AI that becomes part of the work, not a chatbot that gives advice.
An agent only gets interesting when it goes beyond generating text. Our Robos read information from real systems, make decisions within a defined task, create concrete output and move processes forward. Sometimes on their own, sometimes with a person as a checkpoint. That is how we are building, step by step, a team of specialised AI tools for our own work.
The case in brief
67% of Helma's answers usable right away
Mae examines repositories and creates concrete issues
Mark goes from signal to publication and feedback
Nola creates Dutch transcripts and minutes
Why not just one AI assistant?
Many business processes seem suitable for AI, but in practice they have very different context, permissions, risks and actions. A generic assistant with a bit of access to everything quickly becomes hard to control. That is why we choose specialised Robos, each with a clear area of work.
See the approachThe work is spread over real systems
The information an agent needs is not in a single prompt. It lives in GitHub, Slack, product documentation, mailboxes, customer data, Google Calendar, Meet, Drive, RSS feeds and other operational systems.
Just generating an answer solves little
A maintenance agent must be able to deliver a usable GitHub issue. An incident agent must turn information into a manageable incident. The process has to actually move forward.
Every task needs different boundaries
Sometimes a Robo may act on its own. Sometimes an employee must review or confirm first. We design that boundary per workflow, instead of hoping afterwards that the model does the right thing.
How do we build the Robos?
- 01
One Robo, one clear problem
We do not start with the model but with the task. Helma does support, Mae works on software maintenance, Indie on incidents, Mark on marketing and Nola on meetings. This keeps the context small enough to understand and test well.
- 02
AI gets access to the right context
Depending on the task we use, among other things, Skills, vector stores, repository snapshots, live APIs, historical data, business rules and current information from connected systems.
- 03
Output becomes part of the workflow
A Robo delivers more than a suggestion. Output can end up in a helpdesk ticket, GitHub issue, incident record, marketing schedule, backlog or minutes with action items.
- 04
People stay where they add value
For us, human-in-the-loop is a design choice. Helma has answers reviewed, Indie has an incident adjusted or confirmed first, and Mark has review moments before customer-facing content goes ahead.
What did we build?
The Robos are separate tools and products, each with its own workflow and integrations. Not every Robo needs an application of its own. Together they form our internal lab for AI that actually has to work in production.
Customers and marketing
Robo Helma combines support knowledge, customer context and technical information for customer answers. Robo Mark picks up signals from sources such as RSS, Slack and ideas, creates content, supports review and planning, publishes and uses performance as input for the next generation.
Engineering and operations
At fixed moments, Robo Mae analyses a commit-pinned snapshot of a repository, combines it with current first-party documentation and can create validated maintenance issues in GitHub. Robo Indie turns a Slack thread into a structured incident draft with an internal explanation, a customer-safe explanation and a workaround where available. Stanley works directly from ChatGPT: he watches our GitHub backlog and runs the weekly stand-up, so open issues do not go unnoticed.
Meetings, media and the Robo landscape
Robo Nola retrieves Google Meet recordings and uses its own Dutch ASR pipeline to create transcripts, summaries, action items and minutes. Robo Vidar is our video generation workspace for tools such as Seedance and Veo. Robo Dolly keeps track of active Robos and their history. Robo Rolf is the rollout Robo under construction; the current application deliberately contains only the technical foundation until the release workflow is defined sharply enough.
The Robos are not isolated from each other
As more processes are automated, specialised agents can also use each other's controlled output. The result is a network of small systems with clear responsibilities instead of one big all-knowing super agent.
Indie feeds Helma
Robo Indie is the source of truth for current incidents and offers a protected read-only API for it. Robo Helma can use that information when a support answer depends on a current outage.
GitHub is a work environment for several agents
Mae uses GitHub to record maintenance work as concrete issues. Stanley uses the same backlog in a different way: watching what gets left behind and periodically bringing attention back to open work.
Dolly keeps the whole visible
Robo Dolly gives an internal overview of which Robos are active and stores their history. This keeps the growing landscape of specialised agents itself manageable too.
What does this approach deliver?
AI really takes over a defined piece of work
The Robos do not stop at analysis. They deliver a usable artefact or move a workflow forward: an answer, GitHub issue, incident, publication, backlog action or set of minutes.
Integrations make the difference
The value comes from connecting AI to the systems where the work actually happens. A concrete example is Robo Helma, which can use current incident information from Robo Indie when answering support questions.
We learn from real production problems
Because we use the Robos ourselves, problems with context, permissions, retries, security, evaluation and human-in-the-loop do not first surface at a customer. We solve them while the system is really being used.
These are not standalone AI demos
Behind the Robos are ordinary software engineering principles: authentication, authorisation, queues, retries, structured output, tests, monitoring and explicit data boundaries. The language model is one part of the system, not the system itself.
View casesMeasurable quality instead of AI magic
Robo Helma, for example, measures what support agents actually do with an AI draft. Currently 67% is sent directly, 18% slightly adjusted and 15% fully rewritten. That lets us keep improving in a targeted way.
Autonomy is earned per task
Within a preconfigured maintenance job, Mae may automatically publish a limited number of strictly validated GitHub issues. Indie, on the other hand, first creates a draft that an employee can adjust, cancel or confirm. The degree of autonomy follows the risk.
Not every agent needs an application of its own
Some Robos have a complete interface and backend of their own. Others can be deployed much more lightly. Stanley, for example, works as an agent in ChatGPT and uses GitHub as a work environment for backlog management and the weekly stand-up. The form follows the problem, not the other way around.
Related AI challenges
Have an AI agent built
You know which task or workflow the agent has to perform. Now it needs to be designed well, connected and made reliable enough for real use.
View use caseHave an internal AI assistant developed
You want to give employees faster access to internal knowledge, documents and systems through an assistant that fits your organisation.
View use caseWhich processes are suitable for AI or automation?
You see opportunities to make manual work smarter, but first want to know where AI actually adds value and where regular automation fits better.
View use caseMake your own documents, data and knowledge usable for AI
The valuable information is already there. Now AI has to be able to work with it reliably, securely and practically.
View use caseAdd AI to existing software, an app or SaaS
The product already exists. Now you want to add AI functionality without losing sight of the existing architecture, user experience and reliability.
View use caseBuild & Integrate
We design and build an AI agent around a concrete process, including the required knowledge sources, system integrations, guardrails and human-in-the-loop.
View offerFixed price after scoping
- Result
- A production-ready AI workflow that is truly part of your process
- Duration
- Depends on scope
- Next step
- First we determine exactly which task the agent must carry out and which systems it needs