/ platform

Vera

It all starts with a query.

TableQL's AI data analyst. Connect your data sources, ask questions in plain English, and let Vera do the rest.

/ ask vera Which providers have the highest fraud risk in our Medicare claims?
[ 01 ontology ]

Vera checks your ontology first

Vera doesn't start from zero on every query — that's token-inefficient. Vera searches your ontology, institutional knowledge stored as code in one git-backed repo of every definition, metric, script, and document, and pulls only the narrow slice of context it needs to find the right sources. Nothing is pre-loaded.

Every change is versioned and permissioned in place. Vera proposes, you approve — and if you can't see it, Vera can't either.

SCANNING ONTOLOGY · 1995 OBJECTS
/ ontology lookup/ fig. 01
[ 02 sources ]

Queries the right connectors and sources

No retry storms. No discovery loops. Vera routes each question straight to the sources that can answer it, across your entire stack, with dialect variance handled automatically.

Data lands in a secure, disposable tidepool, spun up for the run and torn down after. Every query is fully audit-logged.

PROVISION → RUN 7990
/ connectors → tidepool/ fig. 02
[ 03 subagents ]

Subagents reason across hundreds of thousands of data points

Vera knows when a question needs more than one query. For work that spans hundreds of thousands of rows or crosses multiple sources, it breaks the task down and fans out to parallel subagents, sweeping, filtering, and aggregating at scale, then reconverges into one answer.

No SQL required, and no more compute than the question actually needs.

raw claims field
/ parallel reasoning/ fig. 03
[ 04 create ]

Any visualization. Any outcome.

Charts, reports, data apps, alerts — Vera delivers the outcome, not just the answer, wherever your team works.

→ a bar chart
/ one finding, four forms/ fig. 04
[ 05 proof ]

They asked, Vera delivered.

/ cobalt cloud

faster delivery per FP&A question

/ meridian labs

data requests answered per week

/ playon sports

datasets joinable by non-technical users

[ try tableql ]

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