Ledger Copilot
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For investors & partners

Ledger Copilot, in 90 seconds.

We're building the AI bookkeeper that watches everything — bank transactions, credit cards, books — and runs continuous audits + reports against a deterministic engine. Tested against six business types from a $850K coffee shop to an $85M housing authority. Modeled $138K annual savings on a $75M construction GC. $1.4B in projected addressable savings across ten Fortune-500 targets we've analyzed.

Dollar figures on this page are forward-looking projections and internal market-sizing estimates, not realized or guaranteed results.

$1.4B
projected addressable AI savings across 10 named Fortune-500 targets
Modeled: Amazon $428M · Walmart $303M · Wells Fargo $161M ...
See target list
$305M
projected across 11 named PHAs we've analyzed
Modeled: NYCHA $188M alone (Troubled status, federal monitor)
See PHA watch
<1 mo
modeled payback across the scales tested
From small SMB to major metro PHA
Run the calculator

Why now

The bookkeeping bottleneck inverts.

For 100 years, the bookkeeper's value was throughput — txns per hour. AI changes that. The mechanical work (categorize, reconcile, post journal entries) is now machine work. The human role shifts to interpretation and strategy.

Compliance burden is exploding.

2024 IRS rule cut e-file threshold from 250 returns to 10. HUD penalty schedules are tightening. State agencies now actively pursue agencies with SEMAP scores below 60%. AI catches issues in-period, eliminating the year-end mystery.

Aggregator dependency is risky.

Plaid alone gives us 12K institutions. Our stack adds OFX Direct Connect (no aggregator dependency), file upload (universal fallback), and an MX-ready adapter slot. We have 100% bank coverage with zero single-point-of-failure.

What's shipped

Not slides. Live, runnable, end-to-end. Click through any of these.

The architecture, briefly

Deterministic-first, LLM-second

All detection (anomalies, reconciliation, audit findings) is rule-based and provable. LLMs only narrate and translate — never make accounting decisions. This is the architectural pattern Rillet uses; it's the only one that's audit-defensible.

A multi-engine deterministic fleet (see /api/health for live counts)

Modular monorepo. Anomaly engine, reconciliation engine, audit engine, financial reports, file importers, bank connectors, activity log — each independently testable, each with explicit contracts.

Universal bank coverage

Plaid is primary. OFX Direct Connect (Schwab, Fidelity, USAA, Navy Fed) bypasses every aggregator. CSV/OFX/QFX file upload is the universal fallback. MX adapter slot ready for activation.

Provable continuity

Every audit produces a SHA-256 fingerprint over input IDs + finding codes. Re-run the audit later on the same data, get the same fingerprint. Downloaded backup JSON has its own content hash in the HTTP header. The financial picture is back-testable.

Get in touch

Investor inquiries, partnership conversations, or just curious — happy to share the deeper data.