Customer Goals:
A custom recreational vehicle manufacturer struggles with inefficient scheduling processes using SaaS and a home-grown ERP. This leads to missed deadlines, high costs, and underutilized resources. AI-driven scheduling can improve resource allocation, reduce downtime, and better align production with demand, making the company more competitive.
Technical Needs:
The client's production scheduling issues arise from manual processes using disparate systems (spreadsheets, email, SharePoint, SaaS) causing missed optimizations, delays, and revisions. Data silos hinder holistic analysis of demand, supply chain, inventory, and machinery data. Static, historical-data-based decisions ignore real-time changes (disruptions, urgent orders, maintenance). Reactive maintenance causes unplanned downtime. Demand and production aren't dynamically integrated, leading to overproduction or stockouts.
Customer Goals:
A custom recreational vehicle manufacturer struggles with production deadlines and costs due to inefficient scheduling from disparate SaaS workflows and a rigid ERP system. This leads to underutilized resources and increased expenses. AI-driven scheduling could improve resource allocation, reduce downtime, and better align production with demand, making the company more competitive.
Technical Needs:
The client's production scheduling issues result from manual processes using disparate systems (spreadsheets, email, SharePoint, multiple data entry points), creating data silos and hindering holistic analysis. Scheduling is based on static rules, ignoring real-time changes. Reactive maintenance causes unplanned downtime, and demand forecasts aren't integrated, leading to overproduction or stockouts.
Customer Goals:
A mid-sized fintech company faces challenges scaling its payment processing platform to meet increasing transaction volumes, ensure global compliance, and maintain data security. Their current system has prolonged development cycles, operational inefficiencies, and limited scalability, hindering business agility. Addressing these issues is critical for growth, cost reduction, and maintaining customer trust. Success depends on streamlining application development and ensuring robust, scalable cloud infrastructure without compromising compliance or security.
Technical Needs:
The customer needs improved process efficiency and scalability to stay competitive. Key needs include automated CI/CD, cloud-native scalability for high transaction volumes, and enhanced security compliance.
Type of | Examples |
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Project Types | Consulting Development Ongoing support |
Expertise | General programming Domain / Industry Data Integration Language specifics |
Contract Types | Time & Materials Time & Materials with Minimum Managed Services Support SLAs |
Type of | Examples |
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Project Types | MLOps Platform Development Operational Efficiency Reinforcement Learning Consulting Computer Vision Chatbot & Virtual AssistantAI-Enabled IoT SolutionsModel DevelopmentLLM * RAG |
Expertise | Data Engineering MLOps Model Pipeline ML Engineering AI Application Development Data Integration |
Contract Types | Time & Materials Time & Materials with Minimum Experiment Design |