Debabrata Pruseth

Debabrata Pruseth is an Enterprise Architect, Applied AI Leader, and Technology Strategist specializing in enterprise AI, AI engineering, cloud strategy, and digital transformation. He helps organizations bridge business strategy and practical AI implementation to deliver scalable, responsible, and measurable business outcomes.

How AI Agents Remember: A Beginner’s Guide to Memory, Embeddings and Vector Databases

AI, AI Engineering /

How do AI agents remember conversations, retain user preferences and retrieve relevant information? This beginner-friendly guide explains how short-term and long-term memory work, including embeddings, vector databases, semantic search and indexing. It also explores how to build memory-enabled AI agents using LangChain, LangGraph, Redis, PostgreSQL with pgvector and Azure AI Search, and the additional value dedicated memory solutions like Mem0 can bring.

How AI Agents Remember: A Beginner’s Guide to Memory, Embeddings and Vector Databases Read More »

Should Your AI Be 90% Accurate—or 85% Accurate and 5% Human?

AI Psychology /

What if a 90% accurate AI is not necessarily better than an 85% accurate AI with a little human help?

We often treat accuracy as the finish line for AI. But when AI makes a mistake, humans react very differently than when another human does. Perhaps the last mile of AI adoption isn’t about eliminating humans—or chasing 100% accuracy—but understanding where the human hand still matters.

In this article, I explore AI accuracy, human psychology, uncertainty, and a surprisingly philosophical question: How accurate is accurate enough?

Should Your AI Be 90% Accurate—or 85% Accurate and 5% Human? Read More »

Does Every AI Control-Plane Decision Need an LLM? Evaluating Specialized Decision Models for Policy-Aware AI Routing

Research Article /

This research explores whether every AI control-plane decision needs a general-purpose LLM, comparing specialized System 1 decision models with LLMs for policy-aware AI routing across decision quality, latency, cost and governance.

Does Every AI Control-Plane Decision Need an LLM? Evaluating Specialized Decision Models for Policy-Aware AI Routing Read More »

System 1 vs LLM: Does Every AI Decision Really Need a Large Language Model?

AI, AI Engineering /

Does every AI decision need the power of a general-purpose LLM? We tested Jev 1.13 as a System 1 decision model against an OpenAI LLM for a banking model-routing problem. The experiment revealed substantially lower observed latency and routing API cost — and raised a bigger question about how enterprises should match the right level of intelligence to each AI decision.

System 1 vs LLM: Does Every AI Decision Really Need a Large Language Model? Read More »

Multisensor Earth Observation and Unsupervised Anomaly Analysis of Environmental Conditions Preceding the 2026 Langtang Lirung Glacier Collapse and Slope Failure

Research Article /

What did satellites observe before the 2026 Langtang Lirung glacier collapse? This study combines climate records, Sentinel satellite data, and unsupervised AI to uncover unusually warm, melt-favourable conditions—while showing why anomaly detection should not be confused with disaster prediction.

Multisensor Earth Observation and Unsupervised Anomaly Analysis of Environmental Conditions Preceding the 2026 Langtang Lirung Glacier Collapse and Slope Failure Read More »

Build an AI-Powered Glacier Monitoring System: What Satellites Revealed Before the 2026 Langtang Lirung Collapse

AI, AI Engineering /

Can AI see a glacier disaster coming?

Satellite and climate observations before the 2026 Langtang Lirung event revealed exceptionally warm, melt-favourable conditions—but an unsupervised AI model did not find a unique monthly warning signal.

That “negative” result teaches one of the most important lessons in applied AI:

Anomaly detection is not prediction.

Build an AI-Powered Glacier Monitoring System: What Satellites Revealed Before the 2026 Langtang Lirung Collapse Read More »

Build an AI-Powered Financial Digital Twin: Simulate a $100 Billion Bank with Python

AI, AI Engineering /

What happens when a bank’s cloud region fails?

It may not remain an IT problem.

Cloud failure → payment disruption → customer impact → deposit withdrawals → liquidity stress.

I explored how Python, NetworkX, SimPy, Monte Carlo simulation, financial models, and an LLM can work together to build a Financial Digital Twin of a synthetic $100B bank.

The most interesting lesson? The LLM doesn’t calculate the financial results. Python does. The LLM explains validated results to humans.

That’s a much more interesting enterprise AI architecture than simply putting a chatbot on top of financial data.

Build an AI-Powered Financial Digital Twin: Simulate a $100 Billion Bank with Python Read More »

Build an AI Signature Verification and Fraud Detection System

AI, AI Engineering /

What if a signature matches perfectly because a fraudster copied the genuine signature?

That’s why signature verification needs more than one neural network.

This project combines:

YOLO detection → Siamese embeddings → structural analysis → document forensics → evidence fusion → explainable reports.

It’s a great example of how real AI systems move beyond model.predict().

Build an AI Signature Verification and Fraud Detection System Read More »

AI Signature Extraction and Manual-Signing Indicator Analysis

AI, AI Engineering /

Can AI tell whether a document contains a handwritten signature?

Finding the signature is only the beginning.

In this project, we combine YOLO, Segment Anything, OpenCV, feature extraction, quality gates, and human review to build an AI-assisted signature analysis pipeline.

Along the way, you’ll learn one of the most important lessons in applied AI:

a model should never claim more than its input can prove.

AI Signature Extraction and Manual-Signing Indicator Analysis Read More »

Build an AI Deepfake Image Detection System: A Beginner’s Guide to Multi-Evidence Digital Forensics

AI, AI Engineering /

Learn how to build an AI-powered deepfake image detection system that combines computer vision, image forensics, metadata analysis, and explainable AI to detect manipulated images with confidence.

Build an AI Deepfake Image Detection System: A Beginner’s Guide to Multi-Evidence Digital Forensics Read More »

Scroll to Top