1. Overview
To streamline the process of creating structured business proposals, we developed an in-house AI-based quotation generator. This system leverages Anthropic Claude Desktop and Model Context Protocol (MCP) to automatically generate comprehensive, customized proposals based on internal templates and approved web references.
2. Problem Statement
Previously, qotation creation previously required manual assembly of deliverables, formatting across departments, and validation against multiple reference sources. This resulted in inconsistent structure, long approval cycles, and avoidable rework.
- Inconsistent document structure across departments
- Extended turnaround times for quotation approvals
- Difficulty in maintaining content accuracy and standardization
3. Proposed Solution
The solution was an AI-based automated quotation generator, deployed within a secure cloud desktop environment and powered by Claude Desktop with Model Context Protocol (MCP) integration. Through a combination of form-based input, context-aware data retrieval, and automated content generation, our solution efficiently produced quotations ready for internal review and client delivery.
- Secure Access: Access local files and departmental templates securely through MCP
- Context Retrieval: Retrieve contextual information from approved web sources
- Quotation Generation: Generate structured quotations dynamically based on user-defined parameters
4. System Architecture
The system follows a simple four-stage workflow: user input, secure context retrieval, AI-driven document generation, and controlled storage of draft quotations for review and editing.
- User Interface Layer
A form-driven interface within Intropic Cloud Desktop where users input deliverables, structure preferences, and reference sources. - Model Context Protocol (MCP) Layer
Provides secure, context-rich access to local documents, databases, and external reference sites. MCP ensures the model processes all sources cohesively. - AI Processing Engine (Claude Desktop)
Analyzes structured inputs, extracts relevant data, and composes complete quotations based on internal templates and retrieved context. - Data Storage and Output Layer
Stores generated quotations, supporting documents, and deliverable data either locally or in the organization’s secure cloud repository for review and final editing.
5. Implementation Highlights
- Seamless Cloud Desktop Integration: Deployed within Intropic Cloud Desktop, ensuring secure access to both cloud and local data sources.
- Model Context Protocol (MCP) Utilization: Enabled context portability, allowing Claude Desktop to interpret and merge data from multiple environments efficiently.
- Automated Module Recognition: The AI automatically identified and extracted relevant content modules from web references and local resources for inclusion in quotations.
- Dynamic Deliverable Structuring: Generated quotations followed pre-defined departmental formats, including deliverables, cost breakdowns, and timelines.
- Prompt-Based Customization: Users could enhance results by providing targeted prompts or adding modules, resulting in highly tailored quotation outputs.
6. Results and Benefits
The system reduced manual quotation drafting effort by approximately 70%, standardized document structure across departments, and significantly shortened turnaround times. Teams reported improved accuracy and higher confidence in proposal quality, while freeing time for higher-value work
- Drafting Efficiency: 70% reduction in manual drafting effort for quotations and proposals
- Document Consistency: Consistent formatting and structure across all generated documents
- Faster Turnaround: Accelerated quotation turnaround, improving response time to client requests
- Data Accuracy: Higher accuracy and relevance, due to contextual data integration and validation
- Productivity Boost: Increased staff productivity, freeing teams to focus on strategic or creative tasks
7. Conclusion & Future Scope
The implementation of the AI-based automated quotation generator marked a major advancement in document automation strategy, as integrating Claude Desktop with Model Context Protocol (MCP) within the Intropic Cloud Desktop unified local and web data into a cohesive, intelligent workflow, enhancing operational efficiency while laying the groundwork for future innovations like AI feedback loops, advanced quotation analytics, and real-time multi-user collaboration.
At Gravity Base, our AI team builds internal automation systems like this to reduce operational friction and improve consistency across business workflows.