System thinking
How I connect data, AI, and product.
Software that survives daily operations is rarely one layer. It is a chain of decisions — what enters the system, how it is validated, where it lives, and how a human actually uses it under pressure.
Data systems
Government data has to become desk-ready.
When I built the trademark register inside NepalIPMS, the raw source was not a clean API. It was Department of Industry bulletins, legacy spreadsheets, and partial exports that firms had accumulated over years. The system’s job was not to mirror government files — it was to make 130,000+ records searchable while a paralegal is on the phone.
I think about data as a pipeline with explicit gates: import, validate, persist, index, expose. Each gate exists because real desks fail at handoffs. A row that slips through validation becomes a renewal missed. A search index built on unnormalized marks returns false confidence. Notifications only matter if the underlying record is trustworthy.
AI systems
Intelligence belongs in the loop, not in the way.
Document intelligence in legal practice is not about replacing judgment. It is about removing the repetitive translation between a government PDF and a matter record. OCR demos make this look solved. Production taught me otherwise: bulletin layouts shift, bilingual text breaks tokenizers, and confidence scores mean nothing unless a practitioner can correct them quickly.
My AI systems always end at the same place — search and reminders tied to verified records. Click each stage for technology, reason, and tradeoff.
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- Technology
- Reason
- Tradeoff
Product systems
Architecture follows the problem, not the stack.
Product thinking, for me, starts before technology. What breaks on Monday morning? What does the firm do when a client calls about a mark they filed three years ago? Those questions shape workflow design long before I choose Workers, D1, or a frontend framework.
Deployment and iteration close the loop. Shipping on Cloudflare’s edge is a product decision — low latency for Kathmandu teams, operational simplicity, infrastructure that does not require a dedicated DevOps hire. Iteration means watching production queries, sitting with practitioners after launch, and adjusting the model when the desk tells you the abstraction was wrong.
Across ScholarQuest, Global Law Associates, TopRank, and Yogshala, the pattern repeats: understand the domain, design the workflow, architect for reliability, deploy, learn. The stack changes; the sequence does not.