Selector's customer base includes 50 deployments across service providers as well as large enterprises in retail, media distribution, colocation services, and multi-cloud networking services. These customers aim to correlate events across their network, applications, and infrastructure; eliminate the need for human intervention in RCS and remediation; and democratize access to insights using conversational natural language interfaces. Selector delivers on these outcomes, while accelerating incident remediation through smart, actionable alerting and a GenAI-based conversational interface.

Selector translates English phrases to SQL queries through the use of an LLM. Each SQL query includes the table, or data set to be searched, along with filters, or conditions which prune the search results. We walk through a number of SQL queries and sample search results, before considering the LLM-based translation of a sample English phrase processed by Selector.

An LLM differs from a function in that it takes output and imputes, or infers, a function and its arguments. We first consider how this process works within Selector for an English phrase converted to a query. We then step through the design of Selector's LLM, which relies on a base LLM trained with English phrases and SQL translation, then fine-tuned, on-premises, with customer-specific entities. In this way, each of Selector's deployments relies on an LLM tailored to the customer at hand.

Join us for an insightful conversation with Andrew Pringle, Delivery Lead at Nationwide Building Society (NBS), as we dive into how Nationwide transformed their system resiliency and achieved substantial savings. By partnering with Digitate, NBS identified 50 critical scenarios to monitor and alert in their core customer data systems, resulting in enhanced reliability and cost savings of $1.2 million.