The Unvarnished Truth: What I Wish I Knew Building 50+ AI Products
Since 2021, our team at Autor has shipped over 50 AI products across healthcare, fintech, logistics, and SaaS. Some of them are running in production right now, handling thousands of automated calls per month. Others failed spectacularly — and those are the ones that taught us the most.
I started Autor in Toronto as a one-person AI development shop. The original thesis was simple: companies needed custom AI but couldn't hire fast enough to build it themselves. Four years and 50+ products later, we're a senior-only studio with a production voice AI platform (Loquent) serving healthcare and dental clients 24/7. Along the way, we've impacted over 5 million users, helped clients raise more than $10 million in funding, and shipped across 10+ countries.
This isn't a highlight reel. This is the unvarnished list of things I got wrong, figured out the hard way, or wish someone had told me before I wrote my first line of production AI code.
Focus on a Niche, Not a Broad AI Capability
The biggest mistake early on was trying to be a generalist AI shop. We wanted to do everything: NLP, computer vision, predictive analytics, generative AI. The reality is that each of these domains requires deep specialization, and customers rarely need a jack-of-all-trades. They have a specific problem they need solved. Trying to serve too many masters meant we were never the best at anything. We learned that drilling down into a specific vertical, like healthcare AI or logistics optimization, allows you to build deep expertise, understand customer pain points intimately, and develop reusable components. This focus also makes marketing and sales infinitely easier. Instead of saying "we do AI," we can say "we solve patient scheduling issues with AI."
The 'Production-Ready' Trap: Underestimating Deployment and Maintenance
Many AI projects, especially in the early days, are treated like academic exercises. The focus is on building a model that performs well on a test set. But moving from a Jupyter notebook to a production environment is a chasm. We underestimated the infrastructure, monitoring, and maintenance required. This means not just deploying the model, but building robust APIs, ensuring scalability, handling data drift, implementing retraining pipelines, and setting up comprehensive logging and alerting. A model that works in development but fails silently in production is worse than useless; it erodes trust. Think of it less like building a prototype car and more like building a commercial airline: reliability, safety, and continuous maintenance are paramount. We spent far too much time on the "wow" factor of the model and not enough on the "will it run tomorrow?" factor.
Data Quality is King, and 'Garbage In, Garbage Out' is an Understatement
This is a cliché for a reason. The performance of any AI model is directly tied to the quality and quantity of the data it's trained on. We learned that data acquisition, cleaning, and labeling are often the most time-consuming and expensive parts of an AI project. Blindly trusting provided datasets or rushing through the cleaning process leads to models that are biased, inaccurate, or simply don't generalize. For instance, building an automated customer service bot without diverse enough training data on accents or regional dialects will lead to frustrating user experiences. We had to develop rigorous data validation pipelines and invest heavily in data annotation tools and processes. Often, the bottleneck wasn't the model architecture, but the data itself. If you're not obsessing over your data, you're setting yourself up for failure.
The Human Element: AI Augmentation, Not Replacement
One of the most significant shifts in our thinking was around the role of AI. Initially, there was a strong push to build AI that could fully replace human tasks. While this is sometimes possible, we found far greater success with AI that *augments* human capabilities. Think of AI as a co-pilot. For example, instead of an AI that completely handles customer support tickets, we built AI that triages tickets, suggests responses to human agents, and summarizes customer issues. This approach often leads to faster adoption, higher user satisfaction, and more practical business outcomes. It leverages the strengths of both humans (judgment, empathy, complex problem-solving) and AI (speed, pattern recognition, data processing). Developers need to design AI systems with human-in-the-loop workflows from the start, rather than as an afterthought.
Build vs. Buy: Leveraging Off-the-Shelf APIs Wisely
The AI landscape is evolving at breakneck speed, with incredible APIs and pre-trained models becoming available constantly. Early on, we felt compelled to build everything from scratch to demonstrate our AI prowess. This was a mistake. For many common tasks – sentiment analysis, basic image recognition, text summarization – leveraging established APIs from providers like OpenAI, Google AI, or Azure AI is far more efficient. It allows us to focus our custom development efforts on the unique, high-value aspects of a client's problem that off-the-shelf solutions can't address. The key is to understand where the true business value lies. If a task can be solved with a few API calls and some clever prompt engineering, don't reinvent the wheel. This principle frees up engineering resources and accelerates time-to-market significantly.
Long-Term Vision: Beyond the MVP
Many AI projects falter because there's no clear vision for what happens after the Minimum Viable Product (MVP). Building a functional AI feature is one thing; scaling it into a sustainable product or platform is another. We learned the importance of architectural planning from day one. This means considering how the system will evolve, how new data will be incorporated, how performance will be monitored over time, and how the business model will sustain ongoing development and maintenance. It's easy to get caught up in the excitement of a new AI model, but without a roadmap for its long-term integration and evolution, even the most impressive MVP can become technical debt. What nobody has addressed yet is how to properly budget and staff for the ongoing, often unpredictable, operational costs of production AI systems, which can dwarf initial development expenses.