FORWARD-DEPLOYED AI SYSTEMS
I turn open-weight and proprietary models into production systems that enterprises can operate, trust and adopt.
01 / FIELD
Quality, latency, throughput, privacy, cost, integration debt and safe failure decide whether model capability becomes useful infrastructure. I work across that boundary—from discovery and architecture to deployment and adoption.
02 / PRACTICE
At mavQ, I architect and lead intelligent document processing and enterprise automation platforms, translating complex workflows into dependable products.
Deployment systems for open-weight and proprietary models across sovereign, air-gapped, on-prem and multi-cloud environments.
My engineering roots are in accessibility and multilingual systems built for constrained connectivity and validated with the people they serve.
03 / RESEARCH
IEEE/ACM published researcher · W3C AI & Accessibility panelist · ORCID record ↗
04 / FIELD NOTES
Notes on production AI, engineering decisions, product strategy and the forces shaping how technology is built and adopted in India and beyond.
05 SEP 2026 · 6 MINThe deployment envelope comes before model selectionWhy the most consequential AI decision is often made before a benchmark enters the room. →All writing →05 / OPERATING PRINCIPLES
Start with the operating constraint. Model choice follows the workflow, deployment envelope and failure cost.
Measure what users experience. Benchmarks matter; task success, latency, abstention and adoption matter more.
Keep optionality. Open weights create control. Proprietary models can create leverage. Make the tradeoff explicit.
Own the last mile. Deployment is complete when the workflow holds up in use—not when the endpoint responds.
06 / CONNECT
I’m interested in sovereign AI, air-gapped deployments, open-weight serving, agent reliability, document intelligence and the path from capability to adoption.