How to find an AI use case worth funding
A practical test for separating interesting ideas from valuable product bets.
Read the guide →Practical perspective
Ideas for building AI that holds up.
Notes on product strategy, engineering decisions, and the human work around useful AI systems.
- Artificial Intelligence
- LLMs
- AI Agents
- RAG
- Automation
- SaaS
- Product Development
- Mobile Development
Choosing an LLM: a product team's decision framework
Balance quality, latency, cost, data policy, and model flexibility.
Read the guide →Where AI agents create value today
The right conditions for agents that do more than generate text.
Read the guide →Why a RAG system needs evaluation from day one
How to measure groundedness, completeness, and retrieval quality.
Read the guide →Designing AI automation around exceptions
The patterns that preserve trust when operational reality gets messy.
Read the guide →What changes when AI is your SaaS product
Designing the pricing, experience, data model, and quality loop together.
Read the guide →Prototype an AI product without prototyping the wrong thing
How to validate the risky assumption before making a platform investment.
Read the guide →AI on mobile: design for the moment, not the model
What changes when intelligent assistance meets the physical world.
Read the guide →How to make the case for AI adoption
An outcome-led way to align stakeholders without inflated promises.
Read the guide →Model routing is a product decision
How to use more than one model without complicating the experience.
Read the guide →Human-in-the-loop, done with intent
Design review moments that make agent systems safer and better.
Read the guide →Metadata is the hidden work of enterprise search
The information architecture that makes answers more useful.
Read the guide →What to automate before you automate everything
Find workflows with the right volume, clarity, and consequence.
Read the guide →Usage analytics for AI products that learn
Track the signals that reveal trust, usefulness, and product opportunity.
Read the guide →The AI product discovery questions that matter
A checklist for the first conversation with users and operators.
Read the guide →Building trustworthy AI interactions on small screens
Make confidence, controls, and context visible without clutter.
Read the guide →AI governance that enables delivery
Replace blanket restrictions with practical, risk-aware guardrails.
Read the guide →Fine-tuning versus retrieval: how to decide
Choose the technique that serves the behavior you actually need.
Read the guide →Multi-agent systems: when complexity pays off
Understand the use cases where specialized agents are justified.
Read the guide →Measuring the ROI of AI workflow automation
Build an evidence-based scorecard for time, risk, quality, and revenue.
Read the guide →