Building AI for Enterprise

What we've learned deploying AI systems that actually work in production environments.

Enterprise AI is a different beast than consumer AI. The stakes are higher, the data is messier, and the tolerance for hallucination is near zero.

The Reality of Production AI

Most AI demos look great. Most AI deployments don't. The gap between a compelling prototype and a reliable production system is where most projects fail.

We've spent the last year closing that gap for our clients. Here's what we've learned.

Data Quality Trumps Model Size

The instinct is always to reach for the biggest model available. But we've consistently found that a well-tuned smaller model on clean, domain-specific data outperforms a frontier model on messy inputs.

This means investing in:

  • Data pipelines that validate and clean inputs before they hit the model
  • Domain-specific evaluation rather than generic benchmarks
  • Continuous monitoring of input drift and output quality

Latency Matters More Than You Think

A 3-second response time might be acceptable for a chatbot. It's not acceptable for an API that sits in the critical path of a business workflow. We've found that inference optimization — quantization, batching, caching — often delivers more business value than model improvements.

# Simple response caching pattern
from functools import lru_cache
import hashlib

def cache_key(prompt: str, model: str) -> str:
    return hashlib.sha256(f"{model}:{prompt}".encode()).hexdigest()

@lru_cache(maxsize=10000)
def cached_inference(key: str) -> str:
    return run_model(key)

Build for Failure

Every external API call will eventually fail. Every model will eventually hallucinate. The question is what happens when it does. We build systems with:

  1. Graceful degradation paths
  2. Human-in-the-loop escalation
  3. Automated quality checks on outputs
  4. Clear audit trails for every decision

What's Next

We're seeing the industry mature rapidly. The companies that win won't be the ones with the fanciest models — they'll be the ones with the most reliable systems.