The AI Sprint: one workflow, one week, on your own data.
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.
What you leave with
- Working example of one workflow
- Stack read
- Risks and value
- Roadmap
Plus, on the company-memory route
- The skills Praxeum found for that workflow
- Where your documentation and your practice differ
We connect to where the work lives. A source we do not read yet is connected during the sprint.
If agents are the wrong answer for your workflow, the sprint will say so. That is the point.
What happens after the sprint
- We build
- You decide whether to continue
- You own it
Agent step
Map the current workflow end to end with the people who run it today: tools, handoffs, bottlenecks, costs, data flows.
- Current-state process map
- Bottleneck and cost analysis
Human decision point
Design the target agentic workflow, co-designed with the people who will run it: data flows, value metrics, architecture, delivery plan, and the oversight model, which says where a person signs off inside the loop, where they oversee from above it, and who handles the exceptions. Heavier checks where the impact is higher.
- Target workflow design
- Human-in-the-loop model
- System architecture
- Cost and ROI model
- Delivery plan
Agent step
Build the agents, automations, integrations, and interfaces: production-grade, monitored, tested, and logged. Every action traceable: what the agent did, who approved it, when.
- Working agentic workflow
- Monitoring
- Test coverage
- Audit trail
Agent step
Embed the system into your existing stack and rhythm of work, so it runs inside the business rather than beside it.
- Live integrations
- Operational runbooks
Agent step
Finish the enablement that started in step 1. The team that will run the system has been in the room since the map; now the system is written down for them: architecture, decisions, runbooks, what it costs, and how to change it.
- Team enablement
- Technical documentation
- Cost visibility
Your team owns it
Transfer ownership. Your team operates, maintains, and improves the system, with value tracking to prove it keeps earning its place.
- Ownership transfer
- Value tracking
- Improvement roadmap
Gates, not guidelines.
Governance and security are not a chapter in the documentation. They are properties the system enforces: designed in the Blueprint, made mechanical in the Build, and handed over with everything else. They are also what lets the system grow: how much agent work you can run is capped by how much oversight you can give it, and gates with an audit trail let a small team oversee far more runs than they could check by hand.
Human sign-off
A person approves the decisions that carry risk, by construction.
Audit trail
Every action traceable: what the agent did, who approved it, when.
Security guardrails
Least privilege and hard boundaries on what agents can never touch.
Technical documentation
The system is written down: architecture, decisions, runbooks.
Cost view
You see what each workflow costs to run, from week one.
Transparency
People know when they are interacting with AI.
How we take the risk out.
Start with one week
The AI Sprint is fixed scope, one workflow, before any build commitment.
Two gates you control
After the sprint and after the blueprint, you decide whether to continue. Twice, before a full build.
The same shape every time
Every system assembles the Handover Architecture, so the design effort goes into where your judgment line sits, not into inventing the plumbing.
An honest read either way
If agents are the wrong answer for the workflow, the sprint says so, and you have saved yourself a build.
Ready to start with one workflow?
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.
You speak with the people who would build the system. No sales pressure, and an honest read either way.