Field notes from the work: product strategy, AI systems, concept development, prototypes, architecture, launch decisions, and the lessons that only appear when ideas meet production constraints.
AI products need sequencing without bureaucracy: define the product thesis, data shape, evals, and failure modes up front — then iterate inside those guardrails.
Pure agile can become churn when the model, product, data, and UX are all moving at once. Interactive waterfall gives AI work the structure it needs without freezing discovery: decide the irreversible things early, then iterate aggressively inside the frame.
AI product management is less about feature inventory and more about judgment under uncertainty: defining promises, boundaries, evaluation criteria, and the human path when the model should not act.
The cheapest AI build is the one you do not start until the concept is sharp. Model choice comes after the product promise, user behavior, data advantage, and business case are clear.
What you get when your data model is treated as the product, not as scaffolding: feature velocity that compounds, multi-tenant safety from day one, predictable migrations instead of panic refactors, and the ability to ship for years without a rewrite.
Voice AI promises hands-free productivity for mobile workforces. Most voice products ship a system that perfectly hears the model say something wrong. The difference between trustworthy and not is in the prompt and the tool schema, not in the audio.
Every team has a budget for things that might not work. Spend it on a new database, a new framework, a new orchestrator — and there's nothing left for the AI surface, which is the part the customer remembers.
B2B SaaS lives or dies on customer trust. The architecture that earns trust is one that puts tenant enforcement in the database itself — not in application code that has to be perfect every time. Here's what your team needs to know about RLS to ship with confidence.
The default agent UX puts your team in the wrong role: slow editor for a fast generator. Verification gates flip the relationship — agents self-assess, gates catch the weak runs, and operators do the high-judgment work AI can't.
Single-provider AI is single point of failure. When the provider goes down, your product goes down. Multi-provider routing is the small architectural decision that turns the LLM layer into infrastructure your team can rely on.
AI traffic is the most valuable corpus your product produces — debugging trace, eval set, fine-tuning corpus, and quality observability layer all in one. Most teams treat it as transient and pay for it later. Here's what changes when you treat it as the asset it is.
The bottleneck in AI development is rarely raw capacity. It is translation loss: product intent becoming specs, specs becoming tickets, tickets becoming code, and model behavior drifting away from the original user promise.
AI products fail differently than software. Quietly. Plausibly. Often confidently. The first time the system surfaces a wrong answer with no graceful path, the user's trust collapses and doesn't recover. The bad-day path is a feature, not error handling.