More and more companies want to adopt AI. The impact of ChatGPT was impossible to ignore, and "can we do something with AI?" has become a routine question.
But when we step into the actual workplace, the picture is different.
The gap between PoC and production
Most AI projects sail through PoC. The demo gets applause. Leadership signs off. Then nothing happens.
Common stall patterns:
- Demo works, but accuracy drops on real data
- Response time is too slow for actual business workflows
- Costs turn out to be 5x the estimate
- Security review reveals on-premise requirements nobody planned for
- No one is assigned to operate the system after launch
This is not a technology problem. It is a design problem. PoC and production systems have fundamentally different requirements.
Design principles for production AI
When we start a project, we design for production from the PoC stage.
1. Define accuracy first
What does "good accuracy" mean? If you leave this vague, you build a PoC with no evaluation criteria and no way to decide if it worked.
We define success quantitatively at project start. For example: answer accuracy above 95%, inappropriate response rate below 0.5%, response time under 3 seconds.
These numbers become an automated Eval pipeline.
2. Build the cost model early
LLM API costs vary wildly depending on usage patterns. A query you budgeted at $0.01 can easily hit $0.30 with long context and complex prompts.
We estimate monthly costs based on real production use cases before choosing the technology. If the numbers don't work, we consider smaller models, caching strategies, or batch processing.
3. Design operations
AI systems cannot be "build and forget." Model performance degrades over time. Input data patterns shift. User expectations rise.
Before production launch, we prepare:
- Accuracy monitoring dashboard
- Alert and human-review flow for inappropriate responses
- Regression test procedures for model updates
- Cost trend visualization
How we choose technology
"Just use the latest model" is a misconception.
The right choice depends on the problem. RAG architecture for internal document search. Agent design for routine task automation. On-premise fine-tuning for environments with strict security requirements.
We combine technologies based on what needs to be solved. Being cutting-edge is not the goal. Solving the client's problem reliably at minimum cost is.
Finally
AI is not magic. But designed correctly, it reliably transforms business.
If you have a project stuck at PoC, or you are considering AI adoption for the first time, we can help, starting from "what should we do first?"