Every company wants AI in their product. Most have no idea where to start. Here is the practical guide we wish existed when we started building AI integrations.
Start With the Problem, Not the Technology
The first question is not "how do we use GPT?", it is "what manual process costs us the most time and money?" AI is a tool for automation, not a feature to list on your marketing site.
Good AI use cases: customer support triage, document processing, content generation, data extraction from unstructured sources, personalized recommendations, and quality inspection.
Bad AI use cases: anything that needs 100% accuracy (legal decisions, medical diagnoses without human review), problems with tiny datasets, and tasks that are faster to do manually.
The Integration Stack
A production AI integration has more layers than just an API call:
Prompt Layer: Template management, variable injection, version control for prompts. Your prompts will change constantly, treat them like code.
Orchestration Layer: Chain multiple AI calls together. Example: extract data → validate → summarize → decide. Each step might use a different model or provider.
Reliability Layer: Retry logic, fallback models (if OpenAI is down, try Anthropic), timeout handling, and graceful degradation (show a manual fallback if AI fails).
Cost Layer: Token tracking, budget alerts, caching repeated queries, and model selection based on complexity (use a cheap model for simple tasks, expensive model for hard ones).
Evaluation Layer: How do you know if your AI is getting better or worse? Automated evaluation with test datasets, human review pipelines, and A/B testing.
Common Mistakes
Mistake 1: No fallback. Your AI integration will fail. APIs go down, models hallucinate, rate limits hit. Build the manual path first, then automate it with AI.
Mistake 2: Ignoring costs. GPT-4 at scale is expensive. A chatbot handling 10K conversations/day at $0.03/conversation is $300/day or $9K/month. Our detailed cost breakdown covers the real numbers. Know your unit economics before you ship.
Mistake 3: Over prompting. Long, complex prompts are fragile. Break complex tasks into simple steps. Each step should have a clear input and output. Easier to debug, easier to improve.
Mistake 4: No evaluation. "It seems to work" is not a testing strategy. Build a dataset of 100+ examples with expected outputs. Run it automatically on every prompt change. Track accuracy over time.
When to Build vs Buy
Build when: AI is your core product differentiator, you need custom models or fine tuning, you have specific data privacy requirements, or off the shelf tools do not fit your workflow.
Buy when: the use case is generic (chatbot, email writing, image generation), you need it yesterday, or you are testing whether AI adds value before investing in custom development.
Getting Started
1. Identify your highest cost manual process
2. Prototype with an API call (OpenAI or Anthropic)
3. Measure: does it actually save time/money? Is the quality acceptable?
4. If yes, build the production infrastructure (reliability, cost management, evaluation)
5. If no, try a different use case
We help companies at every stage of this process, and we only take on a few projects at a time. Talk to our team →