dodocs AI

Documents arrive by email, WhatsApp, or mobile upload, and the platform de-duplicates and queues them without manual sorting. The extractor reads complex tables, multi-line descriptions, and even handwritten receipts, then codes each one to the right account in a few seconds per page. The point is that routine invoices never wait on a person, which is what separates this from a plain OCR tool.

The platform matches invoices against bank statements automatically, which is the invoice matching step that usually eats the most time. Duplicate invoices, vendor anomalies, and split-billing schemes surface with AI confidence scores so you can judge each one quickly. This is where the reconciliation work moves off a spreadsheet and into a queue of flagged items.

You set a chase loop once, and the agent handles document chasing for missing files across email and WhatsApp. It tracks response rates, nudges every few days, and escalates tone when a contact goes silent, copying the owner when needed. For a firm, the effect is that a junior's chasing work runs without a junior doing it.

Each client can run at its own trust level, from Shadow, which processes silently and posts nothing, up to Aggressive, which posts everything and surfaces only real anomalies. A cautious firm can start in Shadow or Suggest mode and raise the level as the agents prove themselves. This control is what makes an autonomous ledger workable for people who do not want to hand over the books on day one.

Approved documents sync to QuickBooks Online, Xero, or Zoho with a full audit trail, and the QuickBooks integration is the most detailed of the three. Every posted entry keeps its source document, extraction, match evidence, and reviewer, so an auditor can follow the path in clicks rather than email threads. Connecting QuickBooks also cuts duplicate client requests, since the agent checks the ledger before asking for a document that already exists.

For firms, one dashboard ranks every client by document completeness and close progress, with blocked closes explaining why. A margin view compares fee against pages processed and hours saved per client, which shows which accounts are losing money. The team can then reprice those clients with data instead of a guess.

Only documents that need judgment reach a human: low-confidence fields, unmatched bank lines, and policy flags. Evidence is pinned next to each decision, and a rule you set once is applied by the agent going forward. In practice a reviewer works through a small slice of documents instead of every one.