From 5777e0443441183347d2df5ec45be01df9331db0 Mon Sep 17 00:00:00 2001 From: Rodney Anthony Dubose Date: Mon, 4 May 2026 21:26:38 -0400 Subject: [PATCH 1/2] Add files via upload --- inventory/20260421-1.yml | 58 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 58 insertions(+) create mode 100644 inventory/20260421-1.yml diff --git a/inventory/20260421-1.yml b/inventory/20260421-1.yml new file mode 100644 index 0000000..6d65cfc --- /dev/null +++ b/inventory/20260421-1.yml @@ -0,0 +1,58 @@ + +template_date: 2026-04-21 +template_version: 1.0.1 +basic: + date: 2026-04-21 + version: 1.0.0 + contact_info: + - name: Sreenivas Karpurapu + division: Response Security and Data Integrity + mail: sreenivas.karpurapu@census.gov + - name: Ama Danso + division: Response Security and Data Integrity + mail: Ama.A.Danso@census.gov + +bedrock_information: + company_name: U.S. Census Bureau + website_url: https://census.gov/ + industry: Government + intended_users: + internal_employees: true + external_user: false + use_case_description: | + QUANTM’s AI Enhanced Clerical Matching Solution evaluates the application of large language models to support In Field Enumeration (IFE) and Census Data Quality Assurance (CDQA) by performing intelligent matching of complex records, reducing manual clerical effort while producing transparent, explainable decisions consistent with expert human judgment. + +bedrock models: + -anthropic.claude-4-5-sonnet + +federal_standards_information: + id: 20260421-1 + project_name: QUANTM + project_summary: | + As part of the quality operations of the US Census Bureau, computer matching and clerical operations are employed. These efforts help in the assessment of the quality of data collection. The existing operations have constraints in their ability to accurately match and compare complex datasets, resulting in high number of cases being flagged or removed. This process is time-intensive and expensive, creating a significant burden on human resources. With the advanced AI capabilities available in the industry, QUANTM is assessing their feasibility to support the quality assessment efforts and reduce the clerical footprint. The bureau aims to explore whether large language model (LLM) technology can replicate and improve upon the reasoning processes used by human clerks. + development_stage: active + data_sensitivity: | + Yes, Demographic data from census survey responses + dms_project_number: 7535836 + cms_project_number: none + + +deployment_details: + crf_number + finOps_project_number: fs0000000001 + finOps_project_name: edl_ditd + project_role: edl_ditd_quantm +accounts: +-account_id: 001502248272 + account_alias: edl-addcp-prod-ew + environment: prod + management_account_name: edl-management-ew + management_account_project_role: edl-u-7535836 + tenant_account_project_environment_role: r-edl-prod-7535836 + + +history: +-date: 20260421 + comment: + + From 87fa9963c66065425315084c4440d3674b2d0295 Mon Sep 17 00:00:00 2001 From: badra001 Date: Tue, 5 May 2026 14:57:59 -0400 Subject: [PATCH 2/2] rework file --- inventory/20260421-1.yml | 58 --------------------------------------- inventory/20260504-1.yml | 59 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 59 insertions(+), 58 deletions(-) delete mode 100644 inventory/20260421-1.yml create mode 100644 inventory/20260504-1.yml diff --git a/inventory/20260421-1.yml b/inventory/20260421-1.yml deleted file mode 100644 index 6d65cfc..0000000 --- a/inventory/20260421-1.yml +++ /dev/null @@ -1,58 +0,0 @@ - -template_date: 2026-04-21 -template_version: 1.0.1 -basic: - date: 2026-04-21 - version: 1.0.0 - contact_info: - - name: Sreenivas Karpurapu - division: Response Security and Data Integrity - mail: sreenivas.karpurapu@census.gov - - name: Ama Danso - division: Response Security and Data Integrity - mail: Ama.A.Danso@census.gov - -bedrock_information: - company_name: U.S. Census Bureau - website_url: https://census.gov/ - industry: Government - intended_users: - internal_employees: true - external_user: false - use_case_description: | - QUANTM’s AI Enhanced Clerical Matching Solution evaluates the application of large language models to support In Field Enumeration (IFE) and Census Data Quality Assurance (CDQA) by performing intelligent matching of complex records, reducing manual clerical effort while producing transparent, explainable decisions consistent with expert human judgment. - -bedrock models: - -anthropic.claude-4-5-sonnet - -federal_standards_information: - id: 20260421-1 - project_name: QUANTM - project_summary: | - As part of the quality operations of the US Census Bureau, computer matching and clerical operations are employed. These efforts help in the assessment of the quality of data collection. The existing operations have constraints in their ability to accurately match and compare complex datasets, resulting in high number of cases being flagged or removed. This process is time-intensive and expensive, creating a significant burden on human resources. With the advanced AI capabilities available in the industry, QUANTM is assessing their feasibility to support the quality assessment efforts and reduce the clerical footprint. The bureau aims to explore whether large language model (LLM) technology can replicate and improve upon the reasoning processes used by human clerks. - development_stage: active - data_sensitivity: | - Yes, Demographic data from census survey responses - dms_project_number: 7535836 - cms_project_number: none - - -deployment_details: - crf_number - finOps_project_number: fs0000000001 - finOps_project_name: edl_ditd - project_role: edl_ditd_quantm -accounts: --account_id: 001502248272 - account_alias: edl-addcp-prod-ew - environment: prod - management_account_name: edl-management-ew - management_account_project_role: edl-u-7535836 - tenant_account_project_environment_role: r-edl-prod-7535836 - - -history: --date: 20260421 - comment: - - diff --git a/inventory/20260504-1.yml b/inventory/20260504-1.yml new file mode 100644 index 0000000..b3ab9c9 --- /dev/null +++ b/inventory/20260504-1.yml @@ -0,0 +1,59 @@ +template_date: 2026-05-05 +template_version: 1.0.2 +basic: + date: 2026-05-04 + version: 1.0.0 + contact_info: + - name: Sreenivas Karpurapu + division: DITD + mail: sreenivas.karpurapu@census.gov + - name: Ama Danso + division: DITD + mail: Ama.A.Danso@census.gov + +bedrock_information: + company_name: U.S. Census Bureau + website_url: https://census.gov/ + industry: Government + intended_users: + internal_employees: true + external_user: false + use_case_description: | + QUANTM's AI Enhanced Clerical Matching Solution evaluates the application of large language models to support + In Field Enumeration (IFE) and Census Data Quality Assurance (CDQA) by performing intelligent matching of complex records, + reducing manual clerical effort while producing transparent, explainable decisions consistent with expert human judgment. + +bedrock_models: + - anthropic.claude-4-5-sonnet + +federal_standards_information: + id: 20260504-1 + project_name: QUANTM + project_summary: | + As part of the quality operations of the US Census Bureau, computer matching and clerical operations are employed. These efforts help in the assessment + of the quality of data collection. The existing operations have constraints in their ability to accurately match and compare complex datasets, resulting in high number + of cases being flagged or removed. This process is time-intensive and expensive, creating a significant burden on human resources. With the advanced AI capabilities + available in the industry, QUANTM is assessing their feasibility to support the quality assessment efforts and reduce the clerical footprint. The bureau aims to explore + whether large language model (LLM) technology can replicate and improve upon the reasoning processes used by human clerks. + development_stage: active + data_sensitivity: | + Title 13. Demographic data from census survey responses + dms_project_number: 7535836 + cms_project_number: null + +deployment_details: + crf_number: NUMBER + finops_project_number: 1 + finops_project_name: edl_ditd + accounts: + - account_id: 001502248272 + account_alias: edl-addcp-prod-gov + regions: + - us-gov-west-1 + environment: prod + commercial_account_id: 563127383709 + commercial_account_alias: edl-addcp-prod-ew + +history: + - date: 20260505 + comment: created