Executive Summary
A leading UK-based aluminum casting company, renowned for its technical expertise and defect-free products, faced a critical bottleneck: a slow, resource-intensive quotation process. With only a 5-6% conversion rate from over 100 monthly inquiries, the company was losing valuable business and tying up its most experienced personnel. We partnered with them to develop a custom AI-powered quote generator. The result? A quotation process that now takes a few hours instead of weeks, freeing up a significant number of valuable resources and dramatically improving their ability to secure new high-value contracts.
Customer Profile
Industry: Aluminium sand & gravity die casting (low–mid volume), CNC finishing
Market: Industrial machinery, electrical housings, safety-critical components
The Pre-AI Quotation Process (As-Is)
Anchored to your reference “Typical quotation process”:
RFQ → Design Review → Process/Alloy Selection → Costing → Quote → Review/Acceptance. Bottlenecks included manual DFM checks, tribal-knowledge pricing, ad-hoc simulation, and Excel cost builds.
UK peers echo parts of this flow: investment-casting simulation before quoting and formal RFQ review prior to submission.
1. The Challenge: A Quote Process That Was Stifling Growth
The company’s core strength lay in its experienced designers and engineers, who could deliver complex, custom components with exceptional quality. However, their quotation process was still mired in manual, labor-intensive work. This traditional workflow created several critical pain points that limited their potential.
The typical quotation process began with a client sending an RFQ, including 2D drawings and design specifications. The company’s design team would then manually review the drawings to check for manufacturability, a task that required deep technical knowledge and a lot of time.
The pain points were clear:
- Massive Time Sink: The entire manual quotation process took two to three weeks to complete. With a high volume of approximately 100 unique RFQs per month, this created a severe backlog.
- Low Conversion Rate: Despite a high volume of inquiries, the company was only converting about 5-6% of these into paying customers. This low success rate meant that a vast amount of time and effort was being spent on quotes that never became orders.
- Resource Drain: The process required the direct involvement of senior personnel, including designers, quality control teams, and even the CEO, who were pulled away from critical, revenue-generating tasks to handle repetitive quotation work.
- Information Gaps and Inefficiency: The manual process involved multiple back-and-forth cycles with clients to clarify missing information and address feasibility issues. This was a slow, multi-stage process involving spreadsheets and email exchanges.
2. The Solution: Our AI Quote Generator, an AI Powered Application
We developed an enterprise-grade AI solution that seamlessly integrated into the company’s existing workflow. Utilizing an AI agent, the application was trained to act as a virtual assistant, automating the most time-consuming steps of the quotation process. This AI application was not built from scratch; rather, it was a tailored solution that leveraged the power of existing Large Language Models, guided by the company’s unique expertise.
The AI’s knowledge base was built using a “rule book” created by recording the company’s senior designers as they analyzed drawings. This captured their specific thought processes, technical limitations (e.g., minimum wall thickness), and proprietary design principles. This ensured the AI consistently delivered outputs that aligned with the company’s high standards.
3. The AI-Powered Process: From Weeks to Hours
Our custom AI Quote Generator transformed the traditional process, creating a streamlined, efficient, and intelligent new workflow.
- Step 1: Automated Drawing Analysis & Data Extraction A 2D drawing (PDF) is uploaded to the AI powered application. The AI instantly analyzes the drawing, extracting all key information such as dimensions, part name, drawing number, material grade, and surface finish. This replaces the need for proprietary software and saves hours of manual data entry.
- Step 2: Instant Query Generation The AI cross-references the drawing data against the company’s “rule book.” Company’s rule book typically includes all documents, drawings, parameters, static and dynamic data being used to prepare quotation in legacy methods. In seconds, it generates a comprehensive list of all missing or ambiguous information, such as an unspecified material grade or unclear dimensions. This list can be sent directly to the client, drastically shortening the first interaction cycle.
- Step 3: Intelligent Feasibility Analysis Once initial queries are addressed, the AI performs a detailed feasibility analysis. It checks the component’s design against the firm’s specific technical limitations, such as complex patterns or specific angles that may be unmanufacturable with existing machinery. The AI produces a detailed feasibility report, allowing for rapid decision-making.
- Step 4: Automated, Real-Time Quotation Generation With all necessary information, the AI proceeds to the core task. The user can simply prompt the AI, for example, “Create a quote for 5000 units. Use current raw material prices in any given time and region.” The AI then:
- Calculates the component’s finished weight for shipping.
- Fetches real-time market data for raw aluminum prices.
- Computes the costs for materials, tooling, production, labor, secondary processing, and inspection. The AI compiles a complete, itemized quotation within a couple of hours, cutting down a weeks-long process into a single afternoon.
- Step 5: Strategic Business Analysis In a unique and powerful step, the AI can also be prompted to provide strategic business recommendations. By analyzing the client’s public profile, the AI can assess their potential long-term value and recommend an aggressive, neutral, or non-aggressive quoting strategy. This provides the CEO with data-driven insights to make high-level decisions.
4. The Results: Quantifiable Success
The implementation of our AI quote generator provided immediate and dramatic benefits that directly impacted the company’s bottom line.
- Time Savings: The quotation process, which once took 2-3 weeks, was reduced to a few hours. This speed allows the company to respond to RFQs faster, a key competitive advantage in the manufacturing industry.
- Resource Optimization: Senior designers, engineers, and even the CEO were freed from repetitive, low-value quotation tasks. They could now focus their time and expertise on active projects and strategic initiatives, significantly boosting productivity.
- Increased Win Rates: By providing faster and more accurate quotes, the company could bid on more projects and win a higher percentage of them. The ability to quickly turn around a comprehensive, data-backed quote positions them as a responsive and innovative partner.
- Business Empowerment: The AI application empowered non-designer personnel with some technical knowledge to generate comprehensive quotes, requiring only a final review from the CEO. This decentralization of the process dramatically increases efficiency.
Conclusion
This case study is a testament to the transformative power of industrial artificial intelligence when applied to a specific, critical business problem. By turning an inefficient, manual process into a streamlined, AI-driven one, the aluminum casting company not only optimized its internal operations but also positioned itself to secure more high-value contracts and drive significant revenue growth. This is more than just a case study; it’s a blueprint for any manufacturing company looking to gain a competitive edge in a rapidly evolving industry.
This transformation shows what happens when manufacturing meets AI. If your business is struggling with slow, resource-heavy quotation cycles, it’s time to explore a custom AI quote generator tailored to your processes. Talk to an expert now.