AI process automation for businesses, built to run in production

Which processes do we automate with AI?

We automate repetitive work in which a person reads something unstructured, decides by known rules and types the result into a system. That is where AI adds something a fixed rule cannot: it reads free text, scanned documents and ten different phrasings of the same request.

Invoices deserve a note. In Romania, B2B invoices pass through the national RO e-Factura system, so many already exist as structured XML (Ministry of Finance, RO e-Factura), and e-invoicing is spreading across the EU. A good automation reads the structured file first and uses the AI model only for what is unstructured: foreign supplier invoices, scanned delivery notes, attachments, emails with delivery details. It is cheaper to run and wrong less often.

The technical detail for documents is in our guide on how to automate document processing with AI.

What is the difference between a Zapier, Make or n8n workflow and automation built as software?

The difference shows after the demo. A Zapier, Make or n8n workflow connects apps, and it is the right tool for many jobs. Automation built as software adds what matters once a process touches money, customers or legal duties: integration with systems that have no connector, measured quality, monitoring and a person who approves.

When is a no-code recipe enough? When every app has a connector, the volume is small, a mistake shows up at once and costs little, and someone in your team can maintain the flow. A web form that creates a CRM lead and sends an email does not need a custom build.

When is it not? When the process writes into an ERP or the accounts, when an error reaches a customer or a tax authority, when volume grows from dozens to thousands of documents a month, or when someone will ask how you checked that the AI is right. If you are not sure whether you need classic automation or an agent, read how to choose between an AI agent and RPA. For the next step up, a process with several decisions and tools, see custom AI agents.

Which technologies and systems do we work with?

We build on or integrate with the tools you already run. The list below is what we can implement or connect to, not a list of past clients; if your system is not on it but has an API, a database, an export or a portal, we can usually reach it.

Automation platforms

n8nZapierMakeMicrosoft Power Automate

ERP and accounting

SAPOracle NetSuiteMicrosoft Dynamics 365OdooSAGASmartBillRO e-Factura / ANAF SPV

CRM and support

SalesforceHubSpotPipedriveZendeskFreshdesk

Collaboration and messaging

Microsoft 365Microsoft TeamsSharePointGoogle WorkspaceSlackWhatsApp Business

E-commerce

ShopifyWooCommerce

Data

PostgreSQLMySQLMicrosoft SQL ServerREST and SOAP APIsCSV and Excel exportsSFTP

AI models

OpenAIAnthropic ClaudeGoogle GeminiMistralOpen-source models on-premise

What does "production-ready" mean for an AI automation?

It means you know how well it works before you switch it on, and you find out at once when it stops working. Every automation we deliver has:

• An evaluation set built from your real documents and emails, with the correct answer marked by the people who do the work today. We re-run it on every model or prompt change.
• Confidence thresholds: below the threshold a case does not enter the ERP; it goes to a person with the uncertain fields highlighted.
• A human in the loop for sensitive actions: payments, replies to customers, data sent to authorities.
• Monitoring: how many cases pass on their own, how many are corrected, what each costs, what changed since last week.
• A log of what the system received, what it decided and who approved it.
• Handover: code, documentation, runbook and evaluation set, in your own repository.

Most back-office automation does not fall into the EU AI Act's high-risk category, but that is checked per use case, not assumed. We write the classification into the project documentation. (General information, not legal advice.)

How does an automation engagement with Sapio run?

Every engagement starts with a paid, fixed-price sprint, before anything is built. We do not guess the cost of a process we have not seen on your data.

1. Scoping or audit sprint (a few days to two weeks). If you know which process you want automated, we scope it: a written specification, a test on a sample of your documents and a fixed price for the build. If all you know is that "we lose too much time on paperwork", we run an audit: two or three processes mapped, the data checked, and a list ordered by value and difficulty.
2. Fixed-price build (usually 6–12 weeks). Integration, evaluation set, thresholds, monitoring. It runs in parallel with your team first, then takes over the cases where it has proved itself.
3. Optional: an AI engineer inside your team. If one process should lead to the next, a senior Sapio engineer can work inside your team, on your systems, paired with one of your own engineers, so the know-how stays with you. Ask us about it.

The step-by-step, from choosing the process to the pilot, is in how to automate a business process with AI. If you are still asking what is worth automating at all, Vlad Tudor has written about that separately: what is actually worth automating.

Why Sapio?

Because we have put AI systems into production that run every day, not just demo workflows.

• ai-aflat.ro, built by Sapio: an AI assistant over 220,000+ Romanian legislative acts, updated daily. Every answer cites the law and links the official source. How we built it.
• An AI customer-support agent for retail, integrated with ChatGPT, delivered for a client that went on to raise a $4.7M seed round.
• A public-tenders agent that reads notices on e-licitatie.ro, Romania's procurement portal, and matches them against a company profile built from its past projects. In testing it identified the relevant tenders. It is not yet in production, so we publish no time-saved figures.
• Sapio was founded in 2021 by Vlad Tudor (Forbes Romania 30 Under 30, 2025), who leads every engagement personally.

The market context: among European companies that considered AI and did not go ahead, 41.6% cite incompatibility with existing equipment, software or systems, and 70.3% a lack of relevant expertise (Eurostat, KS-01-26-009, Table 7, reference year 2025, published 26 March 2026). Integration is the hard part, not the model.

What drives the cost of AI automation?

We do not publish prices, because "we process invoices" can be a week of work or three months. The cost depends on:

• The systems to integrate. An ERP with a documented API is simple; a local ERP with no API, or a portal with no export, means integration work.
• The state of the data. Born-digital or scanned, one format or dozens of suppliers with their own templates.
• Tolerance for error. A mistake in an internal report costs little; a mistake in a payment or a tax filing costs a lot, so thresholds and human review are stricter.
• Volume. It sets the monthly running cost (model calls, hosting), not only the build.
• Where it must run. Public cloud, your own cloud region, or on-premise for sensitive data.
• Maintenance. Suppliers change formats, models change, processes change. Automation without monitoring degrades and nobody notices.

The scoping sprint exists to turn that list into a fixed price for your process.