Bigeye.

What Bigeye actually is

Bigeye is an enterprise data observability platform whose technical centre is Autometrics — automatic ML-based anomaly detection on warehouse tables, with thresholds learned from each metric’s history rather than hand-tuned. Around that core sits a column-level lineage product (extracted from query logs and dbt manifests, including downstream BI dashboards), a sensitive-data scanner for PII/PHI/PCI classification, and — as of 2026 — AI Guardian, a runtime gate that enforces data-access policy on AI applications calling internal data.

Authoring is split: ML thresholds and certifications happen in the UI; deterministic checks and config-as-code live in bigConfig, a YAML format that pairs with the Bigeye CLI and Python SDK. The customer list (Cisco, Zoom, USAA, Burberry, Centene, NOV, Freedom Mortgage) is heavily regulated-enterprise.

Where it fits against the alternatives

Against monte-carlo, Bigeye is the more recent ML-anomaly-detection platform with — by reviewer reputation — sharper Autometrics tuning and stronger lineage. Monte Carlo has the brand and the broader ecosystem; Bigeye has the more recent technical investment.

Against anomalo, both are ML-first, both target enterprise. Anomalo is more GUI-first and has gone further into unstructured-data monitoring; Bigeye is more code-supported (bigConfig) and has gone further into AI Trust / governance.

Against soda, Bigeye is the ML-led counterpoint to Soda’s contract-led story. Soda has a real data-contract product and a YAML-DSL authoring path; Bigeye has stronger ML detection and stronger lineage. Buyers who lead with contracts pick Soda; buyers who lead with detection-and-governance pick Bigeye.

On the 2026 AI Trust pivot

The 2025–2026 repositioning is the dominant strategic fact about Bigeye today. AI Guardian, the runtime data-access policy product, is positioned as the core differentiator going forward — the argument is that traditional data observability (catching bad data after it ships) is necessary but insufficient when AI applications are reading data autonomously, and that runtime enforcement of who-can-read-what is the next frontier. Founder Kyle Kirwan moving from CEO to CPO during this pivot is a real signal — succession of any kind is worth raising in a sales conversation about a multi-year contract.

For buyers whose primary need is data quality observability, Bigeye still ships that product. But the future investment is concentrating on AI Trust, and that should factor into any 2027-and-beyond evaluation.

How to evaluate it

The right test is a real warehouse with real seasonality. Pick five tables that have actually broken in the last quarter — freshness, schema, value distribution, row count, downstream dashboard impact — and let Autometrics learn them. Look at: did the alerts catch the real incidents, what was the false-positive rate over a representative four-week window, and did the lineage product surface the downstream impact accurately enough that you’d trust it when triaging a production page?

If AI Guardian is the reason you’re evaluating, run a separate test on that. The product is new enough in 2026 that the maturity should be confirmed against your specific access-policy shape, not assumed from the marketing page.