Product, data, architecture
and security under one responsibility

We combine a product perspective, data architecture and enterprise security, so the AI workflow makes sense in real operations.

We don’t start with the model. We start with how data, processes and decisions actually work inside the company.

Team

Two roles, one responsibility for the result

One owns the workflow design, integrations and operational usability. The other owns security, governance, BI and trust in the data. Together we lead the project from mapping the process through to a workflow that holds up in operations and under audit.

Michael Lapčík

Workflow architecture · Implementation · Product

Michael connects product thinking, UX, data and delivery. For nearly twenty years he has helped companies turn ideas into working digital solutions - from strategy through design to delivery.

At Inovai he owns the workflow design, user logic, integrations and the operational usability of the solution.

He has worked for teams and brands such as Windy, CZC, Komerční banka, Alensa, T-Mobile, Nvidia, Asus, Allegro, Meteoblue and Uber.

At Inovai he is mainly responsible for:

workflow and target architecture design,
implementation and integrations,
data flows and validations,
process UX and handling exceptions,
operational usability of the solution.

His role: making sure the workflow isn’t just a good design, but a system usable in day-to-day operations.

Radek Palla

Security · Compliance · Data Foundation · BI

Radek has 14+ years of experience in the enterprise environment of Forvia Hella Mohelnice - from IT consulting through leading an international security team to BI architecture over MES data from more than 35 production plants.

At Inovai he owns the security, data and governance layer of the workflow: data lineage, access rights, the audit trail, reporting and the deployment architecture.

At Inovai he is mainly responsible for:

the security and data layer of the workflow,
Data Foundation - sources, quality and relationships,
access rights, logging and audit,
governance of data and outputs,
BI and reporting over operational data,
cloud, private or local architecture.

His role: making sure the workflow is secure, measurable and trustworthy for operations and management alike.

Combination of competencies

An AI workflow isn’t just a model. It’s product, process, data and security.

For critical workflows it isn’t enough to plug in a model. You need to understand the process, the data, integrations, people, security and operations.

Product perspective

We address who uses the workflow, where the decision happens and what needs to be understandable to a person.

Technical architecture

We design data flows, integrations, validations and an operational foundation that can be measured and extended.

Security and governance

We address access, logging, output validation, the audit trail and data sensitivity.

Operational responsibility

We treat the pilot as the start of a workflow that can be operated, tuned and developed over the long term.

Principles

Technology is a means , not the goal

The principles we hold across clients, sectors and project sizes.

We start with the process

First we understand how the work really happens, where errors arise and who should decide.

We validate on real data

We don’t judge AI by a demo, but by accuracy, error rate and impact on the process.

We give the human a clear role

For critical decisions, AI should prepare the basis, not take over the responsibility.

We think about auditability

An output should have a source, a reason and a rule you can trace back to.

We say when AI doesn’t make sense

When a process, a rule or a simpler tool solves the problem better, we say so.

Product, data, architectureand security under one responsibility