Hi Friends
Good Day
Hope you are doing good.
I have created this presentation for Idea, which aims to address a significant challenge in talent acquisition: efficiently processing a large volume of resumes within tight deadlines.
To develop this solution, I have utilized various AI/ML LLM models, including Cohere Embed, reinforcement learning (based on trial and error), and the Random Forest (decision trees) ML model, to analyze data and effectively process a greater number of resumes in a shorter time frame, ensuring the selection of more qualified and skilled candidates.
For coding the algorithms in Python, I am leveraging Chat GPT and Google Bard LLMs.
To articulate the Idea, the problem, the solution, and the marketing opportunities, I have employed the Lean Canvas Model, which provides a clear explanation of each section.
I encourage you to watch the video and share any questions you may have, as your feedback will be invaluable in enhancing my solution.
https://lnkd.in/dDwZWzfm
Thanks
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Friday, October 23, 2026
Address a Significant Challenge in talent acquisition: efficiently processing a large volume of resumes within tight deadlines
Thursday, October 22, 2026
Day 8 — AI Can Help the BA Negotiate a Difficult Customer Conversation
Day 8 — AI Can Help the BA Negotiate a Difficult Customer Conversation
Imagine the customer says:
“We will not approve additional budget.”
Instead of arguing, the BA can use AI to model alternatives.
Scenario A
Full requirement
→ Full investment
→ Original timeline + additional time
Scenario B
Prioritized requirements
→ Lower investment
→ Earlier delivery
Scenario C
Phase 1 + Phase 2
→ Controlled investment
→ Business value delivered earlier
AI can help the BA perform:
What-if analysis
But the BA should never manipulate numbers to force customer approval.
The objective is:
Find the best business outcome within the customer's constraints.
That is negotiation.
Not estimation manipulation.
hashtag#BusinessAnalyst hashtag#Negotiation hashtag#AI hashtag#StakeholderManagement hashtag#BusinessValue
Tuesday, October 20, 2026
Day 10 — AI Can Turn a BA into a Business Problem Solver
Day 10 — AI Can Turn a BA into a Business Problem Solver
A customer may say:
“Our invoice processing is taking too long.”
A traditional BA may document:
“Invoice processing should be faster.”
An AI-enabled BA should investigate:
Where is the delay?
Order received
↓
Validation
↓
Mapping
↓
ERP processing
↓
Invoice generation
↓
EDI translation
↓
Transmission
↓
Customer acknowledgement
AI can help analyze:
Process logs
Transaction volumes
Failure patterns
Processing times
Exception reports
Operational tickets
The BA can then identify:
“The problem is not the EDI transmission. The major delay occurs during downstream ERP validation.”
That's a much more valuable BA contribution.
hashtag#AI hashtag#BusinessAnalyst hashtag#ProcessImprovement hashtag#B2B hashtag#DigitalTransformation
Saturday, October 17, 2026
Day 7 — How to Explain an Estimate Overrun Without Losing Customer Trust
Day 7 — How to Explain an Estimate Overrun Without Losing Customer Trust
Never tell the customer:
“The team underestimated the project.”
Instead, establish transparency.
Use this structure:
1. What we originally understood
What assumptions were used?
2. What we discovered
What new information became available?
3. What changed
Which requirements, dependencies or complexities changed?
4. What is the impact?
Additional:
Development
Testing
Infrastructure
Integration
Security
Support
5. What are the options?
For example:
Option A: Full scope — higher investment
Option B: MVP — lower investment
Option C: Phased implementation — controlled investment
AI can help prepare this analysis.
But the BA must own the conversation.
Transparency creates trust.
hashtag#BusinessAnalyst hashtag#StakeholderManagement hashtag#AI hashtag#Estimation hashtag#CustomerSuccess
Thursday, October 15, 2026
Day 1 — The Future BA: From Requirement Collector to Business Advisor
Day 1 — The Future BA: From Requirement Collector to Business Advisor
A traditional BA primarily asks:
“What are your requirements?”
An AI-enabled BA asks:
“What business problem are we actually trying to solve?”
AI can help the BA:
Analyze customer documents
Extract business requirements
Identify missing requirements
Detect contradictions
Group requirements by business capability
Identify functional and non-functional requirements
Generate clarification questions
Identify assumptions and dependencies
Compare current vs future processes
But the BA remains responsible for understanding why the requirement exists.
The real transformation is:
Requirement Collector → Requirement Analyst → Business Advisor
AI does not make someone a good BA.
The BA's ability to ask the right questions + AI's ability to analyze information makes a powerful combination.
Monday, October 12, 2026
🚀 Building an EDI Validator designed to simplify EDI testing
🚀 Building an EDI Validator designed to simplify EDI testing
After spending more than 20 years in the EDI/B2B integration industry across retail, logistics, finance, and enterprise integration projects, one question has consistently stayed with me:
Why do experienced EDI teams still spend so much time finding validation problems?
Throughout my career, I've repeatedly seen the same challenges across organizations of all sizes:
🔴 Trading partner testing cycles taking weeks instead of days
🔴 Manual verification of ISA/IEA, GS/GE, and ST/SE control numbers
🔴 Missing mandatory segments causing repeated rejections
🔴 Segment count mismatches discovered late in testing
🔴 Customer-specific business rules maintained in spreadsheets and documents
🔴 Teams using multiple tools, editors, and scripts to validate a single transaction
🔴 Valuable time spent debugging instead of delivering business value
As an EDI developer and integration consultant, EDI Tech Lead I often asked myself:
"Why are we still manually investigating problems that software should identify in seconds?"
Over the years, I worked with various EDI translators and integration platforms. While they provide excellent integration capabilities, I found that validation activities themselves often remain fragmented, time-consuming, and highly dependent on individual expertise.
A typical EDI validation process usually involves:
✅ Parsing transactions
✅ Verifying envelope structures
✅ Checking control numbers
✅ Validating segment counts
✅ Confirming mandatory segments
✅ Applying business rules
✅ Verifying trading partner requirements
✅ Preparing error reports
✅ Repeating the process after every correction
For many teams, this can consume hours or even days.
This recurring challenge became the primary motivation behind my personal project:
🚀 Building an EDI Validator designed to simplify EDI testing, partner onboarding, and transaction validation.
My goal is not to replace existing EDI platforms.
My goal is to provide a focused validation layer that can:
✔ Detect structural errors quickly
✔ Validate business rules automatically
✔ Support trading partner-specific requirements
✔ Produce meaningful error reports
✔ Reduce onboarding cycles
✔ Help EDI teams spend more time delivering solutions and less time debugging files
The platform currently supports:
• ISA/IEA, GS/GE, ST/SE validation
• Segment count validation
• Business rule validation
• Trading partner-specific validation
• Error severity classification
• Detailed validation reporting
• Validation history and analytics
I'm building this solution based on nearly two decades of real-world EDI implementation experience.
What has been the most time-consuming or frustrating part of EDI validation and partner onboarding in your experience?
hashtag#EDI hashtag#B2BIntegration hashtag#EDIValidation hashtag#X12 hashtag#EnterpriseIntegration hashtag#SupplyChain hashtag#Integration hashtag#BuildInPublic hashtag#SaaS
Sunday, October 11, 2026
Day 14 — AI + BA: From Customer Requirement to Approved Business Case
Day 14 — AI + BA: From Customer Requirement to Approved Business Case
Imagine the complete flow:
Customer Problem
↓
AI-assisted Requirement Discovery
↓
BA Validation
↓
Business Process Analysis
↓
Requirement Gap Analysis
↓
Company Capability Mapping
↓
Solution Options
↓
Effort Estimation
↓
Cost/Benefit Analysis
↓
Risk Analysis
↓
Timeline
↓
Customer Discussion
↓
Approval
↓
Implementation
↓
Measurement of Business Outcome
This is much more powerful than simply creating a requirements document.
The BA becomes the orchestrator of business value.
hashtag#BusinessAnalyst hashtag#AI hashtag#BusinessCase hashtag#RequirementsEngineering hashtag#DigitalTransformation
Friday, October 9, 2026
Day 13 — How to Present a Solution Within the Company's Budget
Day 13 — How to Present a Solution Within the Company's Budget
A good solution is not necessarily the most technically advanced solution.
It must balance:
Business Value
Customer Need
Technology Capability
Risk
Timeline
Budget
For example:
Solution A
Advanced platform
Investment: High
Time: 9 months
Solution B
Existing platform enhancement
Investment: Medium
Time: 5 months
Solution C
MVP + phased enhancement
Investment: Low initially
Time: 2 months for Phase 1
AI can help compare:
Cost vs Benefit vs Risk vs Timeline
The BA can then recommend the option that provides the best business value, not simply the most sophisticated technology.
hashtag#BusinessAnalyst hashtag#AI hashtag#BusinessValue hashtag#CostOptimization hashtag#DigitalTransformation
Tuesday, October 6, 2026
Monday, October 5, 2026
Sunday, October 4, 2026
CHAT GPT 6 ASTRA - Introduction - continuation
Monday, August 24, 2026
From EDI Validation Concepts to a Working Solution — Introducing EDI Validation Suite
From EDI Validation Concepts to a Working Solution — Introducing EDI Validation Suite
Over the years, many of us working in EDI/B2B Integration have experienced the same challenges during implementation and testing:
• EDI syntax errors
• Missing or incorrect segments/elements
• Envelope and control-number mismatches
• Business-rule violations
• Trading-partner-specific requirements
• Repeated manual validation and troubleshooting
We often discuss these challenges and their solutions. But I wanted to take it one step further — can we build a practical tool that helps EDI teams identify these problems before transactions reach the trading partner or production environment?
That is the idea behind the EDI Validation Suite.
In this video, I'm sharing an early demonstration of the working solution.
You'll see how we can:
➡️ Upload an EDI transaction
➡️ Process and validate the document
➡️ Identify validation failures
➡️ View detailed error information
➡️ Understand where and why the transaction failed
This is still an evolving product. My current objective is not to present it as a finished commercial solution, but to share what we are building, demonstrate the progress, and learn from the EDI community.
In the coming videos, I will gradually demonstrate individual capabilities and explain how we're approaching different EDI validation challenges.
If you work with EDI, B2B Integration, EDI Testing, Trading Partner Onboarding, Business Analysis or Integration Architecture, I would really value your feedback.
What are the biggest EDI validation challenges you face during implementation and testing?
Your suggestions can help shape the future direction of this solution. It is greatly appreciated.
hashtag
#EDI hashtag
#B2B hashtag
#EDIValidation hashtag
#X12 hashtag
#B2BIntegration hashtag
#SystemIntegration hashtag
#SupplyChain hashtag
#Integration hashtag
#EDIDevelopment
https://lnkd.in/dqqgZMz5
Thursday, June 4, 2026
Why do EDI projects still spend hours troubleshooting basic X12 errors?
EDI Quality Insights : #1
Why do EDI projects still spend hours troubleshooting basic X12 errors?
Common issues I repeatedly see in enterprise EDI environments:
❌ Missing ISA/GS/ST envelopes
❌ Incorrect segment counts in SE
❌ Invalid control number matching
❌ Missing mandatory segments
❌ Incorrect element lengths
❌ Delimiter inconsistencies
❌ Trading partner compliance failures
These issues often result in:
Delayed order processing
Partner rejections
Increased support costs
Production incidents
To address this challenge, I've been developing an EDI Validation Engine that performs:
✅ Structural validation
✅ Envelope validation
✅ Segment validation
✅ Element-level validation
✅ Control number verification
✅ Real-time error reporting
Example:
Input:
SE*2*0001~
Validation Result:
Expected Segment Count = 3
Actual Segment Count = 2
Status: Failed
The objective is simple:
Catch EDI errors before they reach production.
I'd love to hear:
What is the most common EDI validation issue your organization faces?
hashtag#EDI hashtag#X12 hashtag#B2BIntegration hashtag#SupplyChain hashtag#IBMSTERLING hashtag#Axway hashtag#Boomi hashtag#EnterpriseIntegration hashtag#EDIQuality hashtag#X12Errors hashtag#EDITroubleshooting hashtag#DataValidation hashtag#EDICompliance hashtag#SupplyChainEfficiency hashtag#ErrorReporting hashtag#EDIValidationEngine hashtag#BusinessAutomation hashtag#OrderProcessing hashtag#TradingPartnerSuccess hashtag#DataIntegrity hashtag#EnterpriseEDI hashtag#TechSolutions hashtag#EDIInsights hashtag#ErrorPrevention hashtag#DigitalTransformation hashtag#ProcessOptimization hashtag#EDIManagement hashtag#RealTimeValidation
Friday, March 13, 2026
What is AI / how the Data will be processed using AI capabilities / How we can achieve Cybersecurity and avoid data breaches or data thefting and security vulnerabilities
Your question touches four big areas:
1️⃣ What AI is
2️⃣ How data is processed using AI
3️⃣ How AI helps achieve cybersecurity & prevent breaches
4️⃣ AI tools companies can use for different needs
I'll explain each clearly. 🚀
1️⃣ What is Artificial Intelligence (AI)?
Artificial Intelligence (AI) is the ability of machines or software to simulate human intelligence such as:
-
Learning from data
-
Recognizing patterns
-
Making predictions
-
Understanding language
-
Automating decisions
AI systems are usually built using fields like:
-
Machine Learning
-
Deep Learning
-
Natural Language Processing
-
Computer Vision
Simple Example
A spam email filter learns from thousands of emails and automatically detects whether a new email is spam or legitimate.
2️⃣ How Data Is Processed Using AI
AI systems follow a data pipeline.
Step-by-Step AI Data Processing
1️⃣ Data Collection
-
Databases
-
Sensors
-
User activity
-
Logs
-
APIs
2️⃣ Data Cleaning & Preparation
-
Remove duplicates
-
Handle missing values
-
Normalize formats
3️⃣ Feature Engineering
-
Extract useful information from raw data
Example
Raw log → IP address, location, login time
4️⃣ Model Training
Using algorithms such as:
-
Regression
-
Decision Trees
-
Neural Networks
AI models learn patterns from historical data.
5️⃣ Model Testing
Check accuracy using validation datasets.
6️⃣ Deployment
Model is deployed into:
-
Apps
-
Security systems
-
Fraud detection engines
7️⃣ Continuous Learning
AI updates models when new data arrives.
3️⃣ Using AI for Cybersecurity & Preventing Data Breaches
AI plays a huge role in modern cybersecurity.
Common threats:
-
Data breaches
-
Phishing attacks
-
Malware
-
Ransomware
-
Insider threats
AI helps detect abnormal behavior quickly.
Key AI Cybersecurity Capabilities
1️⃣ Threat Detection
AI analyzes billions of logs to detect unusual patterns.
Example:
-
User login from India at 10 AM
-
Suddenly login from Russia at 10:05 AM
AI flags it immediately.
2️⃣ Malware Detection
AI identifies new malware by behavior patterns.
Example security tools:
-
CrowdStrike Falcon
-
Darktrace
3️⃣ Phishing Detection
AI scans emails and URLs to detect fraud.
Example platforms:
-
Microsoft Defender for Office 365
-
Proofpoint
4️⃣ Intrusion Detection
AI detects suspicious network behavior.
Tools:
-
Splunk Enterprise Security
-
IBM QRadar
5️⃣ Fraud Detection
Used heavily in banking and fintech.
Example:
-
Credit card fraud detection
-
Transaction anomaly detection
4️⃣ How Companies Avoid Data Breaches
Organizations combine AI + security frameworks.
Core Cybersecurity Strategies
🔐 Zero Trust Architecture
-
Never trust any user automatically
-
Always verify identity
Example platform:
-
Okta
🔐 Data Encryption
Sensitive data is encrypted.
Standards:
-
AES-256
-
RSA encryption
🔐 Access Control
Only authorized users can access data.
Methods:
-
Multi-factor authentication
-
Role-based access control
🔐 Security Monitoring
Continuous monitoring using SIEM systems.
🔐 Regular Vulnerability Scans
Tools like:
-
Nessus
-
Qualys VMDR
5️⃣ AI Tools Used by Companies
Here are different categories of AI tools used by companies.
🔹 AI Development Platforms
Used to build AI models.
-
TensorFlow
-
PyTorch
-
Scikit-learn
🔹 Cloud AI Platforms
Used for scalable AI deployment.
-
Google Vertex AI
-
Amazon SageMaker
-
Microsoft Azure AI
🔹 AI for Cybersecurity
-
Darktrace
-
CrowdStrike Falcon
-
IBM QRadar
🔹 AI for Data Analysis
-
Tableau
-
Power BI
🔹 AI Automation Tools
-
UiPath
-
Automation Anywhere
6️⃣ Real Example: AI Security System Workflow
Example in a bank:
1️⃣ User logs in
2️⃣ AI checks device fingerprint
3️⃣ AI checks location pattern
4️⃣ AI compares behavior history
5️⃣ If anomaly detected → block access
6️⃣ Alert security team
This prevents fraud and data theft instantly.
7️⃣ Skills Needed to Build AI Security Systems
Professionals usually learn:
-
Python
-
Machine Learning
-
Cybersecurity
-
Cloud computing
-
Data engineering
-
Threat intelligence
✅ In simple terms:
AI helps organizations:
-
Process massive amounts of data
-
Detect threats early
-
Automate security monitoring
-
Protect customer information
💡 If you want, I can also explain:
-
Complete AI architecture used in big companies (Google / Amazon / Banks)
-
How to build an AI cybersecurity system step-by-step
-
Top AI security tools used in 2025
-
Career roadmap for AI + Cybersecurity
Just tell me! 🚀
Thursday, January 8, 2026
Check whether in your location and forecast for 7 days
🌦️ Smart Weather PWA
📊 Hourly Temperature (Next 24h)
📅 7-Day Forecast
🛰️ Weather Radar
⚠️ Data accuracy depends on region & provider
Check Your IPv4 and IPv6 of Public IP of your system
Public IP Checker
-
Inbound Flow: 1) The inbound, EDI data needs to be collected. 2) The collected data should be De-enveloped (removing the headers) to get t... -
Here’s a comparison of the latest IBM Sterling Integrator map editor vs. IBM Transformation Extender (ITX) map editor from a data-mapping p...
-
Encoding Conversion (From ISO8859_1 / ISO-8859_1 / ISO_8859_1 to UTF8 and vice versa) There is one service called “Encoding Conversion” in...
When we talk about solving EDI problems, explaining the concept is relatively easy.
Building a working solution around those concepts is the more interesting challenge.
With the EDI Validation Suite, the objective is not only to share knowledge about EDI validation but also to explore how those concepts can be converted into a practical and reusable platform.
In this video, I'm sharing a little of what is happening behind the scenes.
Rather than showing only the user interface, this video provides a glimpse into the technical implementation supporting the solution — including the backend services, APIs and development environment that make the validation workflow possible.
The goal is to build the solution step by step with a foundation that can eventually support:
➡️ EDI transaction validation
➡️ Standard validation rules
➡️ Business-rule validation
➡️ Trading-partner-specific requirements
➡️ Validation history and reporting
➡️ Dashboard analytics
➡️ API-based validation
➡️ Future enterprise and SaaS capabilities
This is still part of the product-building journey.
I'm intentionally sharing some of the implementation process because I want this initiative to be more than discussions, presentations or theoretical EDI concepts.
The objective is to convert real EDI implementation experience into a practical working solution.
Over the coming weeks, I'll share separate demonstrations covering different capabilities of the EDI Validation Suite in more detail.
If you're an EDI Developer, B2B Consultant, Business Analyst, Integration Architect, QA Engineer or someone managing trading-partner integrations, your feedback and suggestions are very welcome.
What capabilities would you expect from a modern EDI validation platform?
hashtag#EDI hashtag#B2B hashtag#EDIValidation hashtag#X12 hashtag#B2BIntegration hashtag#APIs hashtag#IntegrationArchitecture hashtag#SoftwareDevelopment hashtag#EnterpriseIntegration