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Windchill CustomizationServer-Side Java6-month engagement

Custom Engineering Change Workflow with Server-Side Java Automation

A manufacturer running Windchill PDMLink 12.1 had a rigid out-of-the-box ECO workflow that could not handle their multi-tier approval logic, conditional routing by part classification, or automatic ERP notification on release.

المشكلة
Standard Windchill workflow templates could not support conditional branching based on part type, cost impact threshold, or affected assembly level. Approval routing was done manually by email outside the system, with no audit trail.
الحل
Built a custom WorkflowProcessor in Java that reads part classification attributes and change impact metadata to auto-route approvals. Implemented OperationDelegate validators blocking promotion without mandatory sign-offs. Added a server-side event listener posting ERP notifications on lifecycle state change.
التقنية
Windchill 12.1 Java API, WTMethodServer, WorkflowProcessor, OperationDelegate, xconf configuration, Windchill event framework, REST webhook to ERP
100%
Automated approval routing
3d→4h
Average ECO cycle time
0
Manual admin interventions/month
Full
Audit trail in Windchill
← ناقش مشروعاً مماثلاً
Windchill CustomizationBOM / EBOM5-month engagement

EBOM Integrity Validators and Bulk Data Utilities for BOM Restructuring

An industrial equipment OEM had accumulated years of inconsistent EBOM data in Windchill: parts with missing attributes, broken component links, duplicate part numbers across product families, and BOMs that had never been formally validated before release.

المشكلة
No server-side validation enforced completeness before BOM release. Engineers could promote parts and BOMs with missing weight, material, or drawing references. Over 14,000 parts had at least one missing mandatory attribute.
الحل
Developed a set of server-side Windchill validators using ValueRequired constraints and custom OperationDelegate classes that block BOM promotion when mandatory attributes are absent. Built headless Java data utility scripts for bulk attribute repair, part deduplication, and BOM structure normalization.
التقنية
Windchill Java API, OperationDelegate, ValueRequired constraints, WTPart/WTPartMaster subtypes, IBA attribute framework, headless Java batch utilities, Oracle JDBC
14K
Parts cleaned in initial run
0
MES import failures post-deployment
2d→0
Weekly manual repair effort
100%
BOM completeness at release
← ناقش مشروعاً مماثلاً
AI IntegrationRAG / Knowledge System4-month engagement

RAG-Based Engineering Knowledge Assistant Connected to Windchill Document Library

An industrial OEM had 12 years of engineering documentation in Windchill that was effectively unsearchable. Engineers spent significant time hunting for historical decisions, often repeating work that had already been done.

المشكلة
Windchill full-text search returned only metadata matches. The actual content of 600K+ PDF and Office documents was inaccessible. Engineers queried colleagues instead of the system, creating knowledge bottlenecks around senior staff.
الحل
Built a RAG pipeline that indexes Windchill document content into a vector database via the Windchill REST API and Apache Tika for document parsing. Engineers query in natural language and receive answers with citations linking back to source documents in Windchill.
التقنية
Windchill REST API, Apache Tika, OpenAI GPT-4o, text-embedding-3-large, Pinecone vector database, FastAPI backend, React frontend, SAML SSO integration
600K+
Documents indexed
45m→5m
Average document search time
92%
Answer relevance score (UAT)
4 mo
Proposal to production
← ناقش مشروعاً مماثلاً
AI IntegrationWhatsApp Automation3-month engagement

WhatsApp Business Automation with AI-Powered Auto-Confirmation and Order Routing

A B2B supplier was managing customer orders, delivery confirmations, and technical queries entirely through a shared WhatsApp Business account. The volume had grown to 200+ messages per day.

المشكلة
No automation on the WhatsApp channel meant every message required manual reading and response. Order confirmations, delivery status requests, and standard product queries all consumed the same human attention as complex issues.
الحل
Built an AI agent on the WhatsApp Business API that classifies incoming messages, handles routine confirmations and status queries automatically, and escalates non-standard requests to the right person with full context.
التقنية
WhatsApp Business API (Cloud), OpenAI GPT-4o, LangChain agent framework, webhook integration with ERP, message classification pipeline, Python FastAPI, Redis for session state
78%
Messages handled automatically
4h→2m
Response time for routine queries
3h
Daily staff time recovered
100%
Order confirmations automated
← ناقش مشروعاً مماثلاً
AI IntegrationCRM / VOIP5-month engagement

CRM Synchronization via VOIP Call Detection and AI-Powered Transcript Processing

A technical sales team was losing critical information between phone calls and their CRM. Call notes were either not logged or logged inconsistently hours after the fact.

المشكلة
VOIP calls happened outside the CRM with no integration. Salespeople manually typed call summaries with low compliance. Critical customer data—pricing discussed, technical requirements, delivery commitments—was not captured in the CRM.
الحل
Integrated VOIP webhook hooks to detect call start/end events and trigger automatic recording transcription. An AI pipeline processes each transcript to extract key entities (contacts, products, prices, commitments, next steps) and writes structured data back to the CRM via API.
التقنية
VOIP webhook API (Twilio / 3CX), OpenAI Whisper for transcription, GPT-4o for entity extraction, CRM REST API (HubSpot / Salesforce), Python processing pipeline, PostgreSQL for call archive
100%
Calls logged automatically
90s
CRM update after call end
0
Manual note entry required
Increase in CRM data completeness
← ناقش مشروعاً مماثلاً
InfrastructureServer Configuration2-month engagement

Production AI Infrastructure: Containerized Deployment with GPU Inference, Auto-Scaling and Observability

A software company preparing to launch an AI product had built their solution on local dev machines and a single cloud VM. They had no deployment pipeline, no container strategy, no observability, and no plan for handling traffic spikes.

المشكلة
No separation between dev and prod environments. API keys stored in plain text. A single Python process serving all requests with no queuing or load balancing. Model inference running on CPU with 8-second response times.
الحل
Designed and deployed a production-grade infrastructure stack: Dockerized FastAPI services on Kubernetes, GPU node pool for inference, NGINX ingress with rate limiting, Redis queuing, Prometheus/Grafana monitoring, and a CI/CD pipeline via GitHub Actions.
التقنية
Docker, Kubernetes (GKE), NGINX ingress, Redis, Celery workers, PostgreSQL, Prometheus, Grafana, GitHub Actions CI/CD, HashiCorp Vault, NVIDIA A100 GPU node pool, Terraform
8s→0.4s
Inference response time
99.95%
Uptime SLA achieved
Auto
Scales 1 to 40 pods on demand
60%
Cost reduction vs. initial setup
← ناقش مشروعاً مماثلاً
Voice AIGemini TTS / OpenAI4-month engagement

Real-Time Voice Assistant with Gemini TTS, OpenAI Reasoning and Optimized Token Management

A client needed a production voice assistant that could hold multi-turn technical conversations, respond in natural speech under 1.5 seconds, and operate at scale without runaway API costs.

المشكلة
Naive implementation passed the full conversation history to GPT-4o on every turn, causing exponential token growth, high latency, and monthly API costs that made the product economically unviable. TTS responses had 3–4 second delays.
الحل
Built a hybrid voice pipeline: OpenAI Whisper for STT, GPT-4o for reasoning with a custom memory layer, and Google Gemini 2.0 Flash TTS for low-latency speech synthesis. Implemented a sliding window memory system with semantic summarization of older turns.
التقنية
OpenAI Whisper (STT), GPT-4o (reasoning), GPT-4o-mini (summarization), Google Gemini 2.0 Flash (TTS), LangChain memory with custom summarizer, WebSocket streaming, Redis session store
1.1s
End-to-end voice response time
85%
Reduction in token usage vs. naive
Unlimited
Conversation turns with stable context
Lower monthly API cost
← ناقش مشروعاً مماثلاً

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