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AI for Business Processes - Automation Connected to Your Systems

A case moves through the process faster, because data isn’t retyped by hand between systems. We design AI as part of a specific process, not as a tool sitting next to it.

Order an audit of one process
Who it's for

When one case passes through several hands and several systems

An inquiry, an order, a complaint, documentation, client onboarding: before it’s closed, it travels between people and tools.

We address this solution to operations directors, department managers and people responsible for how well a process runs, not for the technology itself.

Where AI adds value to automation

Four things AI adds to automation

Process automation on its own is not new. AI changes four specific things within it, though, and brings entirely new value to improving business efficiency.

Information retrieval

Finding the right information in documents, procedures and company content, without needing to know file names or folder structure. This is the best-tested part of our platform.

Content preparation

A draft reply, summary or piece of correspondence, prepared for a person to approve.

Analysis and classification

Reading what has come in, extracting the data needed, and recognising what the case concerns and where it should go.

Rule-based action

Carrying out an established step in the process: creating a task, passing on a case, updating a record. The client sets the rules; the system applies them and does not create them on its own.

Examples of a process supporting client decisions based on their behaviour

Handling an inquiry in an online store

A module that makes it easier for the client to decide and speeds up finalising the order, which we continually develop.

Today:

The buyer searches the catalogue alone, and questions asked after hours go unanswered until the next day.

After implementation:

The assistant answers from the store’s knowledge base, recommends a specific product and captures the contact. The request goes to the CRM, and the salesperson gets a reminder if no one has replied.

Model scenarios

Four typical processes where AI makes sense

The scenarios below show the type of process: model scenarios based on the areas most commonly reported where AI support works best. During the audit, we check which of them is closest to your situation.

Handling a quote request

Today:

The inquiry waits in the inbox until someone gathers information for the offer and finds time to respond.

After implementation:

It goes straight to the right salesperson together with a draft reply based on the company’s materials. The salesperson checks it, corrects it and sends it.

Effect:

Removes a bottleneck: the time between the inquiry arriving and the first meaningful reply.

Handling a complaint

Today:

The report circulates between people because the data needed to resolve the case is missing.

After implementation:

The system checks completeness, asks the person who filed the report about what’s missing, and passes the complete case to the decision-maker. The decision always belongs to a person.

Effect:

Shortens the time between the report and the moment the case is ready to be reviewed.

Onboarding a new client

Today:

No one knows which documents from the client have already arrived and which are still missing.

After implementation:

A signed contract triggers a list of steps; documents and data are collected through one channel, and completeness is checked against an established list. Missing items generate reminders, and once everything has been gathered, the case moves to the delivery team.

Effect:

Removes the most common onboarding problem: no one knows where things stand.

Production documentation workflow

Today:

Documentation circulates by email, and reconstructing afterwards who approved what and when takes hours.

After implementation:

The document is assigned to an order or batch, checked for the required attachments and passed on, with the approval history preserved.

Effect:

The main value is the ability to reconstruct the course of events after the fact.

Implementation process

From audit to production

This order is not a formality. The audit is sometimes the only stage needed. Sometimes it turns out that the problem is not a lack of automation but the way the process was designed. When that’s the case, we say so directly.

1

Audit

We map out where the case flows, how long each step takes and where manual work occurs. Result: a process map with the points where automation will have the greatest effect.

2

Pilot

One part of the process, one category of cases, or one team. With success criteria set in advance and a baseline measured before the start.

3

Implementation and integration

Connecting to the systems where you actually manage cases. We set the scope of integration after checking what is realistically possible in your environment.

4

Development

We check the effect against the pilot data and, on that basis, decide together whether to extend the solution to further parts of the process or stop where it is.

Integrations

We connect to the systems you actually work in

Cross-system integration is one of our core competencies. We have been delivering it for years in ERP, CRM and production-system projects. We add AI as a layer on top of what you already have, not as another separate tool to operate.

CRM

The case reaches wherever you manage the client, together with context and history, without manual retyping.

ERP

Data from the process enters the system where you handle your accounts, instead of circulating in spreadsheets and emails.

WMS and MES

Information from the shop floor and warehouse links up with the rest of the process, and documentation reaches the right order.

E-commerce

The catalogue, orders and buyer inquiries feed the service process without extra work for the team.

Mail and documents

What arrives by email or as a document becomes a source of knowledge and a starting point for the process.

Internal applications

We also connect systems built specifically for you, including when they don’t have a ready-made integration interface.

Every environment looks different, so we determine the scope and method of integration during the audit. We check what data is available, in what form, and what can be done with it without interfering with critical systems. The quote is based on this.

We don’t make you change the systems you work in. We fit AI to your environment, not the other way around.

Example from our projects

One client’s web application was to be moved to the public cloud, but it ran on software versions the target environment no longer supported. We carried out a step-by-step upgrade of the framework across several successive versions, updated the runtime environment to a version supported in the cloud, rebuilt file handling for cloud storage, and fixed the vulnerabilities identified in the security report. We then continued developing the application according to the client’s new requirements.

We work in existing, often neglected environments, not only on new projects. This is the same type of work that lies behind connecting AI to systems that have been running at your company for years.

Decisions that require a person

We set the boundary before work begins, not after

You set the rules. The system applies them: it does not create or change them on its own. When a case falls outside the rules, it goes to a person instead of being resolved by guesswork.

Always on the human side

  • Decisions with financial consequences: prices, terms, discounts, accepting a complaint
  • Decisions with legal consequences and commitments made on the company's behalf
  • Decisions requiring judgement of a situation or a departure from the rules
  • Approval of content going out to the client on the company's behalf
  • Approval of data from a document before it enters the system

On the system's side

  • Carrying out steps according to rules you have set and approved
  • Finding information and preparing it for a decision
  • Flagging gaps, delays and cases that need attention

Metrics

What this delivers

Before the pilot, we establish what we want to measure, together with a baseline, so that after implementation we can honestly say what changed and what effect the AI implementation brought.

1

Handling time

From the case arriving to its closure.

2

Manual cases

How many cases still require retyping data.

3

Number of errors

Mistakes made when transferring information by hand.

4

Cost per case

The unit cost of handling, if it can be sensibly calculated.

Cooperation with GOTOMA GENERAL

When a project crosses the boundary between software and investment

In that case, we deliver it together with GOTOMA GENERAL, which is responsible for the general contracting of IT environments: architecture, infrastructure, hardware, contractor coordination and bringing the investment to operational readiness. It also coordinates AI implementations, including business process optimisation, and performs an advisory function: selecting suitable solutions and supporting project financing.

Technical criteria: when the way data is processed decides

  • Data and the model cannot leave the client's infrastructure
  • A dedicated environment is needed instead of a shared one
  • Applies to companies with data subject to special requirements, regulated entities, and organisations with a policy prohibiting sending data outside.

Business criteria: when the shape of the investment decides

  • Hardware or physical infrastructure: servers, network, industrial equipment
  • Coordinating several contractors: one point of responsibility
  • Grant funding or a tender procedure: involved from the start
  • Several locations or entities: branches, plants, special-purpose companies
  • A change within the ERP or MES itself, not just a connection to it

What changes for the client: the project additionally covers an infrastructure or coordination layer.

What doesn’t change: there remains one team, one point of contact and one responsibility for the result. We don’t pass the client between companies.

AI and automation

How this differs from Business Process Automation

We offer both, and it’s worth knowing when each makes sense.

Business Process Automation

  • Flows based on unambiguous rules: if condition, then action.
  • Works well wherever the process is predictable and data arrives in a fixed form.

AI for Business Processes

  • Adds a layer that handles what cannot be described by a rule.
  • Content written in natural language, documents with varying structure, questions asked in many different ways.

Most projects need both approaches: rule-based automation as the skeleton of the process, and AI where unstructured data comes in. The audit shows what you need and where; sometimes the answer is “plain automation is enough here”, and when that’s the case, we say so.

Order an audit of one process

We’ll show you where automation will pay off and where it’s worth supporting it with AI.

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