TigerEye is AI for business data, giving sales, marketing, finance, and revenue operations (RevOps) leaders instant, trustworthy answers to their most complex questions. The platform connects to CRM, finance, and operations systems in minutes, removing the need for long setup cycles or complicated integrations. By transforming raw data into clear and explainable insights, TigerEye helps organizations make confident, data-informed decisions across departments.
Built for businesses that value accuracy and speed, TigerEye supports revenue forecasting, territory modeling, pipeline health assessment, and scenario analysis. Leaders can ask questions in natural language, such as “Which regions are slipping?” or “What if we expand headcount by 15%?” and receive real-time, SQL-backed answers that include the logic, data sources, and time-based context behind every metric. This transparency gives decision-makers confidence in the numbers without requiring technical expertise, while freeing up analysts and data teams to focus on higher-value work.
TigerEye delivers a full AI infrastructure, including a data lake, ETL (extract, transform, load) capabilities, and visualization tools. Its time-aware architecture captures daily data snapshots, allowing users to rewind business performance to any point in time and see how metrics, territories, and segments have evolved. TigerEye provides leaders with a foundation built for long-term growth and advanced AI workloads.
TigerEye recently achieved ISO/IEC 42001 certification, the first international standard for responsible AI. It sets requirements for transparency, explainability, and governance. It is critical for any organization using AI to make business decisions.
The platform is built for organizations where security, compliance, and speed are non-negotiable, with single-tenant architecture and certifications including SOC 2 Type 2, ISO 27001, GDPR, and CCPA. TigerEye delivers fast, transparent insights without compromising data control or trust.
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TigerEyeLanguages Supported
English
Overview by
Stuart Frederich-Smith