Anshum Dev
Designing production-grade AI systems for enterprise software.
Building
Intelligent Systems
Problems
I'm Anshum Dev, an AI Engineer and Full-Stack Developer passionate about building intelligent systems that solve real-world business problems. I specialize in designing scalable AI applications, enterprise software, and production-ready architectures using modern technologies across backend, cloud, and machine learning. Currently pursuing my Bachelor's in Computer Science, I spend most of my time exploring LLMs, RAG systems, multi-agent workflows, system design, and automation. I enjoy transforming complex ideas into reliable products with a strong focus on performance, clean architecture, and user experience. Beyond building software, I'm constantly learning, experimenting, and documenting my engineering journey. I'm always excited to collaborate on ambitious projects and create technology that has a meaningful impact.
The Autonomous Analyst
The path to autonomy.
Started Programming
Wrote my first line of Python.
Backend Development
Built resilient APIs in Django.
Distributed Systems
Migrated monoliths to microservices.
Artificial Intelligence
Began working with LLM orchestration.
Production AI
Deployed autonomous agents to enterprise.
NEXUS Data Core.
Open Case StudyThe Problem
Enterprise data is fragmented across thousands of PDFs, CRM records, and SQL databases. LLMs hallucinate when forced to read unstructured dumps without strict context windows.
The Result
A highly-concurrent retrieval pipeline that parses 100k+ documents, chunks them semantically, and provides perfect-context answers in under 400ms.
Problems solved in production.
Autonomous Data Core
Migrated a legacy rule-based classification system to a probabilistic LLM orchestration layer capable of querying millions of rows securely.
High-Throughput Ingestion
Architected a highly available microservices ecosystem capable of processing thousands of events per second with zero data loss.
Enterprise ERP Modules
Developed custom inventory and financial reconciliation modules directly integrated into legacy SQL environments without downtime.
The bleeding edge.
Production engineering is about stability, but innovation requires experimentation. This is where I test theoretical models before they hit production.
Enter Research LabWASM-compiled RAG execution
Testing the latency implications of running vector similarity search directly in the browser using WebAssembly and Rust.
"Attention is Not All You Need"
Analyzing the recent paper on the necessity of deterministic verification layers on top of transformer models for enterprise security.
Documenting the process.
Why your RAG pipeline is failing in production.
Most RAG implementations treat LLMs as databases. Here is why you need to decouple retrieval from generation to achieve 99% accuracy.
The illusion of the 10x Engineer.
A 10x engineer doesn't write 10x more code. They make 10x fewer decisions that lead to technical debt.
Transitioning from Microservices to Moduliths.
When to stop separating your services and how to build a modular monolith that actually scales without network latency.
The nervous system.
Simple scales.
Complexity is a debt you pay every time you deploy. A simple architecture outlasts a clever one.
Automation beats repetition.
If you have to do it twice, script it. If you have to do it three times, architect a system for it.
Architecture first.
Code is temporary. Data structures and communication protocols dictate the lifespan of a product.
Towards truly autonomous enterprise intelligence.
Upcoming Products
Open-source deterministic routers for LangChain.
Research Focus
Edge-device LLM orchestration with Rust.
Architecture
Self-healing data pipelines via vector drift analysis.