2_use_case_name: Contract Acquisition Lifecycle Intelligence (CALI)
3_agency: General Services Administration
3_abr: GSA
4_bureau: FAS (QP0A)
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: CALI tool is an automated machine learning evaluation tool built to streamline the evaluation of vendor proposals against the solicitation requirements to support the Source Selection process. CALI is offered by Octo Consulting as Infrastructure as a Service and is implemented and hosted within Amazon Web Services (AWS) public cloud US East and US West regions. Once the Contracting Officer (CO) has received vendor proposals for a solicitation and is ready to perform the evaluation process, the CO will initiate evaluation by sending solicitation documents along with all associated vendor proposal documents to Bizagi’s Source Selection module, which will pass all documents to CALI. CALI will process the documents, associated metadata and begin analyzing the proposals in four key areas: format compliance, forms validation, reps and certs compliance, and requirements compliance. The designated evaluation members can review the evaluation results in CALI and submit finalized evaluation results back to Source Selection in Bizagi. CALI is currently being trained with sample data from the EULAs under the Multiple Award Schedule program.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 11/1/2024
22_dev_method: Developed with contracting resources.
24_hisp_support: No
25_hisp_name: Public Experience Portfolio – GSA 
26_public_service: Enable increased self-service through an expansion of digital costumer service tools
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: No
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: No
53_real_world_testing: No testing: No testing of the model to simulate performance in an operational environment has been conducted thus far.
55_independent_eval: Planned or in-progress
56_monitor_postdeploy: No monitoring protocols have been established: Necessary infrastructure needed to perform monitoring of AI systems in production is not available and/or a plan to monitor models has not yet been established.
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: N/A - individuals are not interacting with the AI for this use case
61_adverse_impact: No
63_stakeholder_consult: None of the above
65_appeal_process: No – Law, operational limitations, or governmentwide guidance precludes an opportunity for an individual to appeal.
66_no_appeal_reason: Law, operational limitations, or governmentwide guidance precludes an opportunity for an individual to appeal.
67_opt_out: No – This AI use case is not subject to the opt-out requirement because the AI functionality is solely used for the prevention, detection, and investigation of fraud or cybersecurity incidents, or the conduct of a criminal investigation.