AUTOMATION & AI

Defined tasks,
explicit checks.

Workflows, integrations and AI tools designed around rules, authorised data and human handover.

Conceptual composition of an automated workflow. — Conceptual illustration of automation. AI-generated illustration.

Manual transfers, duplicate data entry and scattered follow-ups can make a process difficult to track. The starting point is not a promise of savings, but an observation of what actually happens between the form, CRM, invoicing, calendar and reporting.

Automation builds controlled transitions between tools. An AI model can be added when a task requires classifying text, extracting information or preparing a draft. It receives only the intended data, acts only within defined limits and does not replace the employee's responsibility.

Our approach starts with a baseline: volume, time, errors, exceptions, data and risks. We then define an acceptance criterion and a value scenario specific to the project. Any potential gains are only reported after the pilot has been measured.

Our expertise

From lead management to AI

Workflows, reporting and AI assistants designed around verifiable rules.

Lead management

Centralise, qualify and route according to explicit rules.

Automated email marketing

Prepare controlled, measurable sequences.

Automated reporting

Consolidate data at a defined frequency.

AI assistants

Design assistants with sources, limits and human escalation.

Use cases

What a workflow can handle

Four illustrative scenarios to explain the scoping process. They present neither client results nor expected gains.

The visual guide

Three distinct roles within one workflow

The rules

Trigger actions, check fields and route according to explicit conditions.
The model

Prepare a rewording, summary or draft for review.
The person

Approve sensitive points and handle exceptions.

AI is not needed at every stage. The actual workflow determines what can be automated.

Illustrative scenario · Incoming enquiries

Automatic qualification of incoming leads and generation of pre-filled quotes

A form can create a structured record, apply eligibility rules defined by the business and prepare a summary for the person responsible. The model can suggest rewording, but it does not reject an enquiry on its own, and a human handover route remains available.

Points to define: required fields, authorised criteria, enquiry owner, chosen internal response time, decision log and exception handling.

Illustrative scenario · Document search

Document-based AI assistant connected to internal files

An assistant can search an authorised corpus and display the passages used with each response. A citation improves verifiability but does not guarantee that the model has interpreted the document correctly.

Points to define: document permissions, versions, professional confidentiality, deletion, response tests, warnings and approval before reuse.

Illustrative scenario · Reporting

Consolidated sales reporting at a defined frequency

A pipeline can reconcile data from the CRM, accounting and acquisition platforms, then feed a dashboard at a chosen frequency. A model can prepare commentary, which remains a draft to be checked against the source figures.

Points to define: indicator definitions, reconciliations, time zones, source delays, quality checks and the person responsible for approval.

Illustrative scenario · Sensitive data

Pre-filled patient records and automated reminders

An appointment workflow can send a form or reminder according to the practice's rules. For sensitive data, the first question is whether an AI model is needed. Deterministic automation may be more suitable than generative processing.

Points to define: necessity, minimisation, access, hosting, processors, retention, incidents and approval by the competent people.

These scenarios describe possible architectures. Their feasibility, lawfulness, timeline and value can only be established from the actual system and a measured pilot.

Tool stack

Which tool for which need?

Our measured assessment of the 4 platforms we use most often in engagements.

Tool	Use to consider	Scoping question	Point to watch
n8n	Visual orchestration with a controlled hosting option.	Who manages updates, secrets, backups and incidents?	Self-hosting transfers responsibilities to the operator and does not guarantee compliance.
Make	Visual scenarios and hosted integrations.	How are operations counted, and which data passes through?	Check limits, regions, processors and pricing when making the choice.
Zapier	Automations between supported applications.	Do the connectors cover the actions and permissions actually needed?	The catalogue, limits, data processing and prices change over time.
Pipedream	Code steps and API integrations.	Who audits the code, dependencies, secrets and outbound network traffic?	Logs, permissions and technical responsibilities must be defined.

Kanexio does not present any platform as automatically compliant or superior. The decision is dated, documented and reviewed when terms, processors or features change.

Official sources to check when choosing

Method

How we work in practice

Four stages, in this order, with no shortcuts.

Observation and scoping

We observe the actual workflows: who does what, with which tools, how often and for how long. No PowerPoint, just an honest map of bottlenecks.

Prioritisation by value and risk

Options are compared according to usefulness, complexity, the data involved, exceptions and total cost. The first pilot is defined in the Kanexio proposal.

Build + real-world tests

Each workflow is tested against expected cases, errors and exceptions. Production rollout depends on documented acceptance criteria and a rollback plan.

Handover + follow-up

Documentation, training for the designated contact, agreed monitoring and an incident procedure. The precise arrangements are confirmed in the Kanexio scope.

Why Kanexio

Automation designed to remain verifiable

Proportionate architecture

Visual tool, native integration or targeted code according to the rules, risks and available skills.

AI under control

Limited data, verifiable sources, structured outputs and human handover according to the level of risk.

Traceability and monitoring

Logs, alerts, reconciliations and update frequency defined according to the available sources.

Frequently asked questions

Everything we are asked before getting started

What is AI automation for an SME in Brussels?

AI automation connects tools and business rules to a model that can classify, extract or suggest a draft. It can support enquiry qualification, preparation of a response or updating a tracking record. The useful workflow depends on the actual process, authorised data, exceptions and planned human oversight.

How do you estimate the time that could be freed up?

There is no reliable average applicable to every SME. Kanexio measures the current frequency, actual time spent, proportion of eligible cases, human review time, exceptions and maintenance. The resulting scenario provides a basis for a decision and is then compared with pilot data.

Which tools do you use for automation?

Kanexio can work with n8n, Make, Zapier, Pipedream and various model APIs. No tool is chosen on name alone. We compare integrations, access rights, logs, retention, hosting, reversibility, usage limits and commercial terms in force at the time of the project.

Is AI automation GDPR-compliant in Belgium?

Not automatically. Compliance depends on factors including the purpose, legal basis, data used, recipients, transfers, retention, individuals' rights and security measures. Kanexio documents the workflow and roles, then helps the controller determine which approvals and any impact assessment may be needed.

How much does AI automation cost for an SME in Brussels?

Displayed Kanexio commercial terms: a first workflow is scoped at between €1,500 and €4,500 excluding VAT, depending on the scope. An interconnected programme with management and handover may range from €8,000 to €18,000 excluding VAT. The quote specifies the deliverables. Licences, APIs and infrastructure are priced separately at the rates in force.

How long does it take for an AI workflow to become operational?

The timeline is confirmed after an inventory of the systems, access, data, exception rules, approvals and tests required. Kanexio distinguishes the prototype, pilot and production rollout so that a generic timeline is not presented as a commitment.

Will AI replace my team?

A project cannot predict how a job will evolve. Kanexio scopes specific tasks, retains a human owner and documents cases that must be handled manually. Effects on organisation and workload are then measured with the team.

What is the difference between a chatbot and an AI assistant?

A deterministic workflow follows predefined rules. A model-based assistant can interpret wording and suggest a response based on context or documents. This flexibility also introduces risks of error: sources, permissions, limits and human escalation must be explicitly designed.

To explore further

Resources and sectors of application

AI automation by sector

Reading on AI and AEO

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