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WELCOME
Enterprise Architecture & Digital Transformation Advisor — Helping organizations design scalable systems and strengthen engineering capabilities
Welcome To AeeroTech.
1️⃣ Architecture Blueprints
Sample Blueprints
• Cloud-Native Digital Lending Platform
Hub & Spoke Active Active
Reference architecture for scalable lending systems using microservices, event streaming, and API-first design.
• AI-Driven KYC & Onboarding Platform
API First Architecture Microservices Deep Drive, Data Modernization,
Regu latory compliance FIU-Reporting.
Real Time Fraud Detection in Banking
2️⃣ Enterprise Modernization Framework
Legacy Modernization Framework for BFSI
Modernization Approach
1️⃣ Discovery & Assessment
Application portfolio analysis using TIME and 6R frameworks.
2️⃣ Target Architecture Definition
Define microservices domains using Domain Driven Design.
3️⃣ Cloud-Native Platform Design
API-first architecture, event streaming, container platforms.
4️⃣ DevSecOps Enablement
CI/CD pipelines with security gates and automated compliance.
5️⃣ Migration & Execution
Incremental modernization with strangler pattern.
3️⃣ AI for BFSI
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AI-Driven Enterprise Platforms
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All Posts
Digital Lending with Agentic AI
AI-First Automation in Digital Lending” “In our digital lending platform, we adopted an AI-first automation approach , where AI is not an add-on but embedded into every decision-making and customer interaction step.We used traditional ML for deterministic risk decisions , and GenAI for cognitive automation, explanation, and user interaction .The outcome was: 70-80% straight-through processing 40-60% reduction in manual underwriting Loan TAT reduced from few weeks to a day,
Anand Nerurkar
Dec 2, 202525 min read
AI Native with python ecosystem-LangChain/LangGraph/LangSmith
What LangSmith actually does How it integrates with LangChain & LangGraph Exact configuration (env + code) What gets traced automatically How this works in AKS / production How this maps to Spring / Java world 1️⃣ What LangSmith Actually Is (Architect View) LangSmith is a managed telemetry + evaluation backend for: Prompt execution Tool calls Agent steps RAG retrieval LLM latency, cost, errors Automatic trace trees (like OpenTelemetry for LLMs) Think of it as: “Application P
Anand Nerurkar
Nov 26, 20254 min read
AI-native development & deployment
End-to-end GenAI (AI-native) development & deployment — two flavors: Azure / Spring AI (Java) and Python (LangChain) Below is a compact, interview-ready walkthrough you can speak from — step-by-step from requirements → design → build → test → deploy → monitor → optimize . For each phase I list concrete tools/frameworks , why they’re used, and what outputs you produce. At the end I give a short 30-second summary you can use in interviews. 1. High-level lifecycle (common to
Anand Nerurkar
Nov 26, 20255 min read
DevOps pipeline for Spring AI
1️⃣ Core Principle (Very Important for Interview) For Java-based Spring AI systems : Spring AI → serves production traffic PromptFlow / DeepEval / RAGAS → run as external evaluation workers CI/CD orchestrates them as quality gates No Python code runs inside the Java microservice Think of these tools as: “Post-deployment quality scanners for GenAI, not runtime dependencies.” 2️⃣ Where These Tools Sit in the Architecture Git Push | v CI Pipeline | +--> Build & Test Java (JUnit,
Anand Nerurkar
Nov 26, 20256 min read
Systematic Diagnosis
1. The Core Principle of Systematic Diagnosis Never jump to solutions. Always stabilize → observe → hypothesize → test → confirm. Most failures happen because people: Fix symptoms, not root causes Trust logs blindly Skip validation Apply “tribal fixes” Systematic diagnosis avoids that. 2. The 7-Step Systematic Diagnosis Framework (Universal) Step 1 — Stabilize First (Stop the Bleeding) Goal: Prevent business damage before deep analysis. Ask: Is customer impact ongoing? Is dat
Anand Nerurkar
Nov 26, 20253 min read
AI Best Practices
1) Debugging production agent blockers — a systematic checklist When you see symptoms like “agent looping” or “context drift” , follow this rapid diagnostic flow: A. Reproduce & Observe Capture the full conversation transcript + timestamps + request/response IDs. Reproduce with same inputs in a controlled environment (staging) and record all agent steps. B. Check orchestration / control flow Verify orchestrator state machine: are termination conditions implemented? (max_step
Anand Nerurkar
Nov 26, 20254 min read
Best AI Learning Lesson
Model Drift,Context Drift ==== ✅ 1. What is Context Drift ? Context Drift happens when a Large Language Model (LLM) or AI agent loses track of the conversation state, task state, or prior facts during a multi-step interaction. Where it occurs Multi-step agents Complex workflows (KYC → Risk → Underwriting) Long conversations RAG-based interactions Multi-agent orchestration Symptoms The agent forgets earlier instructions or contradicts previous steps. The agent starts answeri
Anand Nerurkar
Nov 26, 20259 min read
✅ AI/GenAI Testing Strategy for Digital Lending (End-to-End)
A production-grade AI system requires five layers of testing : Layer 1 — Functional Testing (AI + Non-AI) Tests if the system produces correct business outcomes . 🔶 1. RAG Retrieval Tests Verify correct chunks retrieved from vector DB Validate recall@k, precision@k Ensure metadata filtering works Validate semantic relevance score threshold 🔶 2. LLM Output Tests Policy adherence (RBI lending rules) Consistency of decisions Structured JSON response validation No hallucinated
Anand Nerurkar
Nov 25, 20256 min read
AI Engineering Best Practices
AI engineering best practices: prompt versioning, evaluation, retrieval tuning, logging, testing.” This is exactly how a GenAI Lead / Advisory Architect should answer. ✅ 1. Prompt Versioning What it means: Treat prompts like source code — version-controlled, reviewed, tested, and released. Why enterprises need it Different business units use slightly different prompts Prompts evolve with product features One small change can break a workflow Compliance requires audit histor
Anand Nerurkar
Nov 25, 20254 min read
RAG/
1. RAG PIPELINES — Enterprise-Grade Reference Architecture RAG in enterprise = 4 layers Ingestion & Preprocessing Indexing & Storage Retrieval & Ranking Generation & Guardrails 1.1 RAG Pipeline — End-to-End Architecture A. Document Ingestion Layer OCR (AWS Textract / Azure Form Recognizer / Tesseract) PII masking (Rule-based + ML-based) Document classification (SVM/BERT/LLMs) Chunking (semantic-aware: sentences, headings) Normalization (clean, dedupe, flatten PDFs) B. Embeddi
Anand Nerurkar
Nov 25, 20257 min read
RAG/
1. RAG PIPELINES — Enterprise-Grade Reference Architecture RAG in enterprise = 4 layers Ingestion & Preprocessing Indexing & Storage Retrieval & Ranking Generation & Guardrails 1.1 RAG Pipeline — End-to-End Architecture A. Document Ingestion Layer OCR (AWS Textract / Azure Form Recognizer / Tesseract) PII masking (Rule-based + ML-based) Document classification (SVM/BERT/LLMs) Chunking (semantic-aware: sentences, headings) Normalization (clean, dedupe, flatten PDFs) B. Embeddi
Anand Nerurkar
Nov 25, 202514 min read
Interpolated ??
Interpolated inputs in prompts refer to dynamic variables that are “injected” into a prompt at runtime instead of being hard-coded.They allow you to build prompt templates where parameters change based on context, user input, or system data. ✅ What Are Interpolated Inputs? It's like having placeholders inside your prompt that get filled when the model is executed. Example (simple) Template prompt: You are analyzing a loan application. Applicant name: {{name}}, Credit sco
Anand Nerurkar
Nov 25, 202518 min read
How to debug issue for AI System??
1) Debugging production agent blockers — a systematic checklist When you see symptoms like “agent looping” or “context drift” , follow this rapid diagnostic flow: A. Reproduce & Observe Capture the full conversation transcript + timestamps + request/response IDs. Reproduce with same inputs in a controlled environment (staging) and record all agent steps. B. Check orchestration / control flow Verify orchestrator state machine: are termination conditions implemented? (max_step
Anand Nerurkar
Nov 24, 20257 min read
Multi Agent & Agentic AI
1. Agentic AI (What it means) Agentic AI refers to AI systems that can take autonomous actions , not just generate text.These systems perceive, reason, plan, act, and learn — like a digital worker that executes end-to-end tasks with minimal human intervention. Key capabilities: Autonomy: Takes decisions without being prompted every time Planning: Breaks goals into sub-tasks Tool use: Calls APIs, databases, models Reasoning loops: Self-critique, refine output Learning: I
Anand Nerurkar
Nov 24, 20256 min read
EA Strategy in Case of a Merger (M&A)
My EA strategy for a merger focuses on four pillars: discover, decide, integrate, and optimize.The goal is business continuity + synergy + tech consolidation. ✅ 1. Rapid Discovery & Baseline Assessment (Day 0–30) The first step is visibility . a. Current Enterprise Landscape Assessment Application inventory Integration landscape Data flows and critical master data Tech stack, infra, cloud usage Security posture, controls, and vulnerabilities Licensing, contracts, vendor depen
Anand Nerurkar
Nov 24, 20254 min read
Red Team Testing
✅ LLM / GenAI Pipeline for Digital Lending (RAG + LLMOps) (This is the pipeline ONLY for policies, SOPs, regulatory rules — NOT customer documents.) 🔵 Stage 1 — Data Ingestion (Policies / SOPs / Guidelines) This pipeline is ONLY for knowledge content such as: RBI credit policy Bank lending policy Product terms & conditions SOPs Operational guidelines Loan agreement templates KYC rulebooks AML rulebooks Sanction list explanation rulebook (but NOT the list itself) Input Sourc
Anand Nerurkar
Nov 23, 202516 min read
AI 1st Automation -Digital LEnding journey
⭐ End-to-End Digital Lending Architecture – Borrower Journey (Ramesh) Scenario Borrower: Ramesh Product: Personal Loan – ₹5 Lakhs PHASE 1 — Borrower Interaction (GenAI + Frontend) 1. Ramesh logs in & applies for a personal loan UI triggers Borrower GenAI Assistant (LLM-based conversational layer). Ramesh asks: “Am I eligible? What documents should I upload?” GenAI retrieves policy rules from the RAG Layer (Policies, SOPs, KYC rules, eligibility matrix). 2. Upload of documen
Anand Nerurkar
Nov 23, 202516 min read
KPI to Be Tracked-Modernization
Unified KPI Table — Before vs After Modernization (Strategic + Tactical + Operational) KPI Category KPI Before Modernization After Modernization Strategic (Business Outcomes) Customer Onboarding TAT 5 days < 24 hrs Strategic (Business) Digital Lending Approval Time 24–48 hours 30 minutes Strategic (Business) Self-Service Digital Adoption 30% 75%+ Strategic (Business) Net Promoter Score (NPS) 45 70+ Strategic (Business) Customer Retention 82% 93% Strategic (Financial) Operatio
Anand Nerurkar
Nov 22, 20253 min read
COTS+API 1st +AI 1st
Uses the existing COTS platforms (e.g., Coupa, GEP SMART) for S2C, P2P, SRM, Procurement Analytics. Unifies them via a microservices layer for enterprise orchestration. Adds AI/ML and GenAI intelligence across workflows (contracts, supplier risk, invoice anomaly detection, spend insights). Supports multi-client, modular, and compliant architecture . Here’s the architected design from that perspective : AI-First Procurement Modernization Blueprint (COTS + Microservices + AI
Anand Nerurkar
Nov 17, 20256 min read
AI-First Procurement Modernization Blueprint GreenField
Absolutely! Let's design a production-ready, AI-first procurement modernization blueprint leveraging Java microservices, Azure Cloud, Spring AI, Azure OpenAI, PostgreSQL with pgvector, and Cosmos DB , integrating your S2C/P2P/SRM processes and AI-driven automation. I’ll break it down layer by layer, including architecture patterns, data stores, microservices, and AI components. AI-First Procurement Modernization Blueprint 1️⃣ Architecture Overview (High-Level Layers) Layer D
Anand Nerurkar
Nov 17, 20256 min read
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