Automating public tender responses with intelligent AI agents
A sophisticated RAG-powered platform that transforms complex procurement questions into accurate draft responses.
To Adjust
To Adjust approached SevenLab to revolutionise the 'Nota van Inlichtingen' process within Dutch public tenders. The objective was to create a tool that automatically generates draft answers to supplier questions by synthesising information from current tender documents and a vast library of historical procurement data.
Public sector procurement teams are often overwhelmed by hundreds of technical, legal, and functional questions during a tender procedure. Manually drafting these responses is repetitive and error-prone, especially when many answers could be derived from previous projects. Ensuring consistency across different roles—such as buyers, legal experts, and project leads—presented a significant operational bottleneck.
A look inside
What SevenLab built
SevenLab developed a custom AI platform using Retrieval-Augmented Generation (RAG) to process current tender documents alongside a historical knowledge base. The system allows teams to generate role-specific drafts with full transparency, including certainty scores, source citations, and logical argumentation for every answer.
RAG knowledge base
Integrates historical tender memos to ensure AI responses align with previously successful procurement logic.
Role-based generation
Tailors draft answers specifically for the unique perspectives of buyers, legal advisors, or project managers.
Certainty and traceability
Each answer includes a confidence score and direct links to the source documents used for generation.
Prompt versioning control
A sophisticated configuration layer allows administrators to manage and optimise AI instructions per organisation.
Measurable business impact
The platform has significantly reduced the administrative burden on procurement teams, allowing them to move from drafting to reviewing. By providing high-quality concept answers based on verified historical data, the consistency and legal robustness of tender responses have improved across the board.
The AI's ability to instantly reference our historical data has transformed how we handle complex tenders. It allows our team to focus on reviewing and perfecting answers rather than starting from a blank page.
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