14+
years building & selling software
AI-Native
engineering:
the machine does
the work. I judge it.
I'm Alpesh Nakrani, VP of Growth at ViitorCloud. I help companies go AI-Native: advising on which AI bets are real, building delivery teams that ship, and teaching teams the operating model behind it. Fourteen years building and selling software, ten as CTO and COO. I also write about all of it, in books and essays.
33
books on AI-Native work
10
years as CTO & COO
AI
Native engineering & judgment
The thesis
AI-Native is not AI-assisted. The machine does the whole job; the human's role narrows to one thing: judgment. Specifying intent, evaluating output, owning the call. Everything I write follows from that single shift.
01/How I help
Work with me
01AdviseWhether your AI bets are real, and which to kill. A blunt, instrumented answer, fast.
02BuildAI-Native delivery with ViitorCloud: teams that ship, with evaluation built in from day one.03TeachCourses and workshops that turn the thesis into your team's operating model.
04SpeakTalks and working sessions on AI-Native engineering and the judgment economy.
The book · 2026
AI-Native
How the machine took the work and left us the judgment
For three years we called it AI-assisted and meant autocomplete with ambition. AI-Native is the harder claim: the model does the job, and the only thing left for us is judgment. This book draws the line precisely: where the machine is now doing real work, where it is not, and what an organization has to rebuild once the bottleneck moves from production to evaluation.
02/From the library
Selected books
2026 · 107pp · PDF
AI-Native
Free PDF with email signup
2026 · 78pp · PDF
The Judgment Economy
Free PDF with email signup
2026 · 60pp · PDF
A Field Guide to Evals
Free PDF with email signup
2026 · 109pp · PDF
Agents That Actually Work
Free PDF with email signup
2025 · 92pp · PDF
The Economics of Inference
Free PDF with email signup
03/Coverage
What I write about
5 titlesAI-NativeWhen the machine does the work and the human evaluates.5 titlesEvalsMeasuring what will actually break in production.3 titlesRAGRetrieval that survives a growing corpus.3 titlesAgentsThe narrow band where autonomy earns its keep.4 titlesInferenceThe economics of shipping models you can run.4 titlesLeadershipTeams and org design after automation.
04/Blog
Latest writing
Blog · 11 min
How to Hire a Data Scientist Who Ships Models That Reach Production
Most data science hires stall between a promising Jupyter notebook and a live business decision. Here is how to scope, interview, and run a one-week trial for a Data Scientist who delivers measurable lift.
HiringEvals
PoV · 11 min
What Is Model Context Protocol (MCP)? The Enterprise Architecture Guide
MCP replaces brittle N-by-M tool connectors with a typed JSON-RPC protocol, provided you design the boundary for agents rather than human REST clients.
AgentsEngineering
Blog · 10 min
Data Scientist vs. Data Analyst vs. ML Engineer: The Hiring Boundary Matrix
Using 'Data Scientist' as a catch-all title is the fastest way to mis-scope a hire. Here is the exact ownership boundary between a Data Analyst, Data Engineer, Data Scientist, MLOps Engineer, and LLM Engineer.
HiringEngineering
05/Praise
