A data platform company that lets enterprises run AI on top of their own data. Its flagship product is an AI Gateway, which launched in general availability in August 2026 with features designed to reduce token waste. That same month, the company unveiled an architecture that compresses agent infrastructure into a single database. It also released OfficeQA Pro V2, a benchmark for measuring AI's reasoning ability on enterprise documents, in August 2026. Around this time, news broke of $7 billion in annual revenue alongside a $5 billion funding round. In the same month, the CEO announced a confirmed valuation of $190 billion and remarked that AGI had already arrived.
Current rank (1M)
26
Rank over the last 30 days
2026.08.21 – 2026.09.19 · High 15 · Low 27 · Now 26
It connects straight to Snowflake or BigQuery, investigates what changed and builds the dashboard for you. No query language required, and the row and column permissions on your account still hold.
Tracing and natural-language queries caught the waste in an hour, after AI agents quietly retried failed tool calls without ever raising a flag
A six-chapter guide for moving AI agents from pilot to production, with DXC and ICE case study numbers included
DuckLabs, which spent five years without venture funding, is joining AWS in early September, and DuckDB stays under its MIT license
The company recommends comparing tools by six capabilities—cataloging, lineage, access control, and more—plus seven evaluation criteria, rather than by brand name
Custom harness on Claude Opus 5 hits perfect score on ARC-AGI-3 — the scaffolding, not the model, made the difference
The 30-day retention requirement stays, but storage location shifts from Anthropic's servers to customers' own cloud
On the same day CEO Ali Ghodsi declared the arrival of AGI, the company closed a $5 billion funding round valuing it at $190 billion
Q2 run-rate revenue tops $7B with growth above 80%, new funding round values company at $190B
Unity AI Gateway moves to general availability, using real-time budget policies to control model selection
A benchmark that measures the ability to answer using in-house documents as evidence, with questions scaled up using synthetic data
CLA case study shows AI agents run on Lakebase Postgres instead of separate queues, schedulers, and caches
