AI modeling and testing is subject to robust AI governance processes and controls. Runtime algorithms are subject to strict SLAs and interaction models are refreshed nightly to ensure the best possible caller\/agent pairings<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
Runtime algorithms subject to strict SLAs and failsafe controls<\/p>
Callers are being paired with the existing set of agents<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
Historical data is used to determine the expected payoff of different caller and agent pairings<\/p>
Interaction models and associated variables dynamically evolve to ensure best possible pairings<\/p>
OFF sample allows for analysis and improvements to the pairing models<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
Gated process to validate across all stages<\/p>
In the deployment phase Afiniti performs a cutover to Monitor Mode and validates every operational aspect before switching ON the solution<\/p>
Periodic testing conducted to ensure models and data used don\u2019t introduce bias (in conjunction with our clients, since Afiniti does not access\/process customer protected data).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
(low, medium, and high) to deter bias and discrimination (including known proxies)<\/p>\n
Process controls to remediate medium and high risk data elements in the collection and organization stages of Afiniti\u2019s data supply chain<\/p>\n <\/li>\n
to include purpose, risk level, location, etc.<\/p>\n <\/li>\n
supporting Afiniti\u2019s purpose: Discovery and Consumption<\/p>\n <\/li>\n
to implement in each stage of the data supply chain when processing, labeling and preparing data for consumption<\/p>\n <\/li>\n <\/ul>\n <\/div>\n <\/div>\n <\/div>\n
Monitor Mode > FIFO > Cutover to validate before switching ON the solution <\/p>\n <\/li>\n
(more than 50) are part of our deployments i.e., signals for client assurance including agent utilization and caller waiting times<\/p>\n <\/li>\n
ON and OFF benchmarking to review impact of Afiniti across customer segments available from structured data<\/p>\n <\/li>\n
to constrain potential bias based on metrics<\/p>\n <\/li>\n
system for mitigating any potential bias<\/p>\n <\/li>\n <\/ul>\n <\/div>\n <\/div>\n <\/div>\n
(and learning ability for model performance monitoring and logging over time<\/p>\n <\/li>\n
in place to raise alarms<\/p>\n <\/li>\n
(production uptime, revenue impact, operational impact)<\/p>\n <\/li>\n
temporarily switch back to Monitor mode as needed for risk mitigation purposes<\/p>\n <\/li>\n <\/ul>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n<\/div>\n\n
![\"Red-Dot\"](\"https:\/\/staging.afiniti.com\/wp-content\/uploads\/2025\/06\/Red-Dot-horizontal.svg\")
\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t![\"red](\"https:\/\/staging.afiniti.com\/wp-content\/uploads\/2025\/06\/Red-Dot.svg\")
\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t