Address a Significant Challenge in talent acquisition: efficiently processing a large volume of resumes within tight deadlines

 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

hashtag#ResumeAutomation
hashtag#AITalentAcquisition
hashtag#GPTinRecruitment
hashtag#LeanCanvas
hashtag#MLResumeScreening
hashtag#TAOneAI
hashtag#RecruitmentTech
hashtag#HiringAutomation
hashtag#AIHiringTool
hashtag#IT
hashtag#Banking
hashtag#logistics
hashtag#EDI
hashtag#B2B
hashtag#Retail
hashtag#resume
hashtag#jobs
hashtag#opportunities
hashtag#latestjobs
hashtag#USA
hashtag#India
hashtag#Singapore
hashtag#Malaysia
hashtag#ITjobs
hashtag#LLM
hashtag#mechinelearning
hashtag#ML
hashtag#chatgpt
hashtag#googlebard
hashtag#googlegemini
hashtag#azure
hashtag#AWS
hashtag#SAP
hashtag#IDOC
hashtag#marketing
hashtag#recruitment
hashtag#itservices
hashtag#jobopenings
hashtag#jobopportunities
hashtag#chatgpt4
hashtag#googlebard
hashtag#googlegemini
hashtag#AzureLogicapps

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

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

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 

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. 

🚀 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

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 

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 

Behind the EDI Validation Suite

 

Behind the EDI Validation Suite — Turning an Idea Into a Working Platform
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

 

 

 

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Intro about ChatGPT-6 Astra

 

Intro about ChatGPT-6 Astra

When I heard first time about Astra and i really excited and get tensed in parallel.

The Real Story Behind GPT-6 Astra

Instead of asking it to write a script for a web app, you point it at a screen, and it builds, tests, and deploys it .

The Pros: What Makes Astra a Game-Changer

True "Computer Use" & Browser Automation: Astra doesn’t just output code or text; it can log into software, fill out complex web forms (like a 1040 tax form), update CRMs, and run frontend quality-assurance checks on its own code . (Think of it like hiring a junior remote assistant who can physically click through a dashboard for you).

Surgical Template Adherence: In past models, getting an AI to match your exact corporate slide deck or legal document template felt like pulling teeth. Astra actually respects structural style guides and pulls only the relevant context rather than padding outputs with fluff .

Long-Horizon Codex Memory: If you’re a developer working in Codex, Astra maintains state across massive, multi-file sessions without losing track of why a previous fix failed 20 steps ago .

Next-Level Math & Science Reasoning: Saturating near-100% on FrontierMath isn't a party trick anymore—it’s actively assisting researchers in mapping complex theoretical logic and data analysis without hallucinating basic structural logic.

The Cons: Where the Reality Check Hits

The Message-Limit Tax: Astra is a heavy-duty engine. It eats up your usage limits twice as fast as the default model (GPT-5.6 Sol). Using it for a simple "write a polite email" request is like using a freight train to go get groceries.

Higher Latency & Cost: Because it's looping through multi-step agentic thoughts and verification checks, you’ll catch yourself waiting 10+ seconds for a response that used to pop up instantly .

The "Critical" Security Edge: Astra crossed OpenAI’s threshold into "Critical" cybersecurity capabilities. While locked down with enterprise guardrails , models this capable of autonomous system navigation require hyper-vigilant admin oversight so they don’t accidentally expose a company dashboard or misinterpret a destructive prompt .

Overkill for 80% of Daily Tasks: If your daily workflow is drafting LinkedIn posts, brainstorming hooks, or rephrasing bullet points, Astra is a massive over-allocation of compute. Sol still handles day-to-day writing faster and cheaper .

How to Explain It to a Non-Technical Friend

"Imagine your last-gen AI was a brilliant intern sitting across the desk who could write a killer essay, but you still had to copy-paste it, format it, log into WordPress, and click publish yourself. Astra is the intern who asks for your login, opens the browser, formats the blog, checks the mobile view for broken CSS, and hits publish—while you grab a coffee."

hashtag#Astra hashtag#AI hashtag#chatgpt hashtag#GpT6 hashtag#LLM hashtag#ML