16 posts
Your AI feature ships to users every hour and you have almost no visibility into what it is actually doing. That is the default state of most SaaS teams in 2026. Here is the fractional CTO's playbook for tracing, evals, and cost telemetry — the third leg of the production AI infrastructure stool, alongside gateways and agent identity.
Read →Your next ten B2B buyers will ask ChatGPT before they Google anything. If your SaaS is not showing up in AI answers, you have a discoverability problem that a marketing agency cannot fix alone. Here is the fractional CTO's playbook for engineering your site to be cited by ChatGPT, Claude, and Perplexity — without buying another year of SEO theater.
Read →Your AI agents are logging in as humans with shared API keys and production-level scope. That is the confused deputy problem with venture funding behind it. Here is the fractional CTO's playbook for giving agents real identities and scoped delegation — before your first incident.
Read →Calling OpenAI directly from your app was fine in 2023. In 2026 it is a liability — cost, reliability, governance, and vendor lock-in all suffer. Here is the fractional CTO's playbook for adopting an AI gateway without over-engineering it.
Read →Single agents hit a ceiling fast. Multi-agent systems are what comes next — and they bring orchestration, governance, and architectural challenges most startups are not ready for. A fractional CTO's field guide.
Read →A practical guide to building retrieval-augmented generation features that actually work — chunking, embeddings, retrieval, re-ranking, evals, and the mistakes I see in almost every first attempt.
Read →The single piece of infrastructure that separates AI features that improve from AI features that drift — what a real eval suite looks like, how to build one, and what to measure.
Read →Why the Model Context Protocol matters for SaaS in 2026, how to expose your product to agents safely, and the specific implementation steps for a seed-stage team.
Read →Why LLM feature costs quietly explode as you scale, the specific decisions that drive the curve, and the handful of optimizations that reliably cut your bill by 50% or more.
Read →The shift from clever prompts to context assembly pipelines — why the 'prompt engineer' role is dissolving, and what replaced it.
Read →A practical decision framework for picking the right LLM for each feature — by cost, latency, quality, privacy, and lock-in — without turning it into a religious debate.
Read →After two years of 'just prompt it,' fine-tuning is quietly winning back ground. Here's when distillation, LoRA, and small specialized models actually beat calling a frontier API.
Read →The architectural patterns that define AI-native SaaS in 2026 — vector stores, semantic caches, tool-use orchestrators, eval infra, and the stack decisions founders get wrong.
Read →A decision framework for founders trying to figure out whether to build, buy, or wrap when adding AI features to a product — with the specific questions that tell you which path fits.
Read →A practical safety baseline for AI features at seed-stage startups — injection defense, PII protection, red-teaming, and audit trails you can implement in a single sprint.
Read →The shift from copilots to agents is restructuring how SaaS gets built, priced, and defended. A fractional CTO's honest read on what that means for founders right now.
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