The Demo-to-Production Gap Is Real
Here's something we've seen play out at least 20 times in the last two years: a client comes to us excited about an AI proof-of-concept their internal team built. The demo looks fantastic. The CEO is hyped. And then... nothing. The project sits in staging for months, eventually dying a quiet death.
We've started calling this the "AI staging graveyard." And honestly, after shipping about 30 AI features into production across different clients, we think we understand why it happens. It's almost never about the model. It's about everything around the model.
The Infrastructure Nobody Budgets For
When our team builds an AI feature, the actual model or API call is maybe 15% of the work. The other 85% is: input validation, output guardrails, fallback paths when the model returns garbage, logging for debugging, cost monitoring, latency optimization, and user feedback loops. Most teams budget for the 15% and then act surprised when the remaining 85% takes four months.
We had a client last year — a mid-size logistics company in Pune — who built a pretty solid demand forecasting model using Prophet and some custom LSTM layers. Great accuracy on test data. But they had zero infrastructure for retraining, no monitoring for data drift, and their inference pipeline was a Jupyter notebook running on someone's laptop via a cron job. That's not production. That's a science experiment.
The Evaluation Problem
This one drives us crazy. How do you know your AI feature is actually working in production? With traditional software, you have unit tests, integration tests, and clear pass/fail criteria. With AI, it's fuzzy. We've learned the hard way that you need to define evaluation metrics before writing a single line of model code. For one client, we spent three weeks just building the eval harness before touching the actual LLM integration. Their previous vendor had skipped this step entirely, which is why they were on their third attempt.
We use a combination of automated evals (think: LLM-as-judge for text quality, cosine similarity thresholds for retrieval) and human review queues. It's tedious. It's expensive. And it's the only thing that works reliably.
Cost Estimation Is Broken
Every AI project proposal we've reviewed from other vendors underestimates costs by 3-5x. They quote the API cost for GPT-4o at $5 per 1M input tokens and call it a day. They don't account for retries, chain-of-thought prompting that 4x's your token usage, embedding costs for RAG, vector database hosting, or the GPU costs if you're fine-tuning. We had one project where the client's monthly OpenAI bill went from the estimated $200 to $4,700 because nobody modeled the actual traffic patterns.
What Actually Gets Projects to Production
After all these projects, our playbook is pretty simple. First, start with the smallest possible AI feature — not a platform, not an "AI layer," just one feature. Second, build the monitoring and eval infrastructure in sprint one, not sprint ten. Third, always have a non-AI fallback path. If the model is down or returning nonsense, the user should still be able to complete their task. Fourth, budget 3x what you think you'll need, both in time and money. And fifth, get real users testing within two weeks, not two months.
The teams that ship AI to production aren't the ones with the best models. They're the ones with the best engineering discipline around everything except the model.