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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hashtag#GPTinRecruitment
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hashtag#IT
hashtag#Banking
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hashtag#EDI
hashtag#B2B
hashtag#Retail
hashtag#resume
hashtag#jobs
hashtag#opportunities
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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
Wednesday, October 21, 2026
Day 5 — AI Can Help the BA Detect Scope Creep
Day 5 — AI Can Help the BA Detect Scope Creep
A customer may initially request:
“Implement 5 EDI transactions.”
Later the requirements become:
5 EDI transactions
API integration
real-time processing
additional partners
dashboard
historical reporting
automated reconciliation
+ 24x7 support
The project estimate may have been based on the original scope.
AI can compare:
Original Requirement vs Current Requirement
and highlight:
New functionality
New interfaces
New integrations
Increased transaction volume
Additional testing
Additional security
Additional infrastructure
Additional support requirements
The BA can then show the customer:
“These changes are not merely clarification. They introduce additional scope.”
This makes difficult conversations much more objective.
hashtag#BusinessAnalyst hashtag#ScopeManagement hashtag#AI hashtag#ProjectManagement hashtag#Requirements
Tuesday, October 20, 2026
AI-Powered Business Analyst Series | Post 17
AI-Powered Business Analyst Series | Post 17
💡 A customer may approve your solution… but will it actually solve their problem?
Let me ask you something.
Have you ever worked on a project where the requirements were clear, the solution was approved, the development was completed, and the project was delivered on time…
…but the customer was still not getting the expected business results?
I believe this is one of the most important challenges a Business Analyst needs to think about.
Because delivering a solution and solving a business problem are two different things.
Imagine a logistics company handling thousands of EDI transactions every day.
Their operations team spends hours investigating failed transactions. The customer requests a new monitoring dashboard.
We analyze the requirements, estimate the effort, build the dashboard, and deliver it successfully.
But what happens next?
If the team still spends the same amount of time investigating failures, have we really solved the problem?
Perhaps the real opportunity was to identify recurring failures, automate error classification, prevent avoidable errors, and help the operations team resolve issues faster.
Now, this is where I see an interesting opportunity for AI in Business Analysis.
Instead of asking AI only to generate requirements or prepare documentation, we can use it to explore questions such as:
🔹 What is causing the highest operational cost?
🔹 Which recurring problems can be prevented or automated?
🔹 What measurable improvement could different solutions deliver?
🔹 Can we achieve the same outcome using our existing platforms?
🔹 How can we measure success after implementation?
Of course, AI-generated suggestions must be validated against actual operational data, technical feasibility, costs, and customer priorities.
The BA must also work with the customer and technical teams to agree on realistic success criteria.
For example, instead of promising that a dashboard will solve everything, we could agree to measure investigation time, repeat failure rates, and manual effort before and after implementation.
This gives everyone a clearer picture of the value delivered.
And there is a benefit for the service provider too.
When we demonstrate measurable business value, we build customer trust, strengthen long-term relationships, and identify opportunities to improve our own services.
My personal takeaway:
A Business Analyst should not stop at asking, “Did we deliver what the customer requested?”
We should also ask, “Did our solution improve the customer's business?”
AI can help us investigate possibilities, challenge assumptions, and identify better options.
But the real value comes when we combine AI with business understanding, domain knowledge, and human judgment.
💬 I would love to hear your thoughts:
When evaluating a project's success, what matters most in your experience?
1️⃣ Delivering the agreed requirements on time and within budget?
2️⃣ Achieving the business outcome the customer actually needs?
Or do you believe both must be measured together?
Let's discuss in the comments.
#BusinessAnalyst #ArtificialIntelligence #BusinessAnalysis #BusinessValue #DigitalTransformation #EDI #B2BIntegration
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
Monday, October 19, 2026
Day 6 — When the Estimate Is Burning: How AI Helps the BA
Day 6 — When the Estimate Is Burning: How AI Helps the BA
This is a very realistic project situation.
Suppose:
Original estimate: 500 hours
Actual forecast: 750 hours
The worst BA response is:
“We need another 250 hours.”
The customer will immediately ask:
“Why?”
AI can help the BA analyze the variance.
For example:
Original assumption
5 partners
5 mappings
1 communication protocol
Current reality
12 partners
14 mappings
3 protocols
Additional validation rules
Additional security requirements
Additional testing
Now the BA can build:
Original Scope → New Discovery → Impact → Effort → Timeline → Commercial Impact
The conversation becomes fact-based rather than emotional.
hashtag#BusinessAnalyst hashtag#StakeholderManagement hashtag#AI hashtag#Estimation hashtag#CustomerSuccess
Sunday, October 18, 2026
Day 4 — Mapping Customer Requirements to Company Capabilities
Day 4 — Mapping Customer Requirements to Company Capabilities
This is one of the most powerful uses of AI for a BA.
Customer requirement:
“We need 24x7 B2B transaction processing.”
The BA can map this against company capabilities:
[
{
"Customer Requirement": "24x7 processing",
"Company Capability": "B2B platform",
"Gap": "Available"
},
{
"Customer Requirement": "EDI X12",
"Company Capability": "EDI translator",
"Gap": "Available"
},
{
"Customer Requirement": "Real-time API",
"Company Capability": "API platform",
"Gap": "Available"
},
{
"Customer Requirement": "High availability",
"Company Capability": "HA infrastructure",
"Gap": "Partial"
},
{
"Customer Requirement": "Disaster recovery",
"Company Capability": "DR capability",
"Gap": "Available"
},
{
"Customer Requirement": "Partner onboarding",
"Company Capability": "Existing process",
"Gap": "Partial"
},
{
"Customer Requirement": "Advanced monitoring",
"Company Capability": "Monitoring platform",
"Gap": "Gap"
}
]
AI can help automatically create this initial mapping.
The BA then validates it with:
Customer + Technical Team + Architecture + Operations + Security + Finance
This produces a much stronger outcome:
Customer Requirement → Capability → Gap → Solution → Cost → Timeline
That's the real value of an AI-enabled BA.
hashtag#BusinessAnalyst hashtag#AI hashtag#B2BIntegration hashtag#EnterpriseArchitecture hashtag#Requirements
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
Friday, October 16, 2026
Day 2 — How AI Can Extract Clear Requirements from Customers
Day 2 — How AI Can Extract Clear Requirements from Customers
Customers rarely provide perfect requirements.
They may provide:
Emails
Excel files
Process documents
Meeting notes
Visio diagrams
Existing system documentation
EDI specifications
API specifications
Screenshots
Voice discussions
The BA can use AI to convert this information into a structured requirement model.
For example:
Customer statement:
“We need faster order processing.”
AI can help the BA identify possible questions:
What is the current processing time?
What is the expected processing time?
Which order types are affected?
What is the current transaction volume?
Is real-time processing required?
What happens during failures?
What SLA is expected?
Which downstream systems are impacted?
The important point:
AI should not convert vague requirements directly into solutions.
It should help the BA discover the questions required to make the requirement measurable.
hashtag#BusinessAnalysis hashtag#RequirementsEngineering hashtag#AI hashtag#BusinessAnalyst hashtag#DigitalTransformation
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.
Wednesday, October 14, 2026
Day 11 — Don't Just Ask Customers What They Want. Recommend What They Need.
Day 11 — Don't Just Ask Customers What They Want. Recommend What They Need.
A powerful BA should not always wait for the customer to propose the solution.
Customer:
“We want a dashboard showing failed EDI transactions.”
BA investigation:
Failure volume → 8,000/month
Manual investigation → 20 minutes/transaction
Common failures → mapping + validation + partner configuration
Potential solution:
Automated EDI validation + error classification + partner-specific rules + operational dashboard
Now the BA isn't simply delivering the customer's requested dashboard.
The BA is addressing the underlying business problem.
This is where:
Domain Knowledge + Business Analysis + AI
becomes powerful.
hashtag#BusinessAnalyst hashtag#AI hashtag#EDI hashtag#BusinessTransformation hashtag#Innovation
Tuesday, October 13, 2026
Day 9 — Can AI Help Achieve 100% Customer Satisfaction?
Day 9 — Can AI Help Achieve 100% Customer Satisfaction?
There is an important reality:
No process can guarantee 100% customer satisfaction.
But AI can help a BA significantly reduce avoidable dissatisfaction.
AI can continuously analyze:
Open requirements
Pending decisions
Change requests
Defects
Customer concerns
SLA breaches
Delivery risks
Dependency delays
Unresolved questions
The BA can create a:
Customer Risk Dashboard
Green: On track
Amber: Potential customer impact
Red: Immediate action required
The biggest advantage is early communication.
Customers usually become unhappy not simply because there is a problem.
They become unhappy when they discover the problem too late.
hashtag#CustomerExperience hashtag#BusinessAnalyst hashtag#AI hashtag#ProjectManagement hashtag#CustomerSuccess
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
What if the customer is asking for the wrong solution?
AI-Powered Business Analyst Series | Post 16
🤔 What if the customer is asking for the wrong solution?
Let me share a situation that many Business Analysts might have experienced.
A customer approaches the team and says:
“We need a dashboard to track our failed transactions.”
The team starts discussing the dashboard design, required fields, reports, filters, and timelines.
Everyone is focused on delivering what the customer requested.
But wait a minute. 👇
Have we really understood the customer's problem?
What if the actual issue is not the lack of a dashboard?
What if hundreds of transactions are failing every day because of incorrect partner configurations, mapping errors, or missing validations?
A dashboard might help the customer see the problem. But will it actually help them solve the problem?
This is where I believe AI can make a real difference in Business Analysis.
Instead of immediately converting the customer's request into requirements, a BA can use AI to explore some deeper questions:
🔹 What is causing the transactions to fail?
🔹 Are there recurring patterns in the failures?
🔹 Which failures consume the most operational effort?
🔹 Can some errors be prevented before transactions are processed?
🔹 Can we automate error classification and suggest corrective actions?
🔹 Would improving the existing process deliver more value than building a new dashboard?
Of course, AI cannot simply assume the answers are correct. The BA must validate the findings using actual data, technical expertise, and discussions with the customer and delivery team.
But imagine the conversation changing from:
Customer: “Can you build us a dashboard?”
BA: “Yes, we can. But based on what we have discovered, there may also be an opportunity to reduce the failures themselves. Can we explore both options before deciding?”
Now the BA is not just documenting requirements.
The BA is helping the customer think about the business problem differently.
And there is another benefit.
The company may be able to deliver a better solution using its existing capabilities, reduce unnecessary development, and create more value for the customer.
That is a win for the customer and the service provider.
💡 My takeaway:
A good BA understands what the customer asks for.
A great BA investigates why the customer needs it.
An AI-enabled BA can go one step further by identifying patterns, exploring alternatives, and helping people make better-informed decisions.
AI should not replace the Business Analyst's thinking. It should help the BA ask better questions.
What is your opinion?
Have you ever seen a situation where the customer requested one solution, but the actual business problem required something completely different?
I would love to hear your experiences in the comments. 👇
#BusinessAnalyst #ArtificialIntelligence #BusinessAnalysis #DigitalTransformation #RequirementsEngineering #AIinBusiness
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
Saturday, October 10, 2026
Day 12 : AI Can Help the BA Find Business Solutions Inside the Company
Day 12 — AI Can Help the BA Find Business Solutions Inside the Company
Sometimes the customer requirement exposes an internal company problem.
For example:
Customer onboarding takes 20 days.
Investigation shows:
Manual partner configuration
Manual mapping requests
Multiple approvals
Manual testing
Manual deployment
Manual validation
The customer requirement has revealed an internal operational problem.
The BA can propose:
Partner Onboarding Automation
Potential capabilities:
Partner registration
→ Configuration
→ Mapping
→ Validation
→ Testing
→ Approval
→ Deployment
→ Monitoring
AI can help analyze the existing process and identify automation opportunities.
So the BA becomes a bridge between:
Customer Problems + Company Problems + Technology Solutions
hashtag#BusinessAnalyst hashtag#AI hashtag#Automation hashtag#ProcessImprovement hashtag#B2BIntegration
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
Thursday, October 8, 2026
Day 15 — The AI-Powered BA of the Future
Day 15 — The AI-Powered BA of the Future
The BA of the future will not be judged only by:
❌ How many requirements they documented
❌ How many BRDs they created
❌ How many meetings they attended
Instead, organizations will increasingly ask:
✅ Did you understand the real customer problem?
✅ Did you identify missing requirements?
✅ Did you identify risks early?
✅ Did you map requirements to company capabilities?
✅ Did you control scope?
✅ Did you explain cost and timeline transparently?
✅ Did you identify better solutions?
✅ Did you improve customer outcomes?
✅ Did you protect company profitability?
✅ Did you deliver measurable business value?
AI can help with almost every analytical step.
But one capability remains fundamentally human:
Business judgment.
The future isn't:
AI replaces Business Analysts.
It is:
AI-powered Business Analysts outperform traditional Business Analysis processes.
hashtag#AI hashtag#BusinessAnalyst hashtag#FutureOfWork hashtag#BusinessTransformation hashtag#DigitalTransformation
Wednesday, October 7, 2026
Tuesday, October 6, 2026
Monday, October 5, 2026
Sunday, October 4, 2026
CHAT GPT 6 ASTRA - Introduction - continuation
Saturday, October 3, 2026
How can AI help an EDI Validator validate messages against a customer’s specific requirements?
🚀 How can AI help an EDI Validator validate messages against a customer’s specific requirements?
I am trying to give explanation in more simplest way.
Traditional EDI validation usually checks whether an EDI message follows the standard X12 or EDIFACT syntax.
But in real-world B2B integration, that is only part of the problem.
A message can be technically valid EDI and still be wrong for a specific customer.
For example:
✅ The EDI 850 structure is valid
✅ Required segments are present
✅ Data types are correct
✅ Element lengths are correct
but the customer may have additional requirements such as:
❌ PO Number must follow a specific format
❌ Certain product codes are mandatory
❌ REF qualifiers must contain specific values
❌ Some segments are required only for particular order types
❌ Quantity/UOM combinations must follow customer rules
❌ Certain elements have customer-specific code lists
❌ Business rules may depend on values in other segments
Where AI can help 🤖
The EDI Validator can use AI to understand the customer implementation guide/specification and convert those requirements into machine-readable validation rules.
The flow could look like this:
Customer Specification / Implementation Guide
⬇️
AI Document Understanding
⬇️
Extract Segments, Elements, Qualifiers & Business Rules
⬇️
Create Customer-Specific Validation Rules
⬇️
EDI Message Validation
⬇️
Compare EDI against Customer Specification
⬇️
Detailed Validation Report
The AI doesn't need to replace deterministic EDI validation.
Instead, the best approach is to combine both:
Rules Engine + AI = Intelligent EDI Validation
The rules engine performs precise checks such as syntax, mandatory segments, data types, code values and relationships.
AI can help interpret complex customer specifications, identify business rules, explain validation failures and assist in generating customer-specific rules.
Example
Suppose a customer's specification says:
"For domestic orders, REF*IA must contain the customer's internal vendor number."
The EDI Validator could identify this requirement from the implementation guide and validate an incoming 850 accordingly.
If the message contains:
"REF*IA*12345"
the validator checks whether "12345" satisfies the customer's defined requirement.
If it fails, instead of simply saying:
"EDI validation failed."
the system could provide:
🔴 Customer Specification Violation
Segment: REF
Qualifier: IA
Issue: Vendor number does not match the customer's defined format/value requirement.
Specification Reference: Customer REF/IA rule
Recommended Action: Verify the vendor number provided in the REF*IA segment.
This makes the validator much more useful to EDI developers, B2B teams and business analysts.
hashtag#EDI hashtag#EDIVAlidator hashtag#AI hashtag#ArtificialIntelligence hashtag#B2BIntegration hashtag#EDIIntegration hashtag#X12 hashtag#EDIFACT hashtag#EDI850 hashtag#B2B hashtag#Integration hashtag#EDIStandards hashtag#SupplyChain hashtag#DigitalTransformation hashtag#GenAI hashtag#AIAutomation hashtag#BusinessIntegration hashtag#EDIConsulting hashtag#IntegrationArchitecture
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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