I have 8+ years of engineering experience processing 1M+ document pipelines and building high-volume workloads at enterprise scale. My focus is not on creating API wrappers or generic chatbots—I specialize in production-grade AI system design, reliability engineering, and handling messy, unstructured human input.
I am building a vertically integrated AI stack focused on behavioral analysis and decision intelligence.
- smart-diary — The Application Layer
- An LLM-powered journaling system focusing on behavioral loop detection, contextual memory, and decision insights from unstructured human input.
- py-llm-skills — The Framework Layer
- A Python SDK for modular AI system building. It extracts core AI capabilities (skills) from the application layer to create reusable, production-grade workflows.
- agent_scratch — The R&D Sandbox
- My experimental grounds for testing agentic routing, orchestration paradigms, and automated evaluation frameworks.
- Architecture & System Design: Decoupling business logic from AI infrastructure.
- Reliability & Failure Handling: Fallback routing, latency management, and strict structural JSON validation.
- Evaluation Pipelines: Building automated systems to test prompt degradation, context overflow, and hallucination rates.
I write strictly about system tradeoffs, architectural failures, and engineering insights discovered in production.
- Deep-Dive Architecture: AnupTechTips
- Engineering Insights: Medium Profile
Is your production LLM system failing in edge cases?
I run asynchronous architecture audits to stabilize and scale AI pipelines. Reach out to discuss system reliability: anup@anuptechtips.com
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