Source and completeness
Track requested, received, processed, missing, duplicated, and rejected documents with client, period, entity, provenance, and version.
Accounting AI implementation
How should accounting work be automated with AI without losing control?
Automate the production flow, not professional accountability. Separate deterministic accounting rules from model judgement, attach every proposed entry to source evidence, route uncertainty to exceptions, and keep close, tax, and official submission controls explicit.
By Onyx Devs Lab · Published · Updated
Evidence firstDocument to extraction, entry, and review
Rules separatedDeterministic logic versus model judgement
Human controlExceptions, close, tax, and submission
Design a controlled accounting production line
The system should expose state and responsibility across collection, bookkeeping, review, close, and filing.
Proposal, not hidden posting
Link extraction and proposed entries to source coordinates; validate chart, period, currency, tax, balancing, duplicate, and materiality rules before posting.
Exceptions and approval
Route low confidence, conflicts, missing evidence, unusual values, policy changes, and high-impact entries to named reviewers with recorded disposition.
Accept operations, not only extraction accuracy
A high field-level score can coexist with an unusable month-end workflow.
Workflow measures
Measure end-to-end completion, exception rate, reviewer time, correction rate, close delay, unresolved items, and cost per completed portfolio.
Control evidence
Preserve source, proposed change, rule and model version, reviewer, approval, posted result, reversal, and audit export.
Official boundaries
Keep tax positions, regulated judgement, final close approval, banking, and official submissions under appropriately authorised professionals and supported interfaces.
How to control the accounting workflow
Map the accounting states
Define documents, periods, entities, ledgers, tasks, dependencies, exceptions, approvals, and systems of record.
Establish a labelled baseline
Sample representative clients and periods; label extraction, coding, matching, exception, and review outcomes.
Pilot one complete cycle
Run collection through review and close with queues, retries, controls, evidence, and human takeover—not isolated document demos.
Release by authority tier
Expand read, propose, approve, and post separately; monitor corrections, exceptions, close impact, cost, and policy changes.
Frequently asked questions
Should AI post directly to the ledger?
Only inside a narrowly approved authority envelope with deterministic validation, idempotency, audit, verification, reversal, and human exception controls.
Is extraction accuracy enough?
No. Completeness, duplicate handling, coding, reconciliation, review effort, exception resolution, period close, and resulting records determine operating value.
Can AI replace professional judgement?
No. Material, tax, regulated, unusual, or policy-dependent conclusions require appropriately qualified and accountable review.
Start with one concrete operating problem.
We will first clarify the workflow, evidence, data boundary, and outcome standard before recommending the next step.
Contact Onyx