rasinmuhammed/saas-finance-sql-evaluation

SaaS Finance SQL Evaluation (MRR waterfalls that reconcile exactly)

An evalpack: an evaluation database generated from the answer key, not annotated after the fact. A VLDB 2026 audit found 52.8% of BIRD Mini-Dev answer keys wrong because benchmarks annotate answers onto existing databases; this dataset inverts the order. The declared properties (curves, shares, identities) are the specification, the database is generated to satisfy them exactly, and every shipped question was re-verified by executing its gold SQL against these exact files with DuckDB, an engine that shares no code with the generator.

6,000 rows across the tables below.

What is in it

Tables: mrr_movements (6,000 rows).

Three SaaS tenants (acme growing, globex declining, initech steady) share one movements table of new / expansion / contraction / churn rows. Every tenant's running MRR balance, recomputed from the raw rows, lands on its declared ending value for every month, to the cent. 108 questions ship with the pack: per-tenant net movements, per-type totals, and running balances.

Files

• tables/*.csv: the database
• questions.jsonl: one verified question per line (natural language, gold SQL, expected answer, tags)
• certificate.json: per-question DuckDB verification plus FK proof
• manifest.json: spec hash, seed, library versions, dropped candidates
• verify.py: standalone re-verification (needs only pip install duckdb)
• schema.misata.yaml: the full declaration; regenerate or rotate the pack from it

Re-verify in thirty seconds

pip install duckdb
python verify.py

Rotate the environment without touching the answer key

pip install misata
misata evalpack --config schema.misata.yaml -o rotated_pack --seed 7

A new seed replaces the rows; the declared answers stay the answers, so agents cannot memorize the environment. Same version + same schema + same seed reproduces these bytes exactly.

Provenance

No real data: fully declaration-generated, offline, deterministic (provenance statement). Generated with misata (MIT). Where the approach fails is documented in LIMITATIONS.md.

The rest of the shelf

Same idea, other domains, every answer re-verified with DuckDB against the shipped files:

• Ecommerce analytics
• SaaS finance
• EdTech practice

If you want ground truth for a regression rather than for SQL, the machine degradation set carries exact remaining useful life on 100 run-to-failure machines.

Generated by Misata (MIT). Build your own: misata.studio.