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Our Thinking

Notes from the build

Practical takes on software development, AI integration, and shipping products that hold up.

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Product 2026-08-30

The Health-Tech Agency Bake-Off: 9 Vendor Questions a Diagnostics Founder Asked Us, and the 4 We'd Answer Differently Six Years On

The real vendor-evaluation questions GlycanAge put to us in 2019 before hiring us to build their lab and customer systems, the answers we gave, and the four answers six years of regulated diagnostics work proved naive.

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AI 2026-08-26

Retrofitting AI Into a Product You Didn't Build: The Six-Point Codebase Readiness Score We Run Before Quoting

The six-axis engineering audit Jaspero runs on an existing codebase before quoting an AI integration: data access path, write-path idempotency, latency headroom, PII boundary, observability, and deploy frequency, with real scores from four production products.

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AI 2026-08-25

The AI Integration Invoice: What Six Retrofits Actually Cost, Line by Line

A line-by-line breakdown of what retrofitting AI into live products actually costs: discovery versus implementation hours, cost per 1,000 calls, eval and fallback budget share, and the two engagements where we wrote off time.

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Product 2026-08-24

The Non-Technical Founder's Escalation Ladder: 7 Decisions You Own, 23 We Take Off Your Desk

A written escalation contract that splits technical decision authority between a non-technical founder and their engineering team, sorted by reversibility, spend, compliance exposure, and customer visibility.

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Product 2026-08-23

The Full-Time CTO Job Description We Talked Three Founders Out Of: A Line-by-Line Duty Audit

Three founders came to us with a drafted full-time CTO posting. We scored each responsibility bullet by real hours consumed, whether it needed continuity or judgement, and which ones a fractional arrangement covers. Plus the two roles we told them to hire full-time instead.

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AI 2026-08-22

Shipping AI Features Without an ML Engineer: The Four Ownership Roles We Assign Instead

None of our three clients running AI features had an ML hire. Here is how we split model behaviour into four named roles (Prompt Owner, Eval Owner, Cost Owner, Fallback Owner) and handed them to existing product and backend staff.

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