AI Business Automation

Document-heavy and repetitive text work is where it pays off first. Pulling fields off invoices, purchase orders, and bank statements into your accounts system, drafting the first reply to a WhatsApp or form enquiry, routing it to the right person, scoring leads, and assembling recurring reports. What it does badly is judgement: pricing exceptions, credit decisions, anything where a person has to be accountable for the call. Those stay with your team, with the AI doing the reading and drafting around them.

Usually yes, though the route matters more than the answer. Cloud systems like Zoho, Odoo, or a custom app expose APIs we can read and write against. Tally running on an office machine is different — we work through its XML interface, ODBC, or a scheduled export instead. We confirm the integration path before quoting, because that single detail decides most of the build effort. If no clean interface exists, a supervised handoff where the AI prepares the entry and a person posts it is often better value than forcing an integration.

It depends heavily on the document, and any honest answer has to be split. Clean machine-generated PDFs — GST invoices, e-way bills, statements downloaded from a bank portal — extract reliably in most projects we see. Photographed handwritten challans, faded thermal prints, and mixed-language formats do not. So we design for review rather than pretending accuracy is a solved problem: low-confidence fields get flagged, and anything that posts to your books or a GST return passes a human check first.

No. Each automation gets access only to the records that workflow needs, not your whole system. Read-only credentials where writing is not required, role-scoped API users, and redaction of customer phone numbers or PAN details before anything reaches an external model. You get a plain list of what leaves your infrastructure and what does not. For genuinely sensitive material, a smaller model running on your own server is a real option — slower to set up, but the data stays in-house.

Every engagement is quoted individually, and the cost sits mostly in integration and review design rather than the AI itself. What moves the number: how many workflows, whether your systems have usable APIs, document volume and messiness, and whether anything has to run on your own hardware. Model and API usage is billed on consumption and, at typical back-office volumes, is usually the smallest recurring line in the budget. We quote that separately so you can see it rather than having it buried.

A single narrow workflow — one document type, one system to write into — usually goes live in a few weeks rather than months. Broader work, like an internal copilot answering questions across an ERP, takes longer because data has to be mapped and permissions settled first. The usual delay is not development. It is getting sample documents, test credentials, and a decision on who signs off the automated output, so those three are worth lining up before work starts.