05 · AI Automation Agents
Every business has someone whose job is moving data from one place to another — a PDF into Tally, an email into a spreadsheet, a WhatsApp photo of a bill into an accounting system. That work is completely automatable now, and it is usually where AI pays for itself fastest.
The problem
Manual data entry is slow, expensive, and wrong more often than anyone admits. It is also the easiest thing in your business to hand to a machine.
What we build
Each one is a page of its own — pick the closest match, or tell us your problem and we'll point you at the right one.
Photo, PDF or scan of a bill in — structured, validated, GST-aware accounting entry out. Handles the messy real-world formats Indian vendors actually send.
Result: Hours of daily typing become a review queue of a few exceptions.
Reads incoming POs, matches them against quotes and inventory, flags discrepancies, and raises the order in your system.
Result: Order-to-fulfilment starts hours earlier.
Staff WhatsApp a photo of a receipt; the agent categorises it, checks it against policy, and files the claim.
Result: Expense season stops being a fortnight of misery.
Moves data between systems that were never designed to talk to each other — including old software with no API, driven through the interface.
Result: Integration without a rewrite.
Reads the inbox, works out what is actually a request, and creates the task, lead or ticket in the right system with the right owner.
Result: Nothing falls through because it was buried in an email thread.
Pulls the numbers, writes the commentary, and delivers the daily or weekly report to WhatsApp or email before you wake up.
Result: The report exists whether or not anyone remembered.
Cleans, reconciles, merges and validates the Excel files your business runs on — on a schedule, without breaking your formulas.
Result: The month-end scramble shortens dramatically.
Consolidates attendance, leave, overtime and advances into a payroll-ready sheet with the exceptions highlighted.
Result: Payroll prep goes from days to an afternoon.
What you get
How it's built
We pick tools per project rather than forcing every client onto the same stack. If something in your business already works, we build around it instead of replacing it.
In depth
Document extraction and back-office automation is the most reliably profitable AI work we do, because the before-state is so plainly wasteful. Here is what accuracy really looks like on Indian paperwork.
Every other AI project involves some argument about whether the outcome is better. Automating data entry does not. There is a person, they spend a measurable number of hours retyping information that already exists in a PDF, and afterwards they do not.
That clarity is why we recommend invoice extraction as a first AI project more often than anything else. The payback calculation fits on the back of an envelope, the risk is contained, and the result is visible within weeks.
Suppliers quote accuracy figures without seeing your documents, which is close to meaningless. Accuracy depends almost entirely on input quality.
| Input type | Realistic field-level accuracy | What it needs |
|---|---|---|
| Clean digital PDF invoice | 95–99% | Minimal review |
| Good scan of a printed invoice | 90–97% | Spot review |
| Phone photo, decent lighting | 85–95% | Confidence threshold + review queue |
| Crumpled bill, poor light, handwriting | 60–85% | Human review on most fields |
This is why every pipeline we build has a confidence threshold and a human review queue. The goal is never zero humans — it is turning three hours of typing into ten minutes of checking the handful of fields the system was unsure about.
Anyone quoting you a single accuracy number before looking at a sample of your actual documents is guessing.
Tally is the accounting system most Indian SMBs actually run, and integrating with it is one of the most common requests we get. It accepts structured XML over an HTTP interface, which is how we write entries in.
The extraction is usually the easy half. The harder half is mapping: GST fields, ledger names, voucher types and tax classifications all have to line up with your existing chart of accounts. If your ledger naming is inconsistent — and it usually is — that cleanup is part of the project and worth doing regardless.
The same pipeline architecture applies across most back-office document work. The commonly requested variants:
Three questions determine whether this is worth doing, and you can answer all of them yourself before speaking to anyone.
An extraction pipeline tuned to your real formats, validation rules, a review queue for low-confidence items, direct write into Tally, Zoho Books, Excel or your own database, an audit trail on every processed item, and an accuracy report so you can see it working rather than take our word for it.
FAQ
On clean documents, above 95% field-level accuracy is normal. On poor phone photos of crumpled bills it is lower — which is why we always build a confidence threshold and a human review queue rather than pretending it is perfect.
Yes. Tally integration is one of the most common requests we get and we handle it through Tally's XML interface.
That is the normal case, not the exception. We tune the pipeline on your real documents during the build — not on a generic template.
Other AI service lines
Next step
A 30-minute call. We'll tell you what we'd do, roughly what it costs, and whether it's worth doing at all.