How to Identify High-Value AI Opportunities Inside Your Organisation
A practical framework for leadership teams to find the AI opportunities that create measurable business value—before choosing any technology.
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Field notes from building AI in production.
Practical writing on AI strategy, data readiness, prototype-to-production engineering and the unglamorous work of operating AI systems that earn their keep. Written by Vikram Katyani, founder.
AI Strategy
Prototype to Production
Data Readiness
Engineering
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Latest articles
Built an AI App in Lovable? Here's What It Really Takes to Run It in Production
Building AI has never been easier. Running it every day has never been harder. A practical guide to taking an AI prototype into a secure, monitored, production-ready business system.
Why Every Organisation Needs an AI Readiness Assessment Before Building AI
Most AI projects don’t fail because of technology. They fail because organisations start building before they’re ready. Why an AI Readiness Assessment matters.
AI Applications Aren’t “Build Once, Run Forever”
Why the most successful AI systems are continuously monitored, maintained and improved—not simply deployed and forgotten. A cornerstone guide to running AI as a living business capability.
Before You Spend a Penny on AI, Answer These Two Questions
Most AI investments fail not because the technology is poor, but because the organisation isn't ready. Two questions prevent months of wasted budget before any vendor is engaged.
From Prototype to Production: Why Most AI Projects Stall After the Demo
Businesses now build working AI tools in days using Lovable, Cursor and Bolt. The harder question is hosting, security, monitoring, backups and long-term support.
Why I build for usability, not scope
Scope makes a product look impressive in a demo. Usability is what makes people actually use it on a Tuesday morning. The teams that win optimise for the second.
If you didn't design the human-in-the-loop, you didn't design the system
Most 'human-in-the-loop' AI is really human-on-the-side. Designing the loop properly is what separates AI that ships from AI that quietly gets switched off.
AI that assists beats AI that replaces
The strongest AI systems do not remove people from the process. They help good people move faster, decide better, and spend less time on repetitive work.
Most AI strategies fail at data readiness
Integration is the easy part. Real organisational data is where AI demos break. AI does not fix messy data — it amplifies it.
When expertise meets AI: lessons from a multi-agent system
Even the smartest agents fail without the right context. The success of our multi-agent system came from the precision of the domain knowledge built into every step.
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