We build AI agents that run your back-office workflows.
So you take on more work with the people you have. The agents know how your company works, and a person signs off where it matters.
The type of agents we build, and the workflows they can run.
Examples of what we can build, from finance to IT: in each one an agent does the steps and a person signs off. With your company memory, the agent also knows the rules your team never wrote down.
Invoice to approval
Invoice arrives
Agent reads it
- Supplier and amounts read
- Purchase order found
Checks your ERP
- Matched with the goods receipt
- Booking drafted
A person signs off
Booked, on record
With company memory
Knows which suppliers your controller always checks twice, and why, from the threads where it was decided.
Contract to first day
Signed contract arrives
Agent prepares the checklist
- Accounts and access prepared
- Welcome email drafted
Updates your HR system
- Employee record created
- First-day plan scheduled
The manager signs off
Onboarded, on record
With company memory
Knows the onboarding steps that never made it into the checklist.
Request to scheduled job
Request arrives
Agent classifies it
- Request read and classified
- Skills and parts matched
Checks the planning
- Free time slots found
- Planning proposal drafted
The planner signs off
Scheduled, on record
With company memory
Knows which jobs your planner never books on a Friday afternoon.
Question to answer
Customer question arrives
Agent finds the case
- Question understood
- Earlier case found
Checks your CRM
- Order and policy checked
- Answer drafted
A colleague signs off
Answered, on record
With company memory
Knows the exceptions your team grants, and who approved each rule.
Request to purchase order
Purchase request arrives
Agent checks the budget
- Request and budget checked
- Specification completed
Matches your suppliers
- Approved suppliers compared
- Purchase order drafted
The buyer signs off
Ordered, on record
With company memory
Knows which suppliers are preferred in practice, not only on the list.
Access request to account
Access request arrives
Agent checks the role
- Role and need checked
- Access policy applied
Checks your directory
- Current rights looked up
- Access change prepared
The owner signs off
Access granted, on record
With company memory
Knows which access requests always need the owner's call.
Where can agents take over?
AI Sprint
- Observe + ask
- Systems + data
- Procedures + docs
Process mapping together What we build
- Rules → Code
- Judgment → Agent
- High stakes → Person
Build + test
Design with the people who run it
- Real business examples
- Quality · time · effort
Blueprint, you decide What we build
- Fail → Fix + retest
- Pass → Deploy
Deploy with the team
Work through real cases together
- Review + correct
Operate, maintain, improve What we build
- In the tools people already use
- Background agents
- Your team's AI
Run + improve
Run log
When something fails, we fix the
- Knowledge
- Instructions
- Workflow
Baseline → live results
- Cycle time
- Error rate
- Cost per run
Then
- Rerun the tests
With company memory, a fix to the knowledge becomes an approved skill, so every agent gets it.
- Your people
- Praxeum
- The agent
Bring the workflow that runs on handoffs today.
We map it with the people who run it, and in a week you see it as a working example on your own data.
Start with one workflow in a weekHow we work
Two ways to work with us.
We build AI agents for your workflows. Or we build them on your company memory, so they know how your team works and keep knowing it after we hand over.
Already running your own AI? Give it your company memory.
One week, on your own data and systems
A working example of one workflow on your own data and systems, so you see what a build would deliver, and a plan for what to build next.
AI automation and agents in your workflows
One workflow, one department, or the whole back office. On the systems you already run, with the people who do the work today, guards and sign-off built in.
Time back, spent better
Your team stops doing the repetitive work by hand and stops hunting for information across systems and inboxes. Those hours come back, and they go to the work that needs a person: the customer, the exception, the decision. The same people get more done, and the work gets better.
Yours, documented and handed over
We write down how it works, train the people who will run it, and hand it over properly. A person still signs off where it matters and every run stays on record. When your team runs it without us, we are done, and that is how it should be.
One week, one workflow, and what your work already knows
The same working example on your own data. We also connect Praxeum read-only to where the work happens and show you the skills it found for that workflow, and where the written process and the real one differ.
Agents that start from approved skills
Your people approve how the work is done, once. Your agents run on your skills, through Claude, ChatGPT, Copilot or Gemini, over MCP. Everyone works the same, approved way.
The next workflow starts further ahead
The skills from the first workflow are there for the second. Every run is on record, and every skill names the person who approved it.
Current after we hand over
When the way your team works changes, Praxeum flags the skill for the person who owns it. Your team runs the agents, and the company memory stays up to date.
Private beta. We connect to where your work lives: Microsoft 365 or Google Workspace, Teams or Slack. Clients on this route are beta partners: early access, our hands on your first workflows, and a say in the roadmap.
How company memory worksCompany memory
Your company already knows how it works. Your AI does not, yet.
Today's AI agents are brilliant at reasoning, but every conversation starts from zero. Praxeum turns how your company really works into memory your agents can act on. We call it a company brain that acts.
1 · Bind Many fragments, one skill Chats, docs and meetings become one coherent way of working. Brain: five senses, one memory
2 · Consolidate Learned once, run every time Human-approved and tested, then your agents run it without starting over. Brain: short-term to long-term
3 · Map Knows where everything is Which system, which owner and which approval each process needs. Brain: the cognitive map
Open standards (Agent Skills and MCP), so the skills are yours and work with any AI.
Your company uses AI, but the daily work has not changed yet.
Not adopted
Whether you have piloted AI or not started, the daily work runs the same way.
Still manual
Core work still runs on manual handoffs, spreadsheets, and inboxes.
Cost grows with revenue
Every new client or process means more headcount, so cost grows as fast as revenue.
No accountability
Nobody can show who approved what, what it cost, or what the agent actually did.
Everyone their own AI
Individual employees run their own AI setups, so there are as many versions of how the work gets done as there are licences.
No company memory
Your company memory is still not available to your AI agents. Your best people know how it is done, but it lives in their heads.
AI solved the cortex. Your company still needs a hippocampus.
The reason your agents improvise isn't a weak model. It's a missing kind of memory, and it's the same split neuroscience has understood for decades.
What we learn building this.
10 Sept 2026
What Is an AI Agent? The Question I Get on Every Sales Call
The explanation I give prospects on a call: what an agent is, how the reasoning loop works, and a walk through Praxeum's own outbound engine, human approval included.
28 Aug 2026
They Fired People for AI. Now They're Hiring Them Back.
Two in three employers that cut roles for AI are already rehiring. The data behind the reversal, and what the companies that kept their people did differently.
25 Jul 2026
Gates, not guidelines: building a product with AI agents that cannot cut corners
The pitch is that you wire up one agent and go ten times faster. That is not what happens. Here is the setup, tool by tool, that actually let AI agents write every line of my product’s code without quietly breaking a database full of customer data.
4 Aug 2026
How I Trust AI Agents to Ship Enterprise-Grade Code Without Reviewing Every Line
The setup that earned that trust: nine phases, guardrail hooks, three AI reviewers, a seven-check gate wall, and the one button an agent can never press.
The people you talk to are the people who build it.
Operator experience, engineering leadership and hands-on implementation in the same room, from the team that also builds the platform underneath. What you get is a working system your team can understand, operate, and improve.
Choose how to get started
The AI Sprint.
One week, fixed scope, one workflow. You leave with a working example of that workflow on your own data, and a plan for what to build next.
Book a call.
Bring a process that is slow, manual, or fragmented. We will tell you which steps an agent can take over, and what it takes to build it on what you already run.