AI POS Bookkeeping & Accounting Automation AI bookkeeping and automated accounting. Categorization, reconciliation, reporting, and integrations — Free Plan, no credit card required.
POS revenue, auto-posted to the right GL
Item × industry × outlet drives a learned rule that posts revenue, COGS, and tax to the right GL accounts. Less bookkeeper time on coding, more time on judgment.
Part of HelloBooks POS · AI
Categorisation is where bookkeeping leaks hours. HelloBooks’ AI engine learns from your historical postings — every accepted suggestion is a training signal — and quickly gets to >95% auto-categorisation accuracy on POS revenue, COGS, and tax lines.
Every detail, dialled in
Built for the till, validated against the canonical accounting engine — so every POS sale closes the books cleanly.
Learned mapping per entity
Item attributes (category, HSN, brand) plus context (outlet, industry mode) plus history train a per-entity model. The model proposes; the bookkeeper accepts or corrects; corrections are training signals.
- Per-entity model
- Item + context + history
- Bookkeeper-in-the-loop
- Corrections feed training
Real-time at settle
The till settles; the engine posts to the suggested GL accounts immediately. Confidence below threshold flags for review; high-confidence postings flow without intervention.
- Real-time GL posting
- Confidence threshold
- Low-confidence flagged
- Bulk-review interface
Accuracy that compounds
The first month of POS rollout might run at 75% accuracy; by month three the same model is at 95%+ for that entity. Bookkeeper effort drops proportionally.
- Weekly retraining
- Per-entity accuracy metric
- Accuracy report visible
- Outlier-pattern alerts
Why teams move off legacy tills
- Manual categorisation per bill
- Errors propagate quietly
- Bookkeeper spends days on this
- No learning over time
- AI suggests, bookkeeper confirms
- Errors caught at confidence boundary
- Bookkeeper hours drop
- Accuracy compounds
Questions, answered
Where does the model run?
Per-entity, on our infrastructure. No cross-entity training; your data trains your model.
What about new items the model has not seen?
Falls back to category-level defaults; the first manual classification trains the model for the future.
Can I see the model’s reasoning?
Yes — every suggestion shows the contributing signals (item attributes, similar past postings, confidence). Bookkeeper transparency is non-negotiable.
How does this connect to GST and TDS?
GST is item-level and explicit; the AI contributes to GL classification, not tax computation. Tax remains rule-driven.
Related POS features
Ready to automate your books?
Let AI handle the bookkeeping and reclaim hours every month. Get started free — no credit card required.