A practice built around the work
Zen Aegis is a Phoenix-based custom AI and workflow practice, owned and led by Robby Prochnow. We work with businesses whose people spend too much time moving information between systems and too little time using it. Our starting point is the work itself: the documents people read, the handoffs they repeat and the decisions that need their judgment. [2]
A business rarely describes its problem as a missing application. It describes an inbox nobody can get through, records that take too long to assemble, a trial that never receives a useful follow-up, or a process that depends on one person remembering the next step. Those are concrete places to begin. Understanding the job comes before choosing the technology.
Align: understand the process and the people
We begin by mapping how work moves today and where it slows down. That includes the tools already in use, the information each step needs and the person responsible for the result. A workflow can look sensible on a diagram and still fail because a handoff is unclear or the team cannot fit it into its day. [1]
Organizational readiness belongs in this conversation. Previous automation attempts, points of resistance and the team’s existing capabilities help determine what should happen first. The result is a sequence of work with a defined purpose. For a regulated or sensitive workflow, the review and access boundaries need attention at this stage, before a build has made them harder to change.
Build: make a bounded part of the process work
The build can be a custom application, an automation pipeline, an agent workflow or an integration between tools a team already has. We focus on a job that can be described and evaluated. A working example gives the people who use the process something concrete to review: what it receives, what it prepares and what happens next. [1]
Testing and team preparation are part of that work. A useful interface must make sense to its users, and the workflow around it must explain where a person reviews, corrects or takes over. The agreed scope determines the deliverable and the responsibilities around it. A demonstration, a delivered application and an ongoing operating arrangement have different purposes.
Operate: keep the system useful as the work changes
The process does not stand still after software is delivered. Outside services change, people develop new needs and exceptions appear in real use. Operating work includes monitoring, feedback, adjustments and coaching so the system continues to fit the team. These activities need an explicit place in the engagement rather than being assumed from the fact that an application was built. [1]
Support runbooks connect the software to the people responsible for it. They describe what to do when a system is uncertain, who should be told and how work continues while an issue is resolved. Delivery and ongoing operation are separate commitments; the project pages identify the status they actually document.
The kinds of work we build
Our capabilities include custom AI applications, agent systems, workflow automation, document handling, knowledge retrieval, reporting, intake and dispatch. The choice follows the task. Document work may need help assembling material for review. An inbox workflow may need sorting and staged drafts. A reporting process may need information brought together so a person can decide what deserves attention.
Agent teams also need a division of responsibilities. A narrowly defined role and an explicit handoff are easier to evaluate than a single instruction to handle an entire business function. Our published operating notes explore these lessons: define the job, separate contexts, make the next handoff visible and pay attention when a service stops responding. [3]
The work in detail
Chronicle is a delivered research and case-organizing application for an in-house legal team. The brokerage engagement has operating intake, research, document-ingestion and prospect-refresh workflows, while the AME application develops around them. The B2B software engagement connects positioning, a website, publishing, search, outreach and post-trial nurture. Each starts with a different process and a different person responsible for the outcome.
Other pages cover fractional AI leadership across a holding group and smaller engagements in education, advisory, consulting and nonprofit work. A training program, registration handoff or publishing system can be a complete and useful job without becoming a larger platform. These accounts describe clients by type so the work can be explained without exposing the organization or the people involved.
Research, public code and prototypes
Some work is exploratory. Grant Match AI has a public codebase that readers can inspect, including its six-factor scoring approach. The nonprofit study explores qualitative themes in partnerships between nonprofits and businesses. Application demonstrations and prototypes, including Everstead and automotive concepts, give ideas a form that can be discussed and tested.
These categories help a reader choose the right comparison. A prototype can illuminate an interaction without establishing product adoption. Public code can show an implementation without proving a live service. Research can clarify a need without proving that a particular tool solves it. The individual pages explain what each example is for and how far the evidence reaches.
Robby and Common Ground Ventures
Robby owns and leads Zen Aegis. He also serves as CTO of Common Ground Ventures and its Technology and AI Architecture principal. His contribution connects system design and implementation with the group’s wider work across disciplines. The collaboration page explains those roles and credits Jesse’s contribution of the original wiki material and publishing approach.
For a prospective engagement, a useful first conversation starts with one process: who uses it, what information it depends on, where it gets stuck and what a better result would look like. That gives us a basis for discussing scope and the people needed to make it work. The pages in this wiki offer concrete examples to bring into that conversation.
Questions & answers
A few useful distinctions
What does Zen Aegis build?
Custom AI applications and workflows shaped around a business’s processes. The work includes agent systems, document handling, knowledge retrieval, reporting, intake and dispatch.
Who is the work for?
Businesses with a concrete workflow to improve and people responsible for using the result. The examples here span legal, brokerage, software, education and nonprofit work.
What happens before a build starts?
We work through the process, its constraints and the people involved. Align establishes priorities; Build creates a bounded solution; Operate addresses the work of keeping it useful.
Does an agent replace the person making the decision?
The work described here supports people through preparation, organization and drafting. Professional judgment and approval responsibilities still need an identified human owner.
Are all the examples live products?
No. Delivered work, operating workflows, developing applications, demonstrations and prototypes are labeled separately. A public repository is evidence of code, not proof of a deployed service.
How should a prospective engagement start?
Bring a process and the result you need: its inputs, handoffs, users and points of friction. That is a practical starting point for agreeing scope and responsibilities.
Public sources
These links provide the public material and background identified below. Client engagements are described by type.
- How we work
Background on the Zen Aegis method; not an independent audit of a private engagement.
- About Zen Aegis
The firm’s public account of its practice and leadership.
- Lessons from organizing agent work
Our published operating perspective on roles and handoffs.