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How AI-assisted engineering, automated testing and development automation supported the development of four connected business portals
A manufacturing company needed a modern enterprise platform connecting order management, sales, planning and supplier collaboration.
The project evolved into four connected portals:
Order Management System
Sales & Pricing Visibility
Demand & Supply Planning
Factory & Supplier Collaboration
The development team combined conventional software engineering with AI-assisted development and automation.
The result was a development approach where AI helped reduce repetitive engineering work while developers remained responsible for architecture, business rules, security, validation and production decisions.
The company operated across several connected business functions:
These activities depended on different systems, data sources and operational workflows.
The solution consists of four connected enterprise portals.
Manages customer orders, order processing, order changes, allocations and operational workflows.
Provides sales visibility, pricing information, MAP monitoring and sales-event processing.
Supports demand and supply planning, supply exceptions, simulations, factory planning and purchase-order workflows.
Allows factories and suppliers to work with purchase orders, acknowledgements, change requests and shipping documents.
Although each portal serves a different business function, they share common enterprise capabilities including:
The objective was not to replace developers with AI.
The objective was to reduce repetitive engineering effort and allow developers to spend more time on:
AI became part of the development workflow as an engineering assistant.
It helped developers move faster from:
Human engineers remained responsible for the final implementation and business decisions.
Break requirements into smaller technical tasks and identify edge cases.
Confirm business intent and scope.
Explore APIs, data structures and implementation approaches.
Select architecture and implementation strategy.
Generate first drafts of repetitive code, models, queries and service logic.
Review, modify, integrate and approve code.
Generate and explain queries and investigate data patterns.
Validate queries against actual business data.
Explain errors and suggest possible causes and fixes.
Confirm root cause and validate solution.
Generate positive, negative and edge-case scenarios.
Validate actual business behaviour.
Prepare first drafts of technical documentation.
Verify accuracy and finalize documentation.
Assist with release checklists and preparation.
Approve production changes.
Some OMS operations could take longer than a normal API request.
The platform therefore uses asynchronous processing through Azure Service Bus.
Correlation IDs and sessions associate requests and responses.
AI-assisted engineering helped developers:
The final architecture and production implementation remained under developer control.
The OMS Audit Dashboard can contain millions of records.
This created challenges around:
The team used database optimization and caching to improve performance.
AI could assist developers by:
The final architecture and production implementation remained under developer control.
Planning and Supplier data could appear different because of:
AI-assisted analysis helped developers investigate:
The final reconciliation logic was validated against actual business data before implementation.
AI was particularly useful for expanding the number of scenarios considered during development.
Instead of testing only the successful workflow, developers could use AI to identify:
Successful ERP response, delayed response, timeout, failure response, duplicate message.
Valid acknowledgement, duplicate acknowledgement, invalid PO, unauthorized supplier.
Missing information, duplicate request, rejection, resubmission, invalid quantity.
Correct role, missing permission, wrong portal, unauthorized factory access.
Matching records, renamed vendor, duplicate PO, regional record, delayed synchronization.
AI and automation played different but complementary roles.
The project material does not identify specific AI product names, so the case study should describe the capabilities rather than inventing vendor/tool names.
Generate starting points for repetitive code, models and service logic.
Requirement analysis, technical investigation, debugging and documentation.
Generate, explain and review database queries.
Generate functional, negative and edge-case scenarios.
Explore patterns, mismatches and reconciliation approaches.
Create first drafts of technical and API documentation.
The primary benefit of AI was the reduction of repetitive engineering effort.
Developers could spend less time starting routine work from scratch and more time reviewing, validating and solving business-specific problems.
A specific 30% development-cost reduction should not be presented as a measured Simplehuman project result unless supporting project records are available.
The correct way to establish such a claim is to compare equivalent development work before and after AI adoption, including:
AI assistance was particularly valuable because the project contained multiple complex business domains.
The platform connects multiple parts of the business rather than treating them as isolated applications.
The overall platform can be represented as:
The project used AI as an assistant, not as the final decision-maker.
| Layer | Technology |
|---|---|
| Frontend |
React
19
Next.js
16
Material
UI 8
|
| Backend |
FastAPI
Python
SQLAlchemy
Alembic
|
| Database |
PostgreSQL
|
| Enterprise Data |
Microsoft
SQL Server / ERP
|
| Messaging |
Azure
Service Bus
|
| Caching |
Redis
|
| Layer | Technology |
|---|---|
| Identity |
Microsoft
Entra ID / Azure AD SSO
|
| Authorization |
Role-Based Access Control
|
| Cloud |
Microsoft
Azure
|
| CI/CD |
GitHub
Actions
|
| Monitoring |
Azure
Application Insights / OpenTelemetry
|
| Integrations |
ERP
SQL
Server
PostgreSQL
Slack
External data
sources
|
The project established a connected enterprise platform covering:
The key lesson from the project is that AI creates the most value when it becomes part of the development process, rather than being treated as a standalone tool.
For a complex enterprise platform such as Simplehuman, AI can help engineers:
Understand requirements faster, Explore technical solutions, Create implementation starting points, Analyze SQL and data, Investigate defects, Expand test coverage, Prepare documentation
Our sales manager reaches out to you within a few days after analyzing your business requirements.
Meanwhile, we sign an NDA to ensure the highest level of privacy.
Our pre-sale manager presents project estimates and an approximate timeline.
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