Strategy & architecture
I help translate enterprise priorities, risk and operating constraints into AI platform and product decisions that teams can execute.
- Build, buy & partner decisions
- System boundaries & roadmaps
- Technical due diligence
TECHNICAL LEADERSHIP · ENTERPRISE AI
AI strategy,
carried into production.
I help enterprises decide, design and deploy AI systems—combining technical leadership with hands-on delivery across open-weight and proprietary models.
Enterprise AI strategyProduction AISovereign AIAir-gapped systemsForward deployment
01 / POSITION
Leadership that stays close to the system.
I work from business ambiguity through architecture, implementation and adoption. The value is not advice at a distance or engineering in isolation—it is technical judgment carried through to an operating result.
02 / PRACTICE
Where I create leverage
Production & private AI
At mavQ, I architect and lead intelligent document processing and enterprise automation platforms. My broader practice spans open-weight and proprietary models across sovereign, air-gapped, on-prem and multi-cloud environments.
- Model selection & inference
- RAG, agents & evaluation
- Kubernetes, GitOps & cloud
Forward deployment & adoption
I stay hands-on from workflow discovery and integration through safe failure, observability and adoption—where model capability becomes an operating system.
- Workflow discovery
- Integration & reliability
- Field learning & adoption
03 / RESEARCH
Selected publications
IEEE/ACM published researcher · W3C AI & Accessibility panelist · ORCID record ↗
04 / FIELD NOTES
Technology in operating context.
Notes on enterprise AI strategy, architecture, forward deployment, technical leadership and the forces shaping how technology is built and adopted in India and beyond.
05 SEP 2026 · 7 MINFour gates before self-hosting open-weight AIControl, workload, model fit and operational ownership should decide whether self-hosting creates leverage or liability. →All writing →05 / OPERATING PRINCIPLES
How I approach the field
- 01
Start with the operating constraint. Model choice follows the workflow, deployment envelope and failure cost.
- 02
Measure what users experience. Benchmarks matter; task success, latency, abstention and adoption matter more.
- 03
Keep optionality. Open weights create control. Proprietary models can create leverage. Make the tradeoff explicit.
- 04
Own the last mile. Deployment is complete when the workflow holds up in use—not when the endpoint responds.
06 / CONNECT
Building serious
enterprise AI?
I compare notes with technical and business leaders working through AI strategy, private deployment, platform architecture, document intelligence, agent reliability and the path from capability to adoption.