AI challenge

We can't get the solution connected to existing systems

The AI demo works. It just still needs to fetch data from system A, change something in system B and respect permissions from system C. That is where an AI project often becomes a serious software engineering project.

Does this sound familiar?

Often the AI part has already been proven and the real technical blocker lies in everything around it.

  1. The API doesn't quite do what you need

    Documentation is limited or outdated, or the desired flow doesn't map neatly onto the available endpoints.

  2. Authentication between systems gets stuck

    Tokens, service accounts or user delegation make the integration more complicated.

  3. Data drifts apart

    Different systems use different identifiers, statuses or data models.

  4. The happy flow works, errors don't

    As soon as an external system is temporarily unavailable, the workflow loses track.

Where do you want to be?

The AI shouldn't stay an isolated island. It should become a reliable part of the systems and processes that already exist.

Reliable data exchange

Data arrives at the right moment, in the right format and at the right system.

Errors are manageable

Timeouts, duplicates and temporary outages don't immediately lead to unpredictable behaviour.

Permissions stay correct

The integration respects existing users, roles and access rules.

Why does this happen?

An integration is more than a line between two boxes in an architecture diagram. It also has to cope with real-world conditions.

See how we approach this

Systems speak different languages

Data models, statuses and responsibilities rarely map one-to-one.

External systems are not always available

Retries, queues and error handling become important as soon as the integration reaches production.

AI actions have real consequences

When an agent is allowed to change something, it must be clear who is responsible and what happens when something goes wrong.

Why 9to5 for this?

Building integrations is at the core of how 9to5 software works. AI only becomes useful when it can work safely with real data, software and people.

View cases

Existing software is the starting point

The organisation doesn't have to adapt to the AI solution first.

Engineering around the API

We also design retries, error handling, logging and permissions.

No demo integrations

The integration has to stay usable under real conditions.

What else fits your situation?