I connect business priorities with engineering strategy, architecture, and delivery. My work spans globally distributed teams, platform transformation, and the governance needed to make AI dependable at enterprise scale.
Recent articles on enterprise AI, engineering leadership, and technology economics, published on Medium and LinkedIn.
Beyond LLMs: The Anatomy of AI-Powered Systems and Why Every Engineering Team Needs to Understand Them AI is changing how software is built, how businesses operate, and how decisions are made. Yet …
A Practical Guide to Automating Everyday Life Learn how to build, deploy and run your own AI agent for personal tasks, family routines, reminders and daily planning. Start with free local AI models…
Your AI Agent Has a Budget. But Who Is Accountable When It Overspends? Imagine giving an AI agent a task. It calls a model. Then a tool. The result isn’t right, so it retries. It retrieves more dat…
AI Is Moving From Chatbots to Everyday Work Artificial intelligence is no longer limited to answering questions or generating images. The latest developments show a clear shift towards AI that can …
The AI Unit Economics Problem: Why Cost per Token Is the Wrong Executive Metric Enterprise AI has a measurement problem. We are becoming increasingly sophisticated at measuring tokens, API calls, m…
For a while, most conversations about AI cost started with tokens. How many did we use? Which model was expensive? Why did the bill move this month? Those questions still matter. But they are quick…
Five working AI agent skills connecting source-code graphs, architectural decisions and durable Obsidian memory across Codex, Claude and MCP.
Long-form perspectives on enterprise AI engineering, hybrid RAG, agentic reliability, and technology economics.
Deconstructing prompt token bloat, cache miss penalties, reranker latency, and egress costs: an engineering guide to modeling enterprise LLM unit economics.
Why pure semantic vector search fails on product SKUs, financial tickers, and regulatory acronyms, and how reciprocal rank fusion (BM25 + Dense + Reranking) fixes it.
A disciplined engineering roadmap from exploration to scale: how to advance enterprise AI initiatives past pilot purgatory into governed, reliable production.
Reference architectures and security topologies developed to bridge the gap between AI proofs-of-concept and scalable enterprise production.
A battle-tested production blueprint for enterprise search and knowledge retrieval combining dense semantic embeddings, sparse BM25 indexing, and cross-encoder reranking.
Multi-hop query decomposition, dynamic query reformulation, and autonomous citation critique for high-complexity enterprise research.
A unified reverse-proxy platform layer enforcing security, multi-provider model routing, semantic caching, token quotas, and audit logging across all enterprise apps.
Defense-in-depth security framework protecting production AI systems from indirect prompt injection, data exfiltration, jailbreaks, and adversarial poisoning.
Bhavin's curated technology tracking across Adopt, Trial, Assess, and Watch categories based on real-world enterprise viability.
Hybrid sparse-dense retrieval with lexical BM25, semantic vector search, and cross-encoder reranking.
Reverse-proxy routing layer providing unified multi-provider fallback, rate-limiting, semantic caching, and token budgeting.
Dynamic retrieval loops where agents decompose ambiguous questions, plan multi-hop searches, and critique intermediate outputs.
Continuous CI/CD eval pipelines (Ragas, DeepEval, Promptfoo) benchmarking ground truth, faithfulness, and latency before deployment.
Vision-guided autonomous agents interacting directly with legacy desktop and web applications via keyboard/mouse emulation.
Multi-agent orchestration architectures (e.g. LangGraph supervisor-worker networks) coordinating specialised agent roles.
Autonomous software agents authorized to execute programmatic financial transfers, invoices, or settlements.
End-to-end agents writing code, merging PRs, and deploying directly to customer-facing production infrastructure without human sign-off.
Transparent, client-side engineering and financial calculators with zero black-box assumptions.
Model monthly vector storage, embedding tokens, inference volume, and caching return across enterprise document pools.
Compute daily, monthly, and annualized inference budgets based on prompt/completion ratios and prompt cache hits.
Map candidate AI initiatives into Quick Wins, Strategic Bets, Experiments, or Defer based on business value and risk.
A rigorous 50-point engineering, security, and FinOps verification gate before promoting Generative AI and Agentic systems to live enterprise traffic.
Senior Engineering Manager at Commonwealth Bank, with a career spanning AI strategy, platform engineering, architecture, and globally distributed delivery.
I work with senior leaders, product teams, and engineers to turn business priorities into delivery roadmaps and dependable services. My approach brings engineering, architecture, and domain expertise together early, with quality and governance built into the operating model.