Prove one AI workflow before funding the larger build.
Map one costly or unreliable workflow, test its critical AI path with approved sample data, define the human controls, and leave with evidence and an implementation-ready plan.
Who this is for
The sprint works best when your team already has repeatable work happening across email, calls, notes, CRM, tickets, spreadsheets, or internal systems.
Service businesses
Dispatch, quote follow-up, field notes, customer updates, job summaries, and office handoffs.
Recruiting and professional services
Meeting notes, candidate or client summaries, CRM cleanup, follow-up drafts, and task creation.
Small SaaS and startup teams
AI features, support workflows, internal ops automation, local/private AI, and tool-connected prototypes.
Example workflows
These are the kinds of workflows that usually show value quickly because the process already exists and the bottleneck is human cleanup.
Meeting notes to tickets
- Summarize the call
- Extract decisions and next steps
- Create reviewed tickets or tasks
- Draft the follow-up email
Field request intake
- Summarize the customer request
- Flag missing details
- Suggest priority and routing
- Prepare a dispatcher-reviewed handoff
CRM cleanup
- Pull useful context from email and notes
- Draft clean account updates
- Identify stale follow-ups
- Keep humans approving record changes
Private AI workflow
- Review sensitive data boundaries
- Choose local, hosted, or hybrid model use
- Add approval gates and logs
- Document what the system can and cannot do
How the sprint works
The goal is not a long AI roadmap. The goal is one practical workflow your team can see, test, and decide whether to expand.
Pick the workflow
We choose one process with repeated inputs, clear review points, and an obvious owner.
Map the current path
We document triggers, tools, handoffs, failure points, and what a human must approve.
Test the critical flow
We exercise the important model and integration path with approved sample or sanitized data, add review steps, and capture repeatable evidence.
Review, harden, and hand off
You get the workflow map, limits, success checks, implementation backlog, and a clear go/no-go path to production or expansion.
Delivery evidence
The sprint uses the same bounded-delivery discipline applied to accepted government systems and consequential payment integrations.
Accepted AETC credentialing system
Delivered a blockchain-based credentialing system for the U.S. Air Force Air Education and Training Command under contract. The system was completed, accepted, and signed off.
Read the case studyRegulated healthcare payments delivery
Designed and shipped an in-app payments experience in approximately three months, including Clover terminal integration, tokenization, and ledger reconciliation.
Read the case studyBounded on purpose
The sprint is a tested decision package, not a promise to deploy an entire production platform in one week.
Included
One workflow, one owner, approved example inputs, measurable success criteria, review gates, proof-of-flow evidence, and the implementation handoff.
Separate scope
Production writes, regulated data, irreversible actions, broad platform builds, and additional workflows require explicit review and a separate implementation scope.
Start with one workflow.
If the first workflow saves time and stays under control, we can expand from there. If it does not, you avoided a bigger AI project that would not have paid off.
Start a conversation