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Curate Labs Article

WACCY v0.1.0: A Three-Statement Modeling Slice

WACCY v0.1.0 is the first released vertical slice of our small-business financial modeling stack: extraction contracts, validated datasets, model builders, and exporters.

WACCY v0.1.0: A Three-Statement Modeling Slice visual summary

Open Source release

WACCY is Curate Labs work, released through the DecisionNerd open-source organization. This post is a release note for small-business financial modeling and practical AI.

WACCY v0.1.0 is out. It is the first released vertical slice of the financial modeling stack we have been building for small businesses that need institutional-quality operating analysis without institutional overhead.

The release centers on a practical path through the system: source extraction, validated financial datasets, three-statement model construction, and exportable outputs. That matters because the hardest part of small-business finance is rarely a single formula. It is turning messy, partial, inconsistently classified records into something auditable enough to support decisions.

What Shipped

The `v0.1.0` release includes the core `waccy` package plus first-party EDGAR and QuickBooks packages. The GitHub release notes call out:

  • Layered financial data contracts from extraction through model builders

  • Fixture-first QuickBooks Online and EDGAR paths

  • A three-statement model builder

  • XLSX and pandas exporters

  • Deterministic ontology, mapping, validation, and confidence diagnostics

  • CI, coverage, docs publishing, and PyPI trusted publishing

That sounds plumbing-heavy because it is. Good financial software needs boring foundations: contracts, validation, diagnostics, and outputs people can inspect.

Why it matters

WACCY sits close to the center of what Curate Labs cares about: explicit structure over one-off outputs.

Small-business records are often ambiguous. Account names drift. Source systems disagree. Classifications are incomplete. A useful modeling system has to preserve provenance, normalize concepts, and make quality visible instead of pretending the input was clean.

This release is an early slice, but it points at the larger idea: financial modeling should be treated as a reproducible system. Extraction, ontology, validation, model construction, and export should be connected enough that a team can rerun the work when the business changes.

What Comes Next

The immediate direction is to harden the vertical slice: better fixtures, more validation coverage, clearer examples, and tighter extension paths around the core package.

The long-term goal is larger than a Python package. WACCY is one of the open-source foundations for operator workflows that understand finance, marketing, and compliance as connected business systems.

Source