01
From Python Prototype to Reliable AI API
A practical guide to FastAPI, asynchronous work, stable errors, webhooks, API styles, and third-party SDK boundaries for production AI systems.
8 minPractical field guide
Field notes
12-stage implementation path
Customer-Ready AI Systems
A 12-stage field guide for taking an AI workflow from Python prototype to a secure, operable customer deployment.
- Built for
- CTOs, founders, product leaders, and customer engineering teams
- Path length
- 12 chapters · 101 minutes
02
Customer Integrations That Survive Production
Design OAuth2, SAML and SSO, SaaS API adapters, signed webhooks, and retry behavior that remain dependable after the demo.
9 minPractical field guide
03
Multi-Tenant AI Without Data Leaks
Compare tenant identifiers, row-level security, schemas, databases, and partitioning patterns for customer-facing AI systems.
8 minPractical field guide
04
Security and Compliance for Customer-Facing AI
Build a practical baseline for SOC 2 readiness, GDPR and HIPAA scoping, PII handling, encryption, and useful audit logs.
9 minPractical field guide
05
Permission-Aware RAG for Customer Data
Design secure ingestion, document-level access controls, permission filtering, citation tracking, and deletion for retrieval-augmented generation.
9 minPractical field guide
06
Repeatable AI Deployments with Docker, Kubernetes, Helm, and CI/CD
Turn an AI application into a versioned, testable deployment that can create consistent customer environments without manual heroics.
9 minPractical field guide
07
Observability for Customer-Facing AI
Connect distributed traces, customer health pages, usage metrics, service objectives, and actionable alerts across an AI workflow.
8 minPractical field guide
08
When Production AI Breaks
A disciplined approach to production debugging, log analysis, rollback, postmortems, and customer communication during AI incidents.
9 minPractical field guide
09
Documentation That Keeps AI Systems Operable
Build deployment guides, API documentation, troubleshooting playbooks, onboarding checklists, and knowledge articles that stay aligned with production.
8 minPractical field guide
10
From Business Problem to Technical Spec
Gather requirements from non-technical stakeholders and translate them into testable AI workflow specifications, tradeoffs, and expectations.
8 minPractical field guide
11
Proving AI Adoption and ROI
Measure meaningful usage, feature adoption, expansion and churn signals, and business return without confusing activity with impact.
8 minPractical field guide
12
How to Publish an AI Case Study Technical Buyers Trust
Document deployed projects, implementation decisions, integration patterns, and success metrics without inventing results or overstating evidence.
8 minPractical field guide