AI Automation Pipelines for Real Businesses
January 28, 2025
AI that ships in production isn't about the best model—it's about infrastructure. You need pipelines that pull the right data, call LLMs in a controlled way, validate outputs, and trigger the next step. Without that, you get demos that never scale.
Here's how to think about AI automation that actually runs your business.
1. Start with the workflow, not the model
Map the human or system workflow first. Where does data come from? What decision or output is needed? What system gets updated? AI is one step in that pipeline. Design the pipeline, then plug in the model.
2. RAG and context are infrastructure
If the LLM needs your data (docs, CRM, tickets), you need retrieval and context assembly. That means vector stores, embedding pipelines, and a clear contract for "what context does this step get?" Build that like you'd build any real-time data pipeline—reliable and observable.
3. Validate and fallback
LLM output is non-deterministic. Validate structured outputs (e.g. with Zod or JSON Schema) and have fallbacks: retry, default action, or human-in-the-loop. Don't let bad output write to your database.
4. Cost and latency at scale
Token cost and latency add up. Cache embeddings and responses where possible. Use smaller/faster models for simple steps and reserve heavy models for complex ones. Monitor usage per workflow.
5. Observability
Log inputs, outputs, and failures. Trace a request through the pipeline so you can debug when something goes wrong. This is the same discipline as reliable webhook processing—just with an LLM in the middle.
AI without infrastructure is useless. If you're building AI-powered automation and want it to run reliably, request an architecture review to align your design with production reality.