Improving Acquisition Processes through Machine Learning and Automation

TeamWorx Security will adapt Hive-IQ®, our collaborative and intelligent workflow management technology, to increase the efficiency, speed, and accuracy of the acquisition process from requirements definition through contract award while leveraging the use of artificial intelligence (AI), machine learning (ML), and automation. TeamWorx Security will configure our existing Hive-IQ® technology to 1) reduce pre-contract award failures; 2) define user or customer requirements; 3) assess competition and pricing between offerors; 4) enable more efficient source selection processes; and 5) validate contract submissions against the Federal Acquisitions Regulations (FAR) and Defense Federal Acquisitions Regulations (DFAR) manuals. We will use Hive-IQ® to seamlessly train entry-level personnel to perform at more senior and experienced levels, improving staff retention, knowledge management, and job performance. We will use Natural Language Processing (NLP) to automatically evaluate proposals in comparison to regulations and RFP requirements. We have designed an automated process that leverages NLP to extract relevant details from large textual sources and auto-generate visual relationships. This process has been proven to decrease by 28% the amount of time required for users to generate conclusions and share information by transferring the repeatable and tedious tasks from the human to the technology. Hive-IQ® empowered by NLP reduces the amount of time for users to sift through mountains of information from hours to minutes, directly improving the acquisition process to create a more efficient working environment for users. Results of our automated processing can then be provided to Technical Review Boards (TRBs) for discussion and review. The resulting TRB recommendations along with the analytic outputs will then be provided to the Contracting Officer (KO) for final determination of contract award(s), maximizing the efficiency of the pre-award acquisition process.