01 — CASE STUDY
2026 — Building contest France 2026
01 - The problem
Tender opportunities often involve large, complex documents and strict compliance requirements. Small teams can spend hours reviewing a single tender before knowing whether it is relevant, realistic, or worth pursuing. The challenge was to reduce this early-stage effort while helping users make more informed bidding decisions.
02 — My response
Tender Copilot gives users a central workspace to analyse tender opportunities and decide where to focus their time. Using AI, the platform can extract key requirements from tender documents, identify potential risks, and support a structured GO / NO-GO decision before a team invests heavily in writing a response.
03 — DAshboard
I designed the dashboard as the main entry point to give teams an instant overview of their tender pipeline. It brings active opportunities, approaching deadlines, AI fit estimates, and proposal progress into one decision-making space.
04 - Key Design Decisions
Dashboard-first experience: Active tenders, deadlines, priorities, and progress are visible immediately, helping users focus on urgent opportunities.
AI-assisted analysis: Rather than asking users to read every page manually, the interface surfaces key requirements and decision factors in a structured format.
GO / NO-GO decision support: The product turns early evaluation into a faster, more deliberate step by highlighting fit, risk, and effort.
Guided proposal preparation: Once a tender is selected, users can move from analysis to building a technical response with a clearer framework.
Information hierarchy: Critical content—such as deadlines, mandatory criteria, and missing elements—is prioritised to reduce cognitive load.
05 — Outcome & learning
Tender Copilot demonstrates how AI can support professional judgment instead of replacing it. The product helps teams spend less time searching through unstructured documents and more time deciding which tenders deserve a strong, tailored response. This project also developed my ability to design AI-assisted B2B workflows where clarity, traceability, and user control are essential.
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