Azure AI Search with RAG integration formed the core of the intelligent search functionality. We implemented both semantic and hybrid search capabilities to handle complex RFP queries through vector-based and keyword-based retrieval simultaneously.
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Client Overview
Our client operates as a Software-as-a-Service (SaaS) platform serving both RFP issuers and responders. Their platform specializes in various procurement document types, including RFPs, RFIs, RFQs, DDQs, and cybersecurity questionnaires, and processes over 15,000 opportunities annually.
Country
USA
Industry
SaaS (ProcureTech)
Team Size
5+ Resources
Hours Invested
600+ Man Hours
Problem Statement
The company faced two critical inefficiencies - data synchronization delays causing users to work with outdated information, and the time-consuming manual process of creating individual responses to questions. These bottlenecks created accuracy issues, version control problems, productivity losses, and user frustration in the RFP management software, which directly impacted service quality and competitive positioning.
Project Description
The engagement focused on creating an AI search solution to support the client team with the RFP software, a foolproof way to generate relevant content suggestions without relying on repetitive manual drafting. They also required real-time synchronization. Every update in the content library had to be immediately available for users and internal processes.
Radixweb’s AI development team implemented semantic and hybrid search techniques to interpret context accurately and retrieve the most fitting information from existing materials. Azure AI Search was selected for its mature data handling capabilities and flexibility in managing complex query patterns.
As we integrated AI search for RFP automation, the SaaS platform could surface contextually aligned responses, and our client could establish a technical foundation capable of supporting broader automation in RFx response management.
What the Client Says
“The AI search engine made a big difference. The system has quietly improved how my team feels about their work. People aren’t bogged down by repetitive tasks. We can see boosted morale and everyday workload getting a lot more manageable.”
David Witz
Chief Operating Officer
“There’s something uniquely rewarding about working on AI projects that directly touch how people work. Designing this solution was technically demanding but being able to contribute to something that tangibly reduces effort for the client makes the hard work pay off.”
Hetal D.
Senior Project Manager at Radixweb
Project Challenges
- Semantic Search Relevance: Tuning the search algorithms to accurately match RFx questions with existing content, understand question context, and deliver highly relevant responses.
- Indexing and Retrieval Performance: Handling thousands of RFP entries with sub-second response times through schema design and query optimization.
- Dynamic Content Optimization: Newly added or updated responses must propagate correctly in real time without creating conflicts or outdated answer suggestions.
- Ambiguous or Multi-Part Questions: For RFP questions with nested requirements context-dependent phrasing, the SaaS system needs to interpret intent and prioritize content intelligently.
Key Deliverables
Full-Text Search with Semantic Ranking
The search and retrieval engine interprets the intent behind each RFP question rather than just matching keywords. As it analyzes context, responses are accurately aligned with the query.
Real-Time Indexing and Data Extraction
Content added or updated in the library becomes available immediately for the AI engine. Every suggestion is based on the latest information that users don’t need to verify manually.
Chat Model and Agent Integration
Using agentic AI solutions, our developers added a conversational layer that allows users to interact with the system. The SaaS platform provides context-aware guidance to all types of questions.
Vector and Hybrid Retrieval
The platform combines semantic understanding with traditional search techniques. We designed the RFP response software with precise retrieval capability even for complex or partially phrased questions.
Custom Scoring Profiles
We configured relevance rules to reflect the client’s priorities, weighing certain content fields higher and adjusting scores according to business requirements. Critical responses appear without manual intervention.
Applied AI and AI-Enriched Content
Existing RFP content is analyzed and enriched to improve reuse. Our AI model integrated with the RFx management software identifies gaps and recommends phrasing or structure that aligns with prior high-quality responses.
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Technology Stack
Azure AI Search
Azure Functions
We used Azure Functions to handle backend automation and integration tasks. It triggers real-time indexing when content is added or updated, manages API calls between the search service and the content library, and processes complex query logic.
Azure Storage
All structured and unstructured RFP content, attachments, files, and metadata were stored securely in Azure Storage. The system utilized Blob Storage for large documents and a structured database layer for metadata.
Solution Outcomes
Using Azure development solutions and AI for RFP response management, the company could maintain better proposal accuracy and service output, as well as improve win ratios across multiple industry bids.
Faster RFP Response Time
The client team significantly reduced the time needed to draft and review responses. AI-driven content suggestions and real-time access to updated content have enabled the client to cut RFP response times by over 90%.
Increased Output without Additional Staff
Automation of repetitive tasks, such as content retrieval, indexing, and preliminary response drafting allows the company to achieve a 66% increase in output using the same number of headcounts.
Improved Team Collaboration
The SaaS procurement platform with AI capabilities optimized content sharing, version control, change management, and workflow orchestration. Multiple team members could work on questions simultaneously.
Higher Win Rate Potential
As the RFP automation software generates more accurate and timely responses, the company can handle up to 4X more RFPs. Their clients are better positioned to submit stronger proposals and win competitive bids.
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