16 posts tagged with "Processes"

Goods arrive with a delivery note — as a PDF in an e-mail, or on paper in the driver's hand — and someone in the warehouse retypes it into a goods receipt, line by line. Structured import solves this only when the supplier can send a clean CSV or XML file; most can't, or won't. When the delivery note is a PDF or a scan, there's nothing to import, so it's keyed by hand — and the stock figures are only as timely as that typing. This is a concrete case where a ready connector doesn't fit and an AI agent that reads the note, matches it to the order and drafts the receipt earns its place.

A lot of B2B orders don't arrive as tidy data — they arrive as an e-mail someone typed, or a PDF attachment, in no fixed format. Structured import and EDI assume the other side can export clean XML or CSV; when your customers just write "send me 20 of the usual plus 5 of the blue ones," there's nothing structured to import, so a person reads each message and keys it in. This is the case where a ready connector genuinely doesn't fit — and where an AI agent that reads the e-mail, extracts the order and posts it (with a human confirming) earns its place.

Most companies don't have one broken tool — they have five good ones that don't talk to each other. Orders arrive by e-mail, stock lives in one system, invoicing in another, and a person retypes between them. The honest answer isn't always "build something custom": if a ready-made connector exists for your combination of tools, use it — it's cheaper. Custom only earns its keep when no connector fits your tools or your process is genuinely non-standard. This guide is about telling those two cases apart — and what we build when it's the second.

Corporate law has a quiet, unglamorous half: keeping dozens or hundreds of client companies formally in order. Filings after every change, beneficial-owner registrations, official-journal announcements that start deadlines whether anyone read them or not. It's high-liability work that runs on spreadsheets, calendar reminders and one very careful paralegal. The pain concentrates in three concrete problems. We show how to build a corporate assistant wired directly to the registers — the Slovak commercial register (ORSR), the register of public-sector partners (RPVS), the official business journals and their Czech counterparts — that watches the sources, drafts the paperwork and keeps the deadlines, with a lawyer approving every step.

Legal AI tools that answer questions about legislation and case law already exist — that layer is covered. What no one covers is the layer where a law firm's real value lives: its own documents. The contracts it negotiated, the filings that won, the opinions partners signed. That know-how sits in folders and in people's heads, and it leaves with them. The pain concentrates in three concrete problems. We show how to build a private firm GPT that attacks all three: retrieval over the firm's own archive, answers with verified citations instead of confident fabrications, and confidentiality by design — hosted in the EU, with a lawyer approving anything that leaves the building.

"AI agents for business" is the most repeated phrase in tech right now — and one of the vaguest. Everything gets sold under it, from a web chatbot to promises of full autonomy. This guide is for companies that want a factual answer: what an AI agent actually does, what it can handle with invoices and orders in systems like Pohoda, Money or Helios — the ERPs Czech and Slovak companies actually run on — how to keep it under control, and how to start so the first deployment delivers a measurable number, not another slide deck.

Invoice data extraction (also called invoice OCR, invoice capture or data mining from invoices) is software that reads supplier invoices — PDFs, scans, photos, e-mails — and turns them into structured data for your accounting system: supplier, amounts, VAT, due date, line items. In 2026 it typically costs from a few cents to about €0.50 per invoice, and modern AI tools read 90–99 % of fields correctly. This guide explains how it works and what you'll actually pay — vendor-neutral, with no tool to sell.

Most companies roll out AI assistants the same way: buy licenses, run a kickoff training, and hope. Three months later nobody can say who actually uses the tool, who is stuck, or whether it brings anything — the honest answer to "is it worth it?" is a feeling, not a number. The pain concentrates in three concrete problems. We show how to build a learning loop around AI adoption that attacks all three: it reads real usage telemetry, gives every developer a personal level and personalized advice, gives managers an aggregate view — and stays firmly on the right side of the line between measurement and surveillance.

A lot of real work still starts on paper. Someone in the field fills in a work-order sheet by hand — what was done, where, how much, on which item — signs it, and it gets scanned. Multiply that by hundreds of locations across several branches and month-end billing turns into an archaeology project: someone retypes a thousand noisy scans into a spreadsheet line by line, then reconciles each row against the order list to find the right code, the right item and the right price. It's slow, it's error-prone, and the mistakes don't surface until they're already invoices. We built a controlled system that does this end to end — and it runs in production for a large organization with field teams, processing thousands of hand-filled work-order sheets a month.

Companies lose hours hunting the internet for what to order and who to order it from — then reorder by gut and find out too late that a supplier was unreliable. That costs real money: stock-outs that lose sales, dead stock that freezes cash, and margin left on the table for want of a price check. It concentrates in three concrete problems. A buyer changes that — it learns what you regularly buy, watches your stock, tells you what's running low and where to buy it best. It never places the order itself: it proposes, you decide, and your feedback makes it sharper.