IT Operations Automation
HUD needed to keep housing assistance contract monitoring and renewals running despite a rigid mainframe-driven environment and government shutdown constraints, while also addressing procurement and HR bottlenecks created by complex federal regulations and legacy operating processes.
Reported impact
HUD deployed contract-monitoring automation in less than 90 days, improved operational resilience around shutdown-related service risk, and used AI/ML analysis to expose procurement and hiring performance gaps so internal teams could take corrective action on bottlenecks.
HUD implemented a two-part automation and AI/ML approach to reduce operational risk in legacy government workflows. For housing assistance programs tied to Section 202 and Section 811, HUD built a UiPath bot to sit on top of mainframe-driven contract processes and automate repetitive monitoring, alerting, and notification of expiring Project Rental Assistance Contracts, avoiding dependence on manual staff actions during shutdown periods. In parallel, HUD used its LiveObjects AI/ML capability to ingest large volumes of structured and unstructured procurement and HR data, model how federal hiring and acquisition workflows actually moved through regulated steps, and learn bottlenecks across those processes. The system also produced data visualizations showing process paths, time taken, and isolated delays so HR and procurement stakeholders could identify corrective actions. Cybersecurity assessment and approval were treated as part of the implementation before broader rollout.
Contract status data from HUD's legacy housing systems was monitored by a UiPath bot, which generated alerts and notifications for expiring contracts. Separately, structured and unstructured procurement and HR data was ingested into LiveObjects, where AI/ML analyzed workflow steps and timeframes, surfaced bottlenecks, and fed visualization outputs and recommendations to HUD HR and procurement stakeholders for corrective action.
"We deployed the bot in less than 90 days."
"We were able to demonstrate performance gaps in these functions by tapping into the power of AI/ML, with learned scenario recommendations on improving isolated bottlenecks or greatest time or resource saving flows."
"developed data visualization capabilities mapping out processes, time taken, and identification of bottlenecks."
"the system is also designed to learn the intricacies of US Federal Government hiring and procurement regulations and processes through orchestrating the acquisition and hiring workflow"
"we built and prototyped a UiPath bot within a few weeks"
"we leveraged this artificial intelligence (AI) and machine learning (ML) capability called LiveObjects, which allows HUD to ingest an enormous amount of structured and unstructured agency procurement and HR data"
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