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Case Study · Fintech · Analytics

240 active dashboards. The CFO opened 8 last quarter.

We retired 232 boards, migrated the 8 that survived onto Apache Superset with custom whale + mekko charts, and gave the customer back $420K in year-one seat fees. Decision cycles dropped from three weeks to a workday.

Industry: Fintech · Series CGeo: India + USSize: ~600 employees
Year-one saving
$420K
BI seat-fee elimination, validated against the prior year invoice
Dashboards
240 → 8
estate retired down to the boards that ship decisions
Load time
15s → 3s
on the highest-traffic margin board, after ClickHouse pre-aggregation

Context.

Challenge. A Series C fintech had built 240 dashboards in three years. Eight got opened in the prior quarter. The CFO trusted one. The licence renewal was up for $480K and the head of data wanted out.

Constraint. Zero downtime, zero data loss, and the seat-fee saving had to be defensible to the audit committee.

Approach.

Week 1

Catalogued the full estate by view-count and ownership. Killed 232 boards on a 5-view threshold with an opt-out window. Three people replied; the rest let the boards go.

Week 2–3

Ported the 8 survivors to Superset. Built two custom chart plugins (whale curve for account contribution, horizontal waterfall for margin variance) and contributed three fixes upstream while we were in there.

Week 4

Performance pass. Integrated ClickHouse for the top three high-traffic boards with pre-aggregated tables. Top board load time dropped from 15s to under 3s.

Week 5–6

SSO + RBAC via OAuth, audit logging on every board, embedded the customer-facing dashboards into the existing portal with row-level security.

Stack.

Apache Superset (custom plugins)ClickHouse for hot aggregatesOAuth + role-based accessThree upstream PRs to apache/superset
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