Business Automation and System Integration
Business automation is the work of taking recurring manual steps out of how a company runs, usually by making systems that were never designed to talk to each other exchange data on their own. Elevio Solutions is a software development company in Belgrade that builds two kinds of it: integrations that keep systems in step, and AI assistants that answer from your own content.
Two kinds, named plainly, because automation is a word that gets sold as a category and delivered as a demo. These are the two we run in production in our own products, which is a different claim from having read about them.
Integrations That Keep Systems in Step
A shop, an ERP and a CRM, each holding its own version of the truth about the same order. Somebody closes that gap by hand every day: retyping orders, correcting stock counts, copying customer records across, chasing a price that changed in one place and not the other. The work is invisible until you add it up, and then it is a salary.
What we build is the layer that does it instead. Orders move from the shop into the ERP as they land. Stock and prices come back the other way on a schedule you set. Customer records stop existing in three slightly different versions. The systems stay where they are, because replacing a working ERP to fix a data problem is the most expensive way to solve it.
The engineering questions are always the same three, and they are the reason this work is worth paying a developer for rather than wiring together in a no-code tool: what happens when the other system is down, what happens when the same message arrives twice, and how you find out something failed without a person noticing. Retries, idempotency and alerting are the difference between an integration you trust and one you check every morning.
AI Assistants That Answer From Your Own Content
The second kind is an assistant that answers questions using your material and nothing else: your documentation, your product pages, your support history. It hands the conversation to a person the moment it cannot answer, and it is engineered so it is never allowed to guess.
That last part is the whole job. A general model asked a question it has no basis for will produce a confident, fluent, wrong answer, and in a support context a confident wrong answer costs more than no answer at all. So the model is constrained to work from retrieved material rather than from memory, and the handover to a human is a designed path, not a fallback nobody tested.
We run this ourselves. Our site assistant is live in production answering real users, and Help Forge, our own support plugin, ships AI-assisted replies alongside its ticketing and email channel. AI in the engine, humans at the wheel.
What Automation Is Worth, and When It Is Not
Every engagement starts by measuring what the current process actually costs: how many times a week it runs, how long it takes, how often it goes wrong, and what a mistake costs to unwind. If that number is small, automating it is not worth your money, and we would rather tell you on the call than after an invoice.
The cases that pay for themselves quickly tend to share a shape. The process runs often. It follows rules a person can write down. The data already exists in a system with an API. And the cost of a mistake is high enough that consistency is worth something on its own.
| Aspect | Done by hand | Automated |
|---|---|---|
| Cost shape | Grows with volume, because every order is somebody’s minutes | Mostly fixed, paid once at the build |
| Consistency | Depends on who is working and how busy the day is | The same every run, including at 3 a.m. |
| Errors | Found later, by a customer, in the version that reached them | Caught at the boundary and raised as an alert |
| Knowledge | Lives with the person doing it, and leaves when they do | Written down as code and readable by the next developer |
| Scaling | Hire another person | Raise a limit |
| Honest limit | Handles the exception nobody anticipated | Needs the exception path designed in advance |
That last row is not a disclaimer. It is the reason discovery matters: the exceptions are where an automation project succeeds or quietly becomes a second manual process.
How an Automation Engagement Runs
1. Map the process
We follow the work as it happens now, step by step, including the parts people do not think of as steps. Most processes contain one decision that looks like a rule and is actually judgement, and finding it early decides what the system is allowed to do on its own.
2. Fixed written scope
You get a proposal before any code: which steps get automated, which stay with a person, what the exception path is, and what it costs. What is explicitly out of scope is the most useful page in the document.
3. Build against the real systems
Integrations are built and tested against the actual APIs, in sandbox where one exists, with logging from the first day. An automation you cannot inspect after the fact is an automation you cannot trust.
4. Run it beside the manual process first
For the first cycles the automation runs alongside the people doing the work, so the two can be compared on the same data. This is where the exceptions nobody remembered turn up, and it is far cheaper to find them there than in production on your customers.
5. Hand over, or stay on
Either we hand over documented code and monitoring your team can operate, or we stay on as the ongoing partner. Automations need an owner: an integration nobody watches is a silent failure waiting for a quiet week.
The Proof Runs in Our Own Products
We do not have a client automation case study to show you, and we would rather say so than dress up a demo. What we do have is the same engineering running in software we operate ourselves, in public, every day.
- Polyglot Translate Cloud is a translation platform live in production on PostgreSQL with Redis and BullMQ job queues, a three-engine translation cascade, and subscription billing wired to an external provider end to end. Every release is checked automatically before it reaches a subscriber. Background jobs, retries and third-party integration are not a slide for us; they are the thing that has to be up on a Sunday.
- Polyglot Connector is published on WordPress.org and connects a WordPress site to that platform. That is a system integration in the plainest sense: two products, two release cycles, one contract between them that has to keep working.
- Help Forge is our support plugin, with ticketing, team workflows, an email channel and AI-assisted replies, live and handling real tickets for its first client.
- Our site assistant is in production answering real users, built on the rule that it is never allowed to guess.
If you want the longer version of how we build software, it is on our SaaS and software development page, and the work itself is in the portfolio.
What It Costs
Automation is scoped case by case, because a two-system sync and a full ERP integration are not the same job and pricing them the same would mean overcharging one of you. The published floors for our engagement types are on the services page, every project gets a fixed written proposal before any code, and ongoing partnership after launch starts at EUR 350 per month.
If you are not sure the work is worth automating yet, that is a consulting conversation rather than a build, and it is a cheaper place to start.
Frequently Asked Questions
What kind of automation do you actually build?
Two kinds. Integrations that keep systems in step, typically a shop, an ERP and a CRM exchanging orders, stock, prices and customer records instead of a person retyping them. And AI assistants that answer from your own content and hand over to a person when they cannot. We name the two rather than listing everything the word could cover, because we would rather be the studio that says what it has built.
Can you automate this without replacing our ERP?
Usually yes, and that is the preferred answer. If the ERP works and the problem is that data does not move between it and your other systems, replacing it is the most expensive possible fix. We build the layer between the systems and leave them where they are. Where a system has no API at all, that changes the options, and we will tell you that during discovery rather than after.
How do we know the automation is still working?
Because it tells you. Every integration we build logs what it did, retries what can safely be retried, and raises an alert when something fails in a way it cannot resolve. The failure mode that matters is not an automation that breaks loudly; it is one that quietly stops running and nobody notices for three weeks.
Will an AI assistant make things up about our business?
Not if it is built correctly, and that is a design decision rather than a hope. The assistant answers from your own material rather than from the model’s memory, and it hands the conversation to a person the moment it has no basis for an answer. We hold our own products to that rule, including one running in a context where a confident wrong answer would be a real problem.
How long does an automation project take?
Scope decides it, which is why scope is fixed in writing before any code. The larger variable is usually not our build time but access: API credentials, a sandbox environment, and someone on your side who knows what the process really does. Projects that stall almost always stall there, so we agree those three things before the start date rather than after.
Start With the Process, Not the Tool
Tell us which task keeps coming back and who does it today. We will tell you honestly whether it is worth automating, and if it is, you get a fixed written proposal before any code. Get in touch and you will have a reply within 24 hours, from the person who would do the work.