Project Vanguard*
A leading Nordic engineering consultancy sought to transform their tender response process, which traditionally required significant manual effort to evaluate tender documents against internal policy frameworks and generate compliant task solutions (oppgaveløsning). The solution: an AI-powered platform that automates document analysis, policy matching, and proposal text generation. Industry: Engineering & Infrastructure Consulting Challenge: Manual tender evaluation consuming 40+ hours per major bid Solution: AI platform for automated document analysis and proposal generation Technology: Azure OpenAI GPT-4, Azure AI Search, Semantic Embeddings Timeline: 4 development sprints (8 weeks)
The Challenge
Engineering consultancies compete for projects through formal tender processes that require detailed responses demonstrating how they will meet client requirements. This process involves: • Analyzing extensive tender documentation (often 100+ pages) • Cross-referencing requirements against internal policies and capabilities • Generating compliant task solutions that address each requirement • Ensuring consistency with previous successful bids • Quality assurance across multiple document versions For the client, each major tender response required 40-60 hours of senior consultant time, limiting the number of bids they could pursue and creating bottlenecks during peak tender periods.
Our Approach
We developed an AI-powered tender response platform with four core capabilities: 1. Intelligent Document Processing: The platform accepts tender documents in various formats and automatically extracts key requirements, evaluation criteria, and compliance points. Using Azure AI Search with semantic embeddings, documents are indexed for intelligent retrieval. 2. Policy Knowledge Base: A dedicated training document upload system allows the organization to maintain their internal policies, past successful bids, and standard response templates. These serve as the foundation for AI-generated responses, ensuring compliance and consistency. 3. AI-Powered Task Solution Generation: The core engine evaluates tender requirements against the policy knowledge base to generate draft task solutions (oppgaveløsning). The AI identifies relevant policies, extracts applicable approaches, and produces structured responses that address each tender requirement. 4. Interactive Refinement: An integrated chat interface allows users to ask questions about tender documents, request clarifications, and iteratively refine generated content. A built-in editor enables direct modification of AI-generated task solutions before export.
The Results
This AI-powered tender response platform demonstrates how generative AI can transform knowledge-intensive business processes. By combining large language models with domain-specific knowledge bases and intuitive user interfaces, organizations can dramatically improve efficiency while maintaining quality and compliance. The platform architecture is adaptable to other document-intensive workflows such as contract analysis, compliance checking, and technical documentation generation. Time per tender response, reduced from 40-60 hours to 10-15 hours. Bid capacity, ability to pursue 3x more tenders. Consistency, standardized responses aligned with policies. Knowledge retention, institutional knowledge captured and reusable.
Technology Stack
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