Manufacturing AI Consulting and Development Company

Our experienced consultants collaborate closely with you to understand your specific needs, challenges, and objectives. By conducting a thorough examination of your existing processes and workflows, we identify opportunities for improvement and innovation. Leveraging industry expertise and best practices, we recommend optimal technologies and solutions to optimize manufacturing operations and guide you through a structured implementation roadmap.

We begin by refining your product concept, evaluating market demands, feasibility and potential challenges. Our process involves creating a Proof of Concept (PoC) to validate core functionalities and key assumptions. Once validated, we progress to developing a Minimum Viable Product (MVP), creating a functional prototype for real-world testing. Through iterative improvements based on user feedback, we transform your idea into a robust, market-ready AI solution.

We specialize in fine-tuning Large Language Models (LLMs) for manufacturing applications, enhancing their performance. By fine-tuning these models to your specific manufacturing processes and datasets, we create AI solutions that understand industry-specific terminology, interpret complex production data, and generate actionable insights. Our tailored approach ensures the LLMs effectively support your operational goals and decision-making processes.

We utilize advanced proprietary and open-source LLMs like GPT, Llama, PaLM, Gemini, Mistral, and Claude to create robust GenAI manufacturing solutions tailored to your production environment. Our expertise includes developing intelligent virtual assistants, advanced process optimization systems, predictive quality control systems and more. These solutions enhance operational efficiency, streamline decision-making, and drive innovation in your manufacturing processes.

We build intelligent AI agents and copilots using leading tools such as AutoGen Studio, Copilot Studio, Vertex AI, and crewAI. Harnessing the capabilities of advanced LLMs alongside a robust skills library, our AI agents excel in automating workflows, analyzing manufacturing data and enhancing customer support. They are crafted to meet the specific operational demands of manufacturing, enhancing efficiency, decision-making and innovation across your production processes.

Our data engineering services cover the entire data lifecycle, including sourcing, cleansing, structuring, and enrichment. We specialize in data mining, analysis, annotation, and labeling, crucial for developing robust AI models. Leveraging advanced techniques and tools, we optimize your manufacturing data for machine learning, facilitating precise and dependable model training processes.

We develop AI-powered MES to optimize manufacturing processes through comprehensive documentation, tracking, and monitoring of the product lifecycle. Our MES solutions enhance production genealogy, minimize paperwork, optimize inventory, increase operational uptime, and improve quality control. These capabilities are designed to foster efficient and streamlined manufacturing operations, thereby boosting productivity and ensuring consistency in production outcomes.

We create Quality Management Systems (QMS) to help manufacturing companies deliver quality processes efficiently and consistently while ensuring compliance with regulatory standards. Our QMS solutions offer benefits like supplier management, improved internal communication, and better product assessment, ensuring high-quality outcomes and operational excellence.

We specialize in developing CPQ solutions that leverage AI to automate the generation of accurate quotes for highly configurable items based on predefined rules. This streamlines pricing, promotes product customization, and speeds up the quoting process, ultimately boosting customer satisfaction and operational efficiency within manufacturing environments.

Our custom-built CRM solutions are designed to boost customer engagement, enhance sales and customer support team performance, and boost sales. Our software includes features like marketing tools, performance monitoring, workflow optimization, and sales monitoring. These tools help streamline customer interactions, drive revenue growth, and improve overall customer experience.

We develop predictive maintenance software harnessing AI and ML algorithms, empowering manufacturing companies to schedule maintenance and predict machinery failures. Our solution includes features like preparing maintenance schedules, tracking maintenance history, issuing alerts, and scheduling recurring services. These capabilities help minimize downtime, extend equipment life, and optimize maintenance operations.

We develop AI-enabled condition monitoring software that collects equipment data from integrated sensors, analyzes it in real-time, and generates actionable insights. Our software monitors equipment health, detects irregularities, and triggers alerts for proactive maintenance. These capabilities enhance proactive maintenance strategies and significantly improve overall equipment performance.

Our solutions are highly customizable and scalable, empowering manufacturers to adapt the software to specific needs and seamlessly scale operations as their business evolves.

Designed with a user-friendly interface, our AI solutions cater to users of all technical proficiencies, ensuring ease of learning and navigation for enhanced usability.

Our solutions integrate seamlessly with existing ERP, CRM, and other systems, providing a unified operational view and facilitating streamlined data access across platforms.

We bring deep expertise in manufacturing processes, regulatory compliance, and the application of cutting-edge technologies such as AI/ML, generative AI, NLP, and more. Our team understands the industry’s intricacies, ensuring tailored solutions that effectively address specific manufacturing challenges.

At LeewayHertz, quality is at the forefront of everything we do. We adhere to rigorous quality assurance processes, conduct thorough testing, and perform continuous code reviews to ensure high standards in our AI-driven manufacturing solutions.

Embracing agile development methodologies, we ensure flexibility and responsiveness in delivering AI-driven manufacturing software. By prioritizing iterative improvements, we align your solution with evolving business needs and market dynamics, fostering continuous innovation and adaptation.

• Understanding Business Goals: We clearly define the manufacturing problem the AI solution aims to address. What are the desired outcomes and key performance indicators (KPIs)?
• Data Analysis and Availability: Identify and assess the existing data sources, their quality, and availability. Are they sufficient to train an AI model?
• Technical Feasibility: Evaluate the feasibility of AI solutions based on the available resources, technology, and complexity of the manufacturing processes.
• Project Scope and Timeline: Define the project scope, deliverables, and a realistic timeline for each project phase.
• Implementation Roadmap: Develop a detailed roadmap for implementing the AI solution, outlining the key milestones and timelines.

• Data Collection: Gather relevant data from various sources, including manufacturing systems, sensors, databases, and external sources.
• Data Cleaning and Preprocessing: Cleanse and transform the raw data to address inconsistencies, missing values, and outliers.
• Feature Engineering: Based on domain knowledge and data analysis, create meaningful features that enhance the model’s performance and interpretability.
• Data Storage and Management: Establish a robust data infrastructure to store, manage, and access the training data efficiently.
• Data integration and validation: Combine data from different sources, ensuring data consistency and completeness.
• Data governance and security: Implement policies and procedures for data security, privacy, and ethical use.

• Unit Testing: Conduct unit tests on individual components of the AI solution to ensure their correctness and reliability.
• Integration Testing: Test the integration of different components of the solution to ensure seamless interaction and data flow.
• Performance Testing: Evaluate the AI solution’s performance under different conditions to ensure it meets the required speed, accuracy, and resource consumption.
• Security Testing: Assess the security vulnerabilities of the AI solution and implement appropriate measures to safeguard sensitive data and prevent unauthorized access.
• User Acceptance Testing (UAT): Involve relevant stakeholders in UAT to validate the solution’s usability, functionality, and compliance with their requirements.

• Performance Monitoring: Continuously monitor the AI solution’s performance, tracking KPIs and identifying potential areas for improvement.
• Model Retraining: Regularly retrain the AI model with new data to ensure its accuracy and performance remain consistent.
• Feedback Loop: Establish a feedback loop to gather user feedback and insights, continuously refining the AI solution and addressing emerging needs.
• Optimization and Improvement: Identify and implement optimizations to enhance the AI solution’s performance, efficiency, and user experience.