Work & Case Studies

Curated portfolio. Problem → Solution → Tech stack → Outcome. Each card opens the full PDF.

Domain: GTM & Platform · Type: Enterprise adoption playbook

Problem: AI platform struggled to communicate value by persona and industry; adoption stalled.

Solution: Six-layer GTM narrative with persona-mapped value props, lighthouse plays, and repeatable enterprise motions.

Tech stack: Platform capabilities mapped to use-cases (RAG, LLMOps, analytics); demo narratives.

Outcome: Clear sales enablement; accelerated enterprise conversations and proof points.

Open Case (PDF)

Domain: Platform & Org Design · Type: 0→1 vision

Problem: Need to build a Chennai engineering hub to deliver AI/FinTech platforms with reliable cadence.

Solution: Phased team build-out, platform architecture, and delivery rituals (SLOs, roadmaps, reviews).

Tech stack: Cloud-native microservices (Kubernetes, CI/CD), data/streaming backbone.

Outcome: Executable plan for hiring, platform enablement, and stakeholder alignment.

Open Case (PDF)

Domain: EdTech · Type: AI CoE + pilots

Problem: Institutions needed trustworthy AI for instructor evaluation, question generation, and thesis review.

Solution: Designed CoE + pilots with assessment pipelines, RAG-based QA, and academic guardrails.

Tech stack: RAG, embeddings, evaluation harnesses; dashboards for faculty oversight.

Outcome: Validated AI assistance for educators; roadmap for controlled rollout.

Open Case (PDF)

Domain: Governance · Type: Lifecycle framework

Problem: Enterprises lacked a pragmatic framework to govern AI systems across the lifecycle.

Solution: RESPECTFUL framework covering risk, safety, evaluation, privacy, and post-deployment monitoring.

Tech stack: Policy + technical controls (evals, guardrails, observability) mapped to SDLC gates.

Outcome: Clear governance model enabling compliant, safe AI adoption at pace.

Open Case (PDF)

Domain: Industrial AI · Type: Roadmap & CoE

Problem: Need a practical roadmap for predictive maintenance, assistants, and analytics in manufacturing.

Solution: Factory AI roadmap with pilots (predictive maintenance, market/HR assistants) and operating model.

Tech stack: Sensor ETL, ML workflows, retrieval-augmented assistants, BI pipelines.

Outcome: Sequenced program enabling measurable wins and organizational learning.

Open Case (PDF)