Bigeye

Bigeye

Kyle Kirwan and Egor Gryaznov launched Bigeye in 2019 after a stint at Uber where they wrestled data quality across hundreds of petabytes and thousands of weekly users. The pair built Uber's in-house A/B testing pipeline and left convinced that automation without control creates new failure modes. Bigeye sells to data engineering teams at large enterprises — Freedom Mortgage, NOV, and Udacity all appear among the firm's named customer logos. The product monitors data health, traces end-to-end lineage, and classifies sensitive information across both modern and legacy stacks. Bigeye operates as a pure-play enterprise software vendor with a platform split across several modules: data catalog and lineage, anomaly detection and monitoring, sensitive-data classification, and runtime policy enforcement for AI access. The firm does not disclose any proprietary fund vehicles, direct co-investment programs, or balance-sheet commitments — it deploys no investment capital on behalf of LPs. Its customers are the enterprise data teams themselves, and the metric they cite is operational: one client reports cutting detection times from over three days to under 24 hours; another cites a 60% reduction in merge time. The product's lineage engine is its structural differentiator, supporting what Bigeye labels 'dependency-driven monitoring' — tying data quality rules to the upstream sources that feed them rather than isolating checks in vacuum. Bigeye is backed by venture investors whose identities are not named on the current firm site beyond a generic 'World class investors' badge, though prior press coverage included a $45 million funding round in 2021. The firm remains remote-first with talent distributed across the United States. No dedicated philanthropic foundations, real-asset arms, or operating subsidiaries are disclosed. A 2022 Solutions Review mention and inclusion in the Data50 list round out third-party recognition. In 2024, Bigeye positioned itself squarely around the 'AI Trust' narrative, adding runtime enforcement and governance layers to the core observability engine — a response to enterprise demand for guardrails as generative AI accelerates data-consumption patterns. Bigeye's structural distinction lies in its lineage-first architecture built by two operators who previously ran data at scale inside a hyperscale consumer tech firm. Rather than competing on breadth-of-integrations or dashboarding, the company ties every quality check to the specific upstream asset that produces the data — an approach that appeals to enterprises whose data stacks mix legacy systems with modern cloud-native services. This architecture means that when a pipeline breaks, teams can trace the failure to the source table rather than hunting across dashboards.