Workflows,
reinvented.
Your enterprise becomes the Hybrid Agentic Enterprise. Scale AI agents by governing them, not operating them.
Most enterprises put a human behind every agent to supervise it. That is not scale. It is headcount with extra steps.
Ruptiv makes the implicit operating model explicit, so humans govern, agents execute, and AtlasView enforces. We price for the cost the operating model takes out.
The AI era arrived. The cost line did not move.
Eighty percent of enterprise AI spend produces nothing measurable. The tools work. The pilots run. The consultants present. The work underneath stays the same, and the people doing it are now buried under it.
That is not a technology problem. It is a delivery model problem. Nobody owns the outcome.
The goal is not fewer humans in the loop. It is fewer humans in the operating seat.
Most enterprises deploy agents into workflows built for humans. Then they assign humans to babysit those agents. That is paving the cow pasture. It does not scale.
Ruptiv moves people out of the operating seat and into the governing seat. Agents take over execution. People take over the calls only people can make.
The Hybrid Agentic Enterprise.
The enterprise people and agents share. Agents take the repeatable execution. People take the judgment, the approvals, the calls only people can make. The operating model is finally one model, not two.
AtlasView. Governance and execution layer for enterprise AI agents.
AtlasView holds the operating model in a form both kinds of actors can read. People read it and act on it. Agents read it and act on it. The same source of truth governs both.
Four stages. Priced for the cost the model takes out.
Diagnostic. POC. Disruption. License. We agree the baseline before any work starts. We invoice against documented savings on a pre-agreed cost line.
When humans move into the governing seat, six things change at once.
Operational throughput goes up.
Execution scales without scaling headcount linearly.
Coordination overhead comes down.
Workflow handoffs that used to live in heads now live in the platform.
Trust in AI execution improves.
Governance becomes visible because the system records every action.
Agentic adoption accelerates.
Governance teams stop blocking, because the controls they need are already in the rails.
Auditability is preserved.
Every agent action accrues to an audit trail you can defend.
Deployment becomes incremental.
Scope by workflow, not by big-bang rollout. The next workflow starts where the last one ended.
Pressure typically arrives in one of three ways.
The Board AI Mandate
The board wants measurable ROI within 12 months. The spend is committed. The results are not. The question is no longer whether to invest in AI. It is where, and how to prove it worked.
The Failed Initiative
A significant sunk cost. A pilot that produced a report and not a result. The organization has the scars. The patience is gone. The next move has to land.
The Cost Line Problem
Operating costs rising faster than revenue. People stretched across workflows that hybrid teams could carry. The board is asking why AI has not changed how the enterprise runs.
The pattern across every successful enterprise AI deployment is the same.
The workflow was redesigned first. Humans and agents were composed into one team from the start. The outcome was defined and measured before the build began.
$60M saved.
One AI agent took on the workload of 853 customer service tickets, freeing the team to focus on the cases that needed human judgment.
450+ use cases live.
Investment banking presentations that took junior analysts hours now take 30 seconds. The analysts now lead the client thinking.
$5M cut from counsel.
One AI agent drafts and reviews contracts, working alongside a smaller, sharper legal team. Outside counsel spend cut by more than $5 million.
These are not Ruptiv clients. They are proof of what the Hybrid Agentic Enterprise looks like in practice. The 5% of AI projects that produce real returns share one trait: the workflow changed and the operating model was redesigned around it. That is what Ruptiv builds.
Most of the agentic AI market is not what Ruptiv is.
McKinsey maps four positions in the agentic AI market. Three describe what Ruptiv is not. The fourth, End-to-End Workflow Disruptor, requires the most capability and produces the most enterprise value. Ruptiv holds it.
Consultants deliver strategy and exit.
Ruptiv installs AtlasView and stays until the cost line moves.
Vendors sell seats and leave you to figure out value.
Ruptiv embeds cross-functional squads inside the organization until the new way of working holds.
SIs connect tools to other tools.
Ruptiv rebuilds the workflow itself, with humans and agents on the same team.
What is missing in the market is the firm that takes accountability for the outcome end to end. The workflow, the agents, the people, the cost line. That is the End-to-End Workflow Disruptor position. That is what Ruptiv holds.
The Hybrid
Agentic Enterprise.
The enterprise people and agents share. Agents take the repeatable execution. People take the judgment. The operating model is finally one model, not two.
Enterprise AI stalls in workflows built for humans.
Agents can execute work. What they cannot do is understand the implicit knowledge people carry in their heads, which is where your actual operating model resides.
Who owns the next step. Which approval is required. When risk needs to escalate. Which system is authoritative. What "done" means here. Which exceptions are acceptable. Humans infer that from context and relationships and ten years on the job. Agents do not.
So the pilots that look great in a sandbox break at the first operational handoff. Governance teams refuse to sign off. And the response in most enterprises has been to put a human behind every agent to supervise it. That does not scale. It is headcount with extra steps.
The implicit becomes explicit.
Ruptiv writes down the operating model that already lives inside your enterprise, in a form software can enforce. Workflows. Roles. Approval chains. Lifecycle states. Escalation logic. Policy controls. The same rules that govern your people start governing your agents.
The agents now get the same direction your humans rely on, in a form they can execute against.
Humans govern. Agents execute.
When humans move out of the operating seat and into the governing seat, the work redistributes. Agents take over the repeatable execution. People take over the calls only people can make.
Govern.
Priorities and intent. Approvals and risk acceptance. Exceptions and ambiguous calls. Final accountability sits with people.
Execute.
Repeatable operational tasks. Information gathering. Provisioning and coordination across the workflow. Agents do the human's bidding, not their own.
AI bolted onto the org chart speeds up a flawed process.
Most enterprise AI fails for one reason. Agents are dropped into workflows built for humans. New bottlenecks open inside the same broken thing. The tools are not the constraint. The operating model is.
If the answer to scaling anything is more headcount, the equation is wrong. Leadership capacity does not scale linearly. Coordination cost does. Pretty soon the firm is paying more to coordinate than to produce.
Humans govern. Agents execute. AtlasView enforces.
Three roles, cleanly separated. This is the picture every operator should remember.
AtlasView holds the rails between the three. The same source of truth governs both kinds of actors. That is what makes the platform human-native, not just agent-native.
Three surfaces. One control plane.
AtlasView gives operators three levers to govern an agent workforce. Agents are the population. Roles are the scope. Workflows are how they run end-to-end. Every operational decision on the platform maps to one of the three.
Stand up and tune the fleet.
Spin up new agents. Configure and tune behavior. Monitor health and hygiene as the population grows.
Bind what an agent may do.
Define operating boundaries. Equip with skills and tools. Deploy an agent into a role. Govern what sub-agents it may spawn.
Run agents end to end.
Design and configure workflows. Orchestrate agents across steps. Monitor workflows in flight as they execute against your real systems.
AtlasView maps your enterprise into the Responsive Framework as it runs.
The Responsive Framework is the diagnostic foundation Ruptiv operates from. Operating models do six things: three activities, three seams between them, and one central norm. The framework gives every operating model a common shape, so the gaps between work-as-named and work-as-substrate become visible.
Once your enterprise is mapped into the frame, the model can be queried. Where is the cost? Where is the delay? Where do decisions stall? The same questions a senior operator asks every Monday morning now have an answer the platform can produce.
Visibility comes with the rails.
Cycle times come down. Coordination overhead comes down. Adoption goes up because governance no longer blocks it. Human teams stop supervising agents and move to higher-value judgment work. Execution scales without scaling headcount linearly.
Four stages.
One principle.
Ruptiv prices for the cost the operating model takes out.
Most AI vendors either charge by the hour or ask the buyer to trust that the software will pay for itself. Ruptiv does neither. The baseline is agreed before any work starts. The cost line is the measurement.
One workflow becomes a function. A function becomes the firm.
Built on the
numbers.
Ruptiv is the End-to-End Workflow Disruptor. We make the implicit operating model explicit so humans govern, agents execute, and AtlasView enforces. We price for the cost the model takes out.
Five seasoned Principals.
Ruptiv is built by five Principals with decades of operating experience inside the firms most enterprises are trying to become.
Tim Mooney is Co-Founder and CEO of Ruptiv. He works with senior leaders to translate complex, evolving challenges into clear direction, aligned operating models, and executable paths forward.
With more than fifteen years of leadership experience across technology transformations with budgets exceeding $100M, Tim moved into senior leadership roles at Starbucks and Zappos. He further refined his transformation practice as an Executive Partner at Gartner, where he advised CIOs and CTOs across global enterprises in retail, financial services, healthcare, and manufacturing. Through those years, he developed deep expertise in operating model design, large-scale program delivery, and the leadership and group dynamics that determine whether transformation lands.
Tim is known for asking the questions that stretch leadership thinking beyond familiar constraints, reframing what leaders believe is fixed until the impossible starts to look probable, while keeping ideas grounded in what is achievable. At Ruptiv, he brings that same orientation to how enterprises restructure work and accountability as AI moves into the operating layer. He is a Certified Integral Facilitator. Tim holds an MBA in Organizational Development from Western Washington University and a BA in History from Gonzaga University.
Ian McKelvie is Co-Founder and President of Ruptiv. As founder of BECAUZ, he spent more than two decades working with senior leaders at Microsoft, eBay, Expedia Group, Providence, Alphabet, Avanade and Zoom on the architecture beneath enterprise performance. His work centered on making leadership systems visible: how direction is set, how decisions are made, how alignment is created under pressure, and how strategy translates into coordinated execution at scale.
That same systems lens shapes his work at Ruptiv, where the question is no longer how leadership operates but how the enterprise operates when AI is part of the workforce. The focus is the operating layer: where authority sits, how work gets resourced, who owns outcomes, and what shows up on the cost line.
His perspective was shaped early. By the age of 13, he had lived in Singapore, England, South Africa, and Canada, learning to read complex environments quickly. Before founding BECAUZ, he led teams at IBM, Xerox, and Chase. He also built three startups: Pacifica Group, Gift Certificates, and eCharge. Ian holds a BBA from Simon Fraser University and graduate degrees in business, process work psychology and neuroscience.
Matt Wells is Co-Founder and Operations Principal at Ruptiv. He is a multi-time founder, operator, and technical strategist with deep experience building the operating systems behind scalable companies.
Through his own consulting practice, the prototype for the venture he now helps lead, he has partnered with leadership teams across product, engineering, GTM, hiring, and AI-enabled workflows to turn strategic ambiguity into executable systems. The work spans operating cadence, internal tooling, recruiting workflows, knowledge systems, and cross-functional execution.
Before Ruptiv, he co-founded Shujinko, a venture-backed cloud compliance automation platform spanning AWS, Azure, and GCP. He led cloud engineering at Starbucks, where his team took the company to the public cloud for the first time with a major production application. Earlier roles include infrastructure and security leadership at CARDFREE, where he helped launch mobile commerce platforms for Taco Bell, Dunkin', and Peet's Coffee. He has also served as an Entrepreneur in Residence at Pioneer Square Labs. Matt holds a degree in Business Administration and Management Information Systems from Western Washington University.
Geoff Lyle is Co-Founder and Product Principal at Ruptiv. He has spent his career at the seam between retail operations and enterprise technology, designing the systems that move data from the store floor to the cloud and back.
He co-founded Sysrepublic, the retail analytics platform that scaled from three founders to a global organization serving Walmart, Kroger, Tesco, Best Buy, and more than seventy other Fortune 500 retailers. The platform consolidated what became the largest private repository of retail point-of-sale data in the world. After Sysrepublic's acquisition he led product and delivery as a Vice President at Appriss Retail, scaling to $175M ARR with ninety percent customer retention across teams on three continents.
More recently he has advised Fortune 100 retailers on ERP modernization, data governance, and transformation governance as an Executive Architect at Deloitte. He is also founder of GrowDirect, building Canary, an agent-driven retail intelligence platform. Earlier in his career he held strategy and delivery roles at PwC and IBM. Geoff holds a BS in Management Information Systems from USC.
Jeff Roberts is Co-Founder and Technology Principal at Ruptiv. He has architected and built the platforms behind mobile commerce, cloud compliance, and security audit at enterprise scale, with hands-on engineering leadership across mobile, healthcare, telecommunications, financial services, and SaaS.
As a Software Architect at CARDFREE, he designed the scalable, reliable platform behind their mobile commerce solution. As Senior Principal Software Engineer and Director of Technology at Shujinko, he architected a cloud compliance application that automated evidence collection across AWS, Azure, and GCP, drove the engineering team's technical roadmap, and helped build a culture with a high bar for quality, ownership, and bias for action. He served as Principal Engineer at Starbucks and most recently as Principal Software Engineer at A-LIGN, the security and compliance partner whose platform is trusted by more than three thousand global organizations.
Earlier in his career, Jeff was a Chief Technology Officer at Network Earth, SNReach, Inmiind Visionary Technologies, and Xpediate Consulting, and held senior engineering roles at Wells Fargo and AT&T. He holds a BS in Computer Science from Shippensburg University.
Positioning requires knowing what to exclude.
Not a consulting firm.
Consulting firms deliver strategy documents and exit. Ruptiv installs AtlasView and stays until the cost line moves.
Not a SaaS vendor.
SaaS vendors sell platform subscriptions and leave the client to figure out the outcome. Ruptiv embeds cross-functional squads inside the organization.
Not a systems integrator.
Systems integrators connect existing tools to each other. Ruptiv rebuilds the workflow itself, with humans and agents on the same team.
Not an agentic AI enabler.
Enablers provide foundational infrastructure for others to build on. Ruptiv builds the outcome, measures it, and invoices accordingly.
Validated by the market. Built for the next one.
McKinsey identifies End-to-End Workflow Disruption as the most defensible position in the agentic AI market. Of the four positions mapped, it requires the most capability and produces the most enterprise value. Ruptiv holds it.