Ankit Garla
Applied AI systems, institutional memory, and evidence.
I build applied AI systems for environments where trust, memory, evidence, and execution matter. The work sits at the intersection of AI, institutional knowledge, enterprise workflows, and organizational judgment.
Institutional Intelligence is where I turn that work into public field notes: mechanisms, patterns, and decision rules for making AI useful after the demo is over.
Most AI writing stops at capability. I care about custody: what the system knows, what it used, what it missed, who should review it, and what action it can responsibly support.
Elsewhere: LinkedIn and ankitgarla.dev.
What I Write About
- Evidence-backed AI systems
- Institutional memory and organizational knowledge
- Provenance, confidence, review, and auditability
- Knowledge graphs, retrieval, and decision workflows
- Governance that makes systems faster, not slower
- The operating discipline behind useful enterprise AI
The Standard
I do love the drama of AI. The strange jumps in capability, the taste debates, the speed of it, the feeling that the ground is moving under everyone at once.
But enterprise data forces discipline. Once systems touch real institutional knowledge, the useful work becomes more specific: source discipline, confidence, review, ownership, and the path from answer to action.
The bar is simple: if a system cannot explain its evidence, it has not earned operational trust.
Boundary
This publication is independent of my day-to-day work. I do not publish private company, client, financial, legal, HR, lease, or employee details. I generalize the field lessons so the ideas can travel without exposing the work behind them.
Start Here
Start with the thesis for this publication: