Why LedgerIQ
Why LedgerIQ wins
Five things the category does not do together.
Most AI bookkeeping tools ask you to pick: trust the model completely, or do it by hand. LedgerIQ refuses the choice. AI does the speed; a trained accountant does the accountability; the database does the isolation. That combination is the product.
20input formats ingested without re-keying
Every entryreviewed by a trained accountant before it posts
Dailycash, runway and margin — not month-end
DB-leveltenant isolation via Postgres row-level security
The five differentiators
- Human-in-the-loop by architecture — Confidence-gated routing sends low-confidence, high-dollar and unknown-vendor entries to an accountant queue automatically. Routine entries move fast; the rest get a human eye before they become a posted fact. This is not a review someone might do — it is where the entries go.
- Efficiency: the close never stops — Transactions process as they arrive. Daily cash-survival checks, weekly operating and margin views and a rolling 30-day runway projection update the instant data lands — replacing a 15-to-30-day lag with a number you can act on today.
- Transparency: an audit trail that holds — Balances are derived from double-entry journal lines, not stored and hoped-for. Posted entries are immutable; corrections are new entries. Proactive alerts are built only on posted, accountant-reviewed numbers — never a draft.
- Cost efficiency — One platform replaces manual data entry, a patchwork of feeds and spreadsheets, and — for many businesses — a chunk of bookkeeping headcount. Bring your own CPA in for free rather than paying for a second set of books. Three plan tiers (AI-only, Founder, and AI-plus-human review) let a business buy only the review depth it needs.
- Security where it cannot be bypassed — Tenant isolation runs as Postgres row-level security on the tables themselves — not an application filter a future query can forget. A frozen tenant is refused at the API in the same request. Daily off-site backups and continuous transaction-log archiving bound worst-case data loss to roughly 30 minutes, and the restore path has been drilled end-to-end.
How that compares
| Dimension | Typical alternative | LedgerIQ |
|---|---|---|
| AI trust model | Fully automated categorisation, or fully manual bookkeeping — pick one. | AI categorises; a trained accountant reviews every entry before it posts. |
| Data isolation | An application WHERE-clause you hope every query remembers. | Row-level security enforced by Postgres itself — survives a forgotten filter. |
| Cash visibility | Current as of the last manual close — often 30+ days stale. | Daily cash signals, weekly margin, rolling 30-day runway, updated on ingest. |
| Input handling | Structured feeds only; invoices, memos and tapes get keyed by hand. | 20 formats — PDFs, tapes, voice memos, POS exports, statements — ingested directly. |
| Ledger integrity | Posted entries can often still be edited or deleted later. | Posted journal entries are immutable; corrections are new entries. |
| Your outside CPA | Locked to the platform's review team, or a slow year-end export. | Invite your own CPA into the same live books, free. |
| Backup & continuity | A manual export someone remembers to run — or nothing. | Automated daily off-site backups; ~30-minute worst-case recovery; restore drilled. |
Who this is for
Growing merchants and multi-location operators who have outgrown spreadsheet bookkeeping but are not ready to bet the ledger on an unsupervised model — and CPA firms that want to scale back-office capacity without scaling headcount at the same rate.
The category is full of tools promising AI will run your books unsupervised. LedgerIQ takes the opposite bet — and it is the bet that holds up under audit.