RFQ Discovery Constraints in DIBBS and Tooling Evaluation
The primary constraint I am encountering is the reliable identification of targeted, active RFQs with sufficient temporal margin between the Issue Date and the Return Date to support compliant sourcing, pricing, and logistics validation. My prior reliance on a scraping-based solution proved insufficient due to structural limitations within DIBBS, specifically, the absence of a public API, frequent session volatility, dynamic page rendering, and multiple cybersecurity and access-control layers that inhibit consistent data acquisition and normalization. These factors introduce unacceptable latency, incompleteness, and data integrity risk into the opportunity-identification workflow. To address this, I am evaluating Loocey as a short-term tooling solution based on its apparent ability to surface time-sensitive RFQs more effectively within these constraints and intend to conduct a one-month functional assessment. In parallel, I am currently on the Contrax AI waitlist and anticipate its deployment as a potentially more robust solution for RFQ filtering, prioritization, and decision support once available.