Agents do the work. Senior practitioners own the outcome.
The agent model is how Dobler Data Solutions delivers. Proprietary agents carry the data lifecycle (ingestion, modeling, governance and operations) continuously, supervised by senior practitioners who set the architecture and own the result.
Your data work, without the headcount
The old model was a bench of consultants billing by the hour. In ours, agents build pipelines, maintain models, monitor performance and resolve drift, while senior practitioners set the architecture and stay accountable. The fee is flat, set by the scope.
Continuous execution
Agents run the data lifecycle around the clock: ingestion, transformation, governance and operations. The work does not stop for handoffs, and knowledge does not walk out the door with a person. A project moves without waiting on handoffs, and if you ask us to run the platform afterwards, the same agents keep it running.
Practitioner supervised
Agents do the work; senior practitioners own the architecture, enforce the standards and stay accountable for the results. You get the consistency of automation with the judgment of experienced engineers behind it.
When a situation is genuinely new (an unusual source, an ambiguous requirement, a strategic trade-off), a person decides.
Governed by design, self-healing in operation
Every pipeline is built to the same disciplined standard, with governance and lineage enforced automatically: no tribal knowledge, drift found and resolved, and a full audit trail.
In operation, agents monitor, detect and resolve issues before they reach a ticket queue, so the platform keeps itself healthy instead of waiting to be fixed.
Governance by design, in depthThe agent model, written down
How agent-operated infrastructure works, what makes a data platform AI-native, and why we price by scope instead of by the hour.
Questions about the agent model
What is the agent model?
Instead of a team doing every step by hand, proprietary agents build and run the pipelines, governance and reporting, supervised by senior practitioners, on Microsoft Azure and Microsoft Fabric.
Is it fully automated? Who is accountable?
Agents do the repetitive build-and-run work. Senior practitioners supervise, review and stay accountable for the result. It is automation with human judgment on top, not unattended automation.
How is it different from hiring a data team?
A team means recruiting, salaries and management before the first result. The agent model starts from the same foundation our own products are built on, so the work begins with the build, not with hiring.
What does it deliver?
Governed data pipelines, a single source of truth, and reports and dashboards, built on the Microsoft data stack and, on request, maintained by us.
See what the agent model would do with your data.
Tell us about your environment, and we will walk you through how we would build and run it.