🚀 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

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 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 

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 

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 

Behind the EDI Validation Suite. ----- Idea Main Reason

 

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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