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Engineering notes on building, modernizing, and operating enterprise software, AI systems, data platforms, and cloud infrastructure.
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Hands-on writing about enterprise AI, architecture, modernization, data, cloud systems, reliability, governance, and security.
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Read the engineering articleEnterprise Architecture
“ERPNext is slow” is not a diagnosis. Performance problems must be isolated by layer, workload, transaction type, and evidence before they can be fixed safely.
Read insightEnterprise Architecture
Standard ERPNext manufacturing provides a useful foundation, but production environments often need careful process mapping and controlled extensions around operational constraints.
Read insightEnterprise Architecture
ERPNext customization becomes difficult when business logic is scattered, upgrades are treated as emergencies, and integrations lack clear boundaries. A controlled architecture keeps change maintainable.
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Product Engineering
Generating forms from spreadsheet columns is easy. Building a useful business application requires understanding the entities, relationships, calculations, and workflows hidden inside the data.
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AI Engineering
Human review should handle uncertainty, not become the workflow itself. Well-designed AI systems automate high-confidence decisions and route only ambiguous cases for review.
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Data Engineering
Enterprise systems often contain many representations of the same real-world entity. Resolving those representations is essential for reliable search, analytics, AI, and automation.
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AI Engineering
Reliable AI systems should not call a language model for every task. Confidence-driven architectures determine when deterministic processing is sufficient and when additional reasoning is required.
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AI Engineering
As enterprise knowledge bases grow, RAG becomes a retrieval-engineering problem involving search strategy, metadata, ranking, freshness, and observability.
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Data Engineering
Before enterprise AI can reason effectively, unstructured content must be transformed into structured, normalized, and trustworthy data.
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AI Engineering
AI demos are easy to build. Production systems require reliability, security, observability, scalability, and disciplined software engineering.
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AI Engineering
Production AI systems work best when deterministic engineering handles predictable tasks and language models are reserved for ambiguity and reasoning.
Read insightAI Engineering
Enterprise AI initiatives often fail because of weak data foundations, unclear architecture, poor evaluation, missing governance, and inadequate production engineering—not because of the model itself.
Read insightExisting insights
Practical articles from the existing Vishleshak insight collection.
Enterprise Architecture
Practical integration design for reducing repetitive work, controlling failure modes, and creating dependable information flows between business systems.
Read insightEnterprise Architecture
A practical comparison of request-driven APIs and event-driven webhooks, including delivery reliability, ownership, retries, and operational trade-offs.
Read insightCloud
Engineering practices for secure, observable SaaS integrations with explicit authentication, retries, rate limits, and manageable failure modes.
Read insightProduct Engineering
Practical React architecture choices for keeping components, state, performance, and team ownership manageable as a frontend codebase grows.
Read insightCloud
How build, deployment, infrastructure, and observability automation make production releases more repeatable, visible, and recoverable.
Read insightAI Engineering
A grounded engineering view of AI-assisted workflows, enterprise information access, operational automation, and the controls production systems require.
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