Automation for Bid Response Compliance

Getting What You’re Asking For!

The Problem

Evaluating bid compliance and scoring is a complex process that requires a team of individuals and a significant amount of time. As a large state, efficiency in this process is crucial while ensuring the right decision is made. AI streamlines this process with the goal of reducing manual resources while still developing a compliant high-scoring bid response.

Our AI Solution

Using CADET^SM AI, our AI-powered sidekick, Elyon Strategies can streamline the analysis of bid responses to determine their compliance and scoring by:

1. Developing a narrow AI application tailored to the client issuing the bid
2. Inputting the Request for Proposal (RFP) and the criteria for scoring
3. Assessing the bid response against the requirements and scoring criteria
4. Assigning a score tag indicating the extent to which the requirement was met

CADET^SM AI’s Bid Analysis used advanced techniques such as Machine Learning (ML), Natural Language Processing (NLP), Automation, and Machine Vision (MV) to conduct a highly efficient comparative analysis of vendor response documents and the States RFP requirements. The AI analysis identified pertinent information from extensive vendor response documents for each of the predefined requirements. The model then generates scores for how well the response meets the requirement. The result provides the reviewers defensible scoring information describing how well the response met each requirement as well as the overall general intent of the procurement document. 

CADET^SM AI performed semi-supervised analysis within hours. This automated approach significantly accelerates the process, which would traditionally take a team of reviewers days or even weeks.

The Result

A bid analysis that typically requires a large team and several weeks can now be completed in just days. This particular analysis took an over 500 page response and scored it in a long weekend.

Testimonial

 “This analysis was also completed by our 10 person evaluation team in parallel and the AI-supported solution was more objective and accurate.”