Pricing the service: subscription, per-resolution, outcome-based
Subscription, per-resolution, outcome-based. The pricing decision tree, the cost-of-goods math, and the free-tier question, closer for the operate series.
Small Business
For small teams and small organizations — frugal stacks, team-of-one governance, small-shop AI
The customer view vs the consultant view: two surfaces, one product
One backend, two surfaces. Customer asks and gets an answer. Consultant supervises the queue, approves, denies, and mines for patterns.
Onboarding new tenants: the five-minute path from signup to working AI
When a consultant signs up, how do they get from 'I have secret sauce' to a live AI surface in five minutes? Onboarding as a first-class feature.
Bedrock model selection: pick on evidence, not vibes
Sonnet, Haiku, Opus, Llama. Picking the right Bedrock model per use case using evals, not gut feel, and knowing when to switch.
What I'd cut, what I'd keep: the actual MVP cutline
Closing the AI MVP series. What you can safely skip on day one, what you absolutely can't, and what the first 30 days of customers will teach you that nothing else can.
The hybrid sync pattern: how cloud and local actually talk
The wiring between a cloud AI product and a local Mac Studio. SQS for events, S3 for artifacts, EventBridge for schedules, signed manifests for the round-trip.
Deployment: IaC, CI/CD, environments, the minimum shape
The smallest setup that lets you ship an AI MVP without breaking things. CDK, GitHub Actions, three environments, migrations and secrets handled honestly.
The cost model: what you pay before you have customers
Runway math for an AI MVP with zero customers. What the AWS free tier actually covers, what bites you, and when local pays back.
Failure modes: graceful degradation when something's down
Bedrock rate-limited, the Mac Studio offline, the customer asking something the AI can't handle, the graceful degradation patterns that fall back to human-only without the customer noticing.
Observability and audit, not later
CloudWatch, structured logs, a real audit table, and trace IDs that follow a request through every Lambda hop and every back-office Mac Studio job. Day one, not a thing you bolt on later.
The approve/deny gate: and when it goes away
Day-one, every AI suggestion goes through a human approval. Then you mine the approvals, find the safe classes, let those auto-resolve, and keep the audit trail through the whole transition.
The eval harness: how you know it's working before customers tell you
Test sets, golden examples, regression detection. The eval harness is how you find out the AI broke before a customer does, and it has to be a permanent part of the stack, not a side project.