The execution layer for human and AI work.
Floxar turns the processes trapped in documents and people's heads into flows that people and AI agents execute step by step, with a trail of every run so organizations can see what happened, improve from evidence, and automate what is ready.
BetaWith selected customers · full launch planned for late November 2026
AI that makes people more capable.
AI is becoming part of how people work and live. It does not replace people; it extends what each person can do. A support agent handles more complex cases with confidence. An operations lead runs a process that used to need a team. A new hire is productive in days. Floxar is built for that future: people and AI agents working side by side, each doing what it does best.
Getting there takes something most organizations do not have yet: clarity. An AI agent is only as reliable as its understanding of the work. Told to “handle the refund,” it guesses. Given the procedure one step at a time, with the paths the business allows, it executes. People need the same clarity to train agents, trust them, and oversee what they do.
Floxar brings the two together. It gives people and agents one shared definition of how the work is done, which a person can read and an agent can execute. It also keeps a record of every run, so people stay in charge as AI takes on more.
AI makes people more capable when it knows exactly how the work is done.
AI agents can’t learn the way people do.
For most of history, organizations have passed on know-how in the same way. They write it down, train people on it, and let experience fill the gaps. A new hire shadows a colleague, picks up the exceptions, asks when unsure and adapts when something changes. The method has always been slow and leaky, but it works because people keep learning on the job.
That model is breaking down. Processes now change faster than training can keep up, and they grow more complex than anyone can hold in their head. Procedures end up in wikis nobody opens and in the memories of the people who have done the work longest. Results vary from person to person, and leaders see outcomes without ever seeing how the work was done.
AI agents don’t fit that model at all. An agent does not shadow anyone, it does not absorb context over months, and it does not notice that a policy changed last Tuesday. Retraining a model every time a process changes is impractical. Handing it the whole knowledge base makes things worse, because a model’s reasoning degrades as its context widens. Oversight built for people, such as sampled QA reviews and spot checks, does not carry over to work done by machines at machine speed.
Agents need knowledge in a form they can execute: the current procedure, one step at a time, with the decisions the business allows at each point. Floxar is the platform that provides it. It runs AI workflows so that agents execute predictably, and people can govern, observe and improve every run.
What Floxar gives AI agents, and the people responsible for them.
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Bounded execution
At each decision point, an agent chooses among the paths the business authored. A step that is not in the flow cannot be reached by navigating it.
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Always the current process
Change a flow in plain language and the next run follows it, with no retraining, no prompt rewrite and no deployment.
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Only the context each step needs
Each step gives the agent precisely scoped instructions instead of a whole knowledge base, which keeps its reasoning sharp and its costs down.
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Visibility into every run
Every step is recorded and attributed to the agent or person who performed it, so what happened is on the record rather than inferred.
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Governance built in
Agents are members of an account with their own credentials and roles, working under the same permissions, approval gates and audit trails as people.
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Improvement from evidence
Trails show where agents and people hesitate, fail or diverge. Fix the flow once and every executor, human or AI, benefits on the next run.
People and agents work from the same flows and can hand work to each other mid-run, so automation can grow one proven step at a time.
Five principles behind the product.
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01
Structure comes before automation.
Automation fails when it is built on how work is assumed to happen. It has to rest on how the work is actually done, so the work gets structured first.
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02
Every run belongs on the record.
You cannot control what you cannot observe. Every run, whether a person or an AI agent performs it, should leave an attributable trail as a byproduct of the work rather than as a separate reporting chore.
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03
Business logic belongs in content, not code.
The people who own a process should be able to change it in plain language. The change should take effect on the next run for people and agents alike, with no deployment.
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04
Automation is earned by evidence.
A step goes to an AI agent once real runs show that it is stable and low-risk, not because it looks automatable on a whiteboard.
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05
The process is the asset, not the model.
People move on and models improve. An organization's process library and trail history should outlast them all and stay its own, independent of any single AI vendor.
What we value and how we act.
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Say what is true.
We describe Floxar as it is today, including the fact that it is in beta, and we do not claim capabilities before they ship.
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Keep people in charge.
AI can draft, execute and suggest, but people decide what a process is and review what agents author. Consequential work gets approval gates, not blind trust.
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Build for consequential work.
Permissions, audit trails, approval gates and versioning are part of the foundation, not an enterprise add-on.
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Do the simple thing that works.
We prefer small, clear steps to clever complexity, both in our flows and in our own engineering.
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Use what we build.
Floxar's own AI authoring assistant runs on operating procedures written as Floxar flows, so we run into the product's strengths and gaps before our customers do.
Early, and honest about it.
- July 2026Floxar, Inc. incorporated
- 2026Beta with a small group of customers running real, consequential processes
- September 2026Public documentation launched at docs.floxar.com
- Late November 2026Full launch (planned)
Until launch, accounts are provisioned for selected beta customers only, and new registrations go on a waitlist. People invited by those customers sign in as usual.
Who is behind Floxar.
- Legal name
- Floxar, Inc.
- Entity
- Delaware C corporation
- Incorporated
- July 2026
- Based in
- San Antonio, Texas
- Leadership
- Salvador Lancaster Jones, Founder and CEO
- IP
- Patent pending
How we handle your work
- Every account is scoped and isolated from every other, and nothing inside the application is publicly crawlable.
- Hierarchical role-based access control, audit trails, approval gates and versioning are built in.
- Data is encrypted in transit and at rest, on AWS infrastructure in the United States.
- The AI model stays your choice: people can connect their own AI client over the Model Context Protocol.
Run a consequential process on Floxar.
We are onboarding a small number of teams ahead of launch. Tell us about the process you want to structure, and we will get back to you.
- Legal: legal@floxar.com · Privacy: privacy@floxar.com