Systems integration and process automation.

When integration pays back faster than a new system.

The typical company we work with in this format has already bought software — a CRM, an accounting system, inventory management, an online store — and each of them works well on its own. The problem sits at the boundaries. An order from the store is retyped into accounting, delivery status is asked for by phone, and nobody has the whole picture until somebody assembles it in a spreadsheet.

This service is the right fit if:

• the same document or order is entered into two or three systems;
• management reports are assembled manually and are therefore produced less often than they should be;
• somebody's daily work can honestly be described as “take it from here, put it there”;
• an IT and AI audit has already been done and the starting point is known;
• a previous automation attempt ended with something nobody used.

When it is not the right fit. If it is not yet clear which process consumes the most time, integration will start from a guess. In that case an audit is cheaper and faster. Nor is this the service for companies wanting an entirely new system built from scratch — that is development.

Data flows that run without a person in the middle.

Automated data flows between existing systems

Every flow is concrete: where the data comes from, where it lands, what happens on error, and who is told. Typical flows we build — an order from the store into inventory and accounting, a quote from the CRM into an invoice, delivery status from the courier back into a customer notification, documents from email into the record system.

Reports and dashboards that update themselves

A management dashboard where the numbers come straight from the systems, not from a spreadsheet somebody refreshes on Mondays. We agree on five to eight metric rows that genuinely change decisions, rather than a full analytics tool nobody opens after a month.

AI applied to specific tasks

Where the task is understanding a document or a piece of text. More on that in the next section. Nowhere that ordinary integration already handles.

Documentation and training for your team

For each flow, a description in plain language: what it is, when it fires, what to do if it stops. Training happens with the people who run the process, not only with management. This point sounds like a formality, but in practice it decides whether the automation gets used or worked around.

Monitoring and error alerts

An integration that quietly stops is more dangerous than one that never existed — the company carries on relying on data that is no longer refreshing. So every flow has a log and an email alert if it stops working.

Stages that each pay off on their own.

The work is not one large project with a handover at the end. It is a sequence of stages, each going live before the next begins. That means you can stop at any point and what has been built keeps working.

Stage 1 — scoping and the first flow

2—3 weeks. We pick one process — usually the one consuming the most hours and the simplest technically — build it, run it alongside the existing process and compare results. This stage is also a test of whether the working relationship fits.

Stage 2 — switch over and measure

1—2 weeks. Once the flow runs correctly, the old path is switched off. We compare against the baseline recorded before the work started: how many times a week the process runs, how long it takes, how many people are involved. Without that measurement the saving would remain a claim.

Stage 3 — the remaining flows

4—12 weeks depending on scope. The next flows follow the same pattern, ordered by return. Reporting and the management dashboard usually join here, because only now does the data sit somewhere it can be collected from.

Stage 4 — handover

1 week. Documentation, access, training, monitoring settings. After this stage the system works even if we disappear — that is the goal, not a side effect.

AI where the task is to understand, not to move.

Most integrations do not need AI. Moving data between systems is rule-based work: it can be done precisely, cheaply and predictably. AI there would be more expensive and less reliable.

AI belongs where the incoming information is unstructured:

• Document processing. Invoices, delivery notes and contracts are read, classified and entered into the system. Accounting checks the result instead of retyping the data.
• Triaging incoming requests. Emails and form submissions are sorted by topic and urgency and land with the right person.
• Summaries. A short explanation attached to a management report of what changed and why.
• An internal knowledge assistant. Staff ask questions of company documents and get an answer with a pointer to the source.

In each of these we first establish which data may reach an external AI service at all, and where a solution running on your own infrastructure is required. That is not a legal formality but an architectural decision, and it has to be made before development, not after.

When a delivery process moves into the system.

At Verbasco, a B2B supplier of industrial components, every shipment ended with two steps outside the system: confirming that the goods had arrived, and signing the delivery paperwork. Both were handled manually, shipment by shipment. The automation pulled both inside Odoo, and the sales process began running in the system rather than around it. The work ran in stages — day-to-day deliveries never paused.

The full case study is currently published in Latvian only; ask for it on the call and we will walk through it in English.

Priced per stage, not per project.

Each stage is estimated and approved separately. You never sign for a sum covering work whose scope nobody knows yet — and we do not take a fixed price for work whose scope will only emerge along the way. In practice that makes the first stage the cheapest way to test whether the engagement is worth continuing.

Prices exclude VAT and are a starting orientation, not a proposal. The main variable is how readily your existing systems let data in and out: a system with a documented interface takes days, one without can take weeks.

Questions asked before an integration.