mind, built in.Monitoring Agent
Tracks real-time errors, latency spikes, usage trends, and user experience metrics. Can auto-trigger rollback or alerts when thresholds are breached.
It’s like having 24/7 DevOps eyes minus the pager fatigue.
Codiris turns deployment from a bottleneck into a competitive advantage.
No more waiting. No more guesswork. Just safe, smart, scalable shipping.
Build Smarter, Ship Faster, Learn Quicker
We’re entering a new era where building software isn’t just faster, it’s smarter. When AI powers every step of your product lifecycle, from idea to deployment, the game changes.
But AI alone won’t make your product successful. You still need user empathy, strong problem-definition, and relentless iteration. The difference is, you can now do all of that in days instead of months.
That’s what Codiris is built for. Whether you’re validating a new idea, building your MVP, or scaling to thousands of users, Codiris gives you the agents and infrastructure to build products people truly want.
Ready to experience it yourself?
👉 Book a live demo