Feedback on an OpenAI-API based SMB automation project (architecture + minimal coding roadmap) Hi everyone — I’m building a small, real project using the OpenAI API and would like technical feedback on architecture and best practices

ChatGPT Use cases and examples
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- I run a small service business (leads → quote → invoice → follow-up).

- Goal: build a working “lead-to-quote assistant” using OpenAI API + automation tools (n8n/Make) without heavy coding.

Planned MVP (v1):

  1. Intake: web form / email → webhook into n8n

  2. LLM step: extract structured fields (job type, address area, photos? timing, constraints) into JSON

  3. Rules/pricing: apply a simple rules engine (tables) to produce an estimate range + next questions

  4. Output: send reply + create a CRM record + schedule follow-up

  5. Safety: avoid hallucinated prices/claims; always show assumptions

Questions:

  1. Which OpenAI approach is best for reliable structured extraction: strict JSON schema / structured outputs / tool calling? Any pitfalls?

  2. How would you design guardrails so the assistant never “invents” pricing or terms (e.g., validations, confidence thresholds, fallback flows)?

  3. What’s the simplest evaluation setup for this (golden set + regression tests)? Any recommended patterns/tools?

  4. For cost control: what are common strategies (caching, smaller models for extraction, prompt compression, batching)?

  5. For maintainability: do you recommend starting vendor-neutral (n8n + HTTP) or going deeper into one ecosystem early?

If helpful, I can share a sample intake message and my current JSON schema.

Thanks — looking for practical, experience-based guidance.