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Most AI automation in a business is not a chatbot. It is the reconciliation someone does every month in Excel, the invoices someone re-types, and the report someone rebuilds every Monday.
The processes that repay automation share a shape: high volume, rule-heavy but not fully rule-expressible, and currently absorbing skilled people's time. In practice that is:
Purchase invoices, bank statements, transport documents and expense claims turned into structured rows ready to post.
Bank against ledger, vendor statements against payables, GSTR-2B against purchase register — matched, with exceptions surfaced for a human.
Monthly MIS assembled from ERP data on a schedule instead of by hand each cycle.
Requests classified and routed with the supporting data attached, rather than chased over email.
Payment reminders and status responses sent from live system data over WhatsApp or email.
Duplicate vendors, inconsistent item masters and mis-keyed tax codes found at scale before they distort reports.
We would rather say this before an engagement than after one. Automation tends not to pay when:
Part of the assessment deliverable is an explicit list of what we recommend not automating, and why.
| Stage | Duration | Output |
|---|---|---|
| 1. Process assessment | 1–2 weeks | Shortlist of candidate processes with volumes, current effort and expected saving — plus what to leave alone |
| 2. Pilot on one process | 2–4 weeks | A working automation on real data, measured against how the task is done today |
| 3. Accuracy review | 1 week | Measured accuracy and exception rate, with a human-review threshold agreed in writing |
| 4. Production rollout | 2–6 weeks | Integrated with your systems, monitored, with a documented fallback when it is unsure |
| 5. Handover & support | Ongoing | Documentation, your team trained, agreed support terms |
We pilot before we roll out because extraction and matching accuracy depends on your documents, not on a vendor's benchmark. Any accuracy figure quoted before seeing your data is marketing.
Yes — that is the normal case, and it is the part most general AI vendors underestimate. We work against Tally through its XML and ODBC interfaces, against SAP through standard extracts, and against ERP and SQL databases directly. Where a system has no usable interface, the automation works from its exports rather than requiring you to replace it.
This is the same capability as our SAP to Tally migration and data migration practice: getting data reliably out of systems that were not designed to share it, and proving the result reconciles.
Assessments are a fixed fee. Builds are quoted per project once the pilot has established real volumes and accuracy, and the drivers are document variety, the number of systems involved, exception-handling depth and whether the output posts back into a live system or is handed to a person to post. We do not price AI automation per seat, because the value tracks transaction volume rather than headcount.
High-volume, repetitive work with clear inputs and outputs: extracting data from purchase invoices and bank statements, matching bank lines to ledger entries, reconciling GSTR-2B against the purchase register, assembling recurring MIS reports, and routing approvals with supporting data attached. Work that turns on judgement is better supported by an assistant that prepares the decision than by full automation.
No. Automations run against your existing systems using their standard interfaces — Tally via XML and ODBC, SAP via standard extracts, ERP and SQL databases directly. Where a system offers no usable interface, the automation works from its exports. Replacing a working accounting system to enable automation is almost never the right trade.
It depends on your documents, which is why we pilot on real data before quoting a production rollout. Any accuracy number given before seeing your invoices is a sales figure. What we commit to is a measured accuracy and exception rate from the pilot, plus an agreed confidence threshold below which an item is routed to a person instead of being posted automatically.
The assessment takes one to two weeks and ends with a shortlist of candidate processes, their current effort and the expected saving, including an explicit list of what we recommend not automating. A pilot on a single process then takes a further two to four weeks and runs on your real data, so the decision to roll out is made against measured results rather than a projection.
A 30-minute scoping call: we look at your system, your ledger count and your history, and tell you honestly what the work involves and what it will cost.
Request a scoping call WhatsApp +91 84348 01033