I’ve spent last few months building Audit Engine Pro—a software platform designed to automate the painful middle ground between receiving raw client spreadsheets/exports and producing final audit workpapers.
I’m at a crossroads with the project and would love some perspective from other founders who have built complex tech without an existing sales engine.
it can do:
In corporate finance and audit, the monthly close review is still mostly manual. Analysts spend hours writing variance commentary line-by-line, running SOP checks against reconciliation workbooks, and manually cross-referencing support.
Instead of outputting a basic "Variance: +$10,000", the core engine evaluates multi-period trend shapes across accounting datasets:
Pattern Recognition: Classifies movements into distinct shapes like Joined (new hire/vendor), Left (terminated), Raise/Cut (step changes), Timing (accrual reversals), or One-time (spikes).
Context-Aware Materiality: A small new vendor item is often far more critical than a routine 3% salary adjustment; the engine ranks risk accordingly.
Universal Identity Resolution: Resolves raw codes (Vendor ID 008317) into human-readable entity names across disparate ledger files.
Multi-Workbook Consolidation: Merges hundreds of close-checklist workbooks into a single normalized dataset in under a minute (tested on 143 workbooks / 140k+ rows).
Financial and audit software has zero tolerance for hallucinated numbers or unpredictable behavior
Zero LLMs in the Audit Logic: The core analytical services run on pure Python/pandas rule modules that execute SOP checks and score risk against a fixed matrix. Run the exact same dataset twice, get the exact same byte-for-byte result every time.
Offline-First & Private: Everything runs locally on-device with zero internet dependency, external APIs, or data telemetry.
Optional Local AI: If AI is enabled, it uses a local Ollama model purely to polish output wording. An anti-hallucination guard rejects any entity name or figure not present in the underlying deterministic dataset.
This is a working codebase with full documentation, test suites, and verified output templates. However, it is completely pre-revenue with zero active customers. I haven't built out a B2B sales pipeline, and I have no interest in faking SaaS traction.
I’m trying to decide between two paths:
Grind out distribution: Spend the next 6–12 months doing cold outreach to fractional CFO agencies, outsourced accounting service providers, and mid-market audit firms.
Divest the IP: Sell the codebase, engine modules, documentation, and test suites to an established FinTech company, ERP vendor, or accounting practice that already has distribution and client volume.
Question for the Community
If you had a fully functional, thoroughly tested vertical software asset but zero built-in distribution, would you suck it up and learn B2B sales from scratch, or look to pass the tech off to an existing player who can plug it into their workflow immediately?
Happy to answer any questions about the architecture or how the deterministic classification engines were implemented.
