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Building AI Products That Actually Work

November 15, 2024
Ahmad Mohamed Yoosuf
AI
10 min read

After building OCRPro and A111y, I've learned that successful AI products aren't about using the latest models or the most complex architectures. They're about solving real problems in ways that existing solutions don't.

The Problem with Most AI Products

The AI landscape is littered with products that are technically impressive but practically useless. They demonstrate capabilities without addressing actual user needs, or they solve problems that don't really exist. This happens when teams focus on the technology rather than the problem.

OCRPro: A Case Study in Practical AI

When I started building OCRPro, the market already had established players like AWS Textract, Azure OCR, and Google Cloud OCR. The question wasn't whether we could build an OCR engine. It was whether we could build one that was meaningfully better.

Finding the Gap

Through extensive testing, I discovered that existing solutions had three main issues:

  • Cost: Enterprise OCR solutions were prohibitively expensive for many use cases

  • Speed: Processing times were often too slow for real-time applications

  • Accuracy on edge cases: Performance degraded significantly on non-standard documents

The Solution

OCRPro addresses these issues through:

  • A hybrid architecture that balances accuracy and speed

  • Specialized models for different document types

  • Aggressive optimization for common use cases

  • A pricing model that scales with actual usage, not enterprise contracts

The result? An OCR engine that outperforms industry leaders on the metrics that actually matter to users.

A11y: Making Accessibility Accessible

A11y emerged from a different observation: while everyone agrees that web accessibility is important, most developers find accessibility testing tools too complex or too removed from their workflow.

The Traditional Approach

Most accessibility tools either:

  • Require extensive manual testing

  • Generate overwhelming reports with hundreds of issues

  • Focus on compliance rather than actual user experience

Our Approach

A11y takes a different path:

  • AI-powered analysis: Understands context, not just rules

  • Prioritized recommendations: Shows what matters most

  • Developer-friendly: Integrates into existing workflows

  • Focus on impact: Measures real accessibility improvements

Lessons Learned

1. Start with the Problem, Not the Technology

Both OCRPro and A11y succeeded because they started with clear problem statements. The AI technology was a means to solve these problems, not the end goal.

2. Benchmark Against Real-World Usage

Academic benchmarks are useful, but real-world performance is what matters. OCRPro's edge came from optimizing for actual documents users process, not standardized test sets.

3. Make It 10x Better, Not 10% Better

To compete with established solutions, marginal improvements aren't enough. You need to be dramatically better on at least one dimension that users care about.

4. Developer Experience Is User Experience

For developer tools like A11y, the API design, documentation, and integration process are as important as the core functionality.

5. Measure What Matters

Success metrics should align with user value. For OCRPro, it's not just accuracy. It's accuracy per dollar and accuracy per second. For A11y, it's not just finding issues. It's helping developers fix them.

The Future of Practical AI

As AI capabilities continue to expand, the opportunity isn't in building more powerful models. It's in applying existing capabilities to solve real problems better than current solutions.

The next generation of successful AI products will be those that:

  • Solve specific, well-defined problems

  • Integrate seamlessly into existing workflows

  • Provide clear, measurable value

  • Focus on user outcomes, not technical metrics

Conclusion

Building AI products that actually work isn't about having the most advanced technology. It's about understanding problems deeply and applying technology thoughtfully. OCRPro and A11y succeed not because they use cutting-edge AI, but because they use AI to cut through real-world problems.

The best AI products are often the ones where users don't even think about the AI. They just appreciate that their problem is solved better than before.