DeGNZ Manifesto
Modern companies need AI workforces that can be modeled, hired, and reliably integrated with human teams. That requires an infrastructure layer that starts with process modeling and ends with repeatable, trustworthy execution across people and agents. When work is clearly modeled, AI workers become dependable teammates rather than experiments.
We turn operations into a place where people and AI do their best work together.
- • Clear maps of real operations.
- • AI workers with defined roles, tools, and access; measured by the same KPIs as humans.
- • Human in the right loop; approvals, escalations, and overrides are part of the design, not afterthoughts.
- • A runtime that delivers consistent outcomes; observed, tested, and improved over time.
Mission
to provide infrastructure where humans and AI workers run business processes together; closing execution and management gaps to accelerate growth.
Vision
Every company fields a reliable, scalable, and continuously improving AI workforce that runs core business operations.
Why Now
Ops are under-modeled
Most work lives in wikis, chats, and tacit knowledge. This hides bottlenecks, creates rework, and blocks automation. Modeling processes as they actually run exposes gaps, clarifies ownership, and reveals where AI workers can safely take load.
Agentic AI skills matured
Planning, tool use, and multi-step execution are now good enough for real operations, provided they run inside an environment that gives them the right context, permissions, and tests. Reliability improves when agents are treated like teammates with roles and policies, not like ad-hoc prompts.
Trust gap
Human teams lose confidence when outputs vary, when there is no clear escalation path, or when systems ignore existing roles. Trust grows when the platform makes decisions auditable, routes edge cases to humans, and shows performance.
Integration imperative
Value shows up only when AI workers are integrated end to end. That means connectors to the actual tools of work, identity and access that match company policy, and observability that lets leaders see quality, cost, and time in one view.
Core Beliefs
We are building the AI Operations Infrastructure that makes autonomy feel like adding a dependable teammate. Our bias is toward safety, clarity, and measurable uplift. We prize fit with real teams and their systems. Our north star is repeatable execution that customers can trust, inspect, and steadily scale.
Process comes before automation.
Clarity beats speed. Before we ask an AI to help, we name the work, the intent, and the few simple rules that make it successful. When everyone sees the same picture of how the work should flow, technology amplifies that clarity instead of adding noise.
AI workers are teammates, not tools.
Teammates carry responsibility, learn, and improve. We expect AI workers to do the same. They have a purpose, they fit into roles beside people, and they earn trust by showing up reliably where it matters most.
Human in the right loop builds reliability of AI workers.
People set direction and make the calls that require judgment. AI supports the flow, handles the routine, and flags the unusual. By keeping humans where meaning and nuance live, outcomes stay dependable and organizations stay confident.
Change should be progressive.
Real progress comes from small wins that accumulate. We start focused, learn in the open, and widen the circle when the work proves itself. No grand promises, just steady steps that last.
Observability drives improvement.
You cannot improve what you cannot see. We keep the work visible, compare outcomes to intent, and learn from each run. Honest visibility turns mistakes into lessons and good results into habits.
Transparency creates trust.
Trust grows when people can see what happened and why. We make histories easy to follow and invite questions. Openness is not a feature, it is the foundation of partnership.
Teams need rhythm.
Good teams move with a beat. We respect routines, share changes early, and choose moments that protect the flow of work. A steady cadence keeps energy high, surprises low, and progress continuous.
AI Ops Infrastructure
The full-stack platform that provides the tools to model real-world business operations processes, then design, hire, and operate AI workforces safely at scale.
Ops 360°
The shared map of how work really happens. It shows the steps, decisions, and handoffs across people and agents, plus where time is lost and quality slips. It is also the daily workspace. Teams assign and track tasks, review progress, and see live performance dashboards in one place. With one clear picture and one home, everyone knows what matters today and how the operation is trending over time.
AI Workers Factory
The place where roles are designed and workers are built with purpose. Teams choose what success looks like, pick the tools they can use, and write the simple rules they follow. Work is tried in practice runs, improved with feedback, and only then put to work. Creators can publish workers to the Market, set price or sharing terms, and get paid when others hire them. Reputation travels with each worker so the best ideas spread.
AI Workforce Control Center
The daily cockpit for running the AI workforce work. Leaders assign tasks, watch flow, and handle exceptions. Every action has a reason that is easy to read, and handoffs to people are smooth. The team stays in charge while AI workers keep the pace and remove friction.
AI Training Center
The gym for skills and judgment. Workers learn from real examples, coaching from humans, and lessons from past runs. They practice via simulations until the work is consistent, then keep learning as the process evolves. Improvement is quiet and continuous, so teams experience reliability rather than surprise.
AI Workers Market
The place to discover, try, and hire proven AI workers. Each worker has a clear profile with purpose, skills, and a simple record of results. Teams can preview work on real tasks, then hire for their own use or adapt to their context. Creators set pricing and terms, and earn whenever their workers are hired. Companies can keep listings private inside their walls or share them with the community. Reputation grows with every successful run, so the best workers rise to the top.
Conclusion
We are builders of dependable operations. We begin with operations mapping, welcome AI workers as teammates, and make the change one win at a time. We measure what matters, speak plainly about results, and protect the rhythm of work. This is how an AI workforce becomes real.
Stand with us if you want operations you can trust, inspect, and improve.