2 weeks
AI Workflow Value and Readiness Sprint
Establish the business case, required data, risks, and a recommendation to proceed, reshape, or stop. Fixed fee agreed before kickoff.
See the Sprint and sample outputPrincipal-led AI engineering
Know whether your AI workflow is worth building.
In two weeks, establish the business case, data requirements, and operating risks, with a clear recommendation to proceed, reshape, or stop.
Work directly with Jovani Pink, from the first question through delivery.
A concrete example
Should AI help review your exception queue?
A reviewer moves between a case record, supporting documents, and a policy. The question: can AI prepare a useful recommendation while the reviewer keeps the decision?
- 1. Gather the record
- 2. Check the evidence
- 3. Human decision
- Recommendation: reshape
- Start with evidence preparation; retain human approval for every case.
- Before a pilot
- Measure review time and corrections. Confirm document access and who owns escalations.
- What would justify proceeding?
- Agree a useful time saving and acceptable correction rate, then test them on representative cases.
Illustrative reasoning, not a customer result or a completed assessment.
Use the decision worksheetTwo weeks to a clearer decision.
6-10 weeks
Governed AI Production Pilot
When the Sprint supports it, implement one bounded workflow in your approved environment. Separate scope, fee, and acceptance criteria.
Understand the conditional PilotSelected work
See the decisions behind the systems.
Dataform + BigQuery governance
Analytics delivery becomes risky when contracts, validation, promotion lanes, rollback behavior, and cost controls are implicit or split across tools.
- Defined contracts and validation gates around analytics changes
- Separated development, approval, promotion, and rollback responsibilities
- Made cost checks and release evidence part of the delivery path
Sanitized architecture pattern; not a deployed customer system.
Compliant GCP platform
Data, analytics, and ML platforms become harder to trust when access, lineage, transformation, and model-facing data are governed as separate concerns.
- Unified compliance, analytics delivery, and ML feature access
- Made ownership and promotion boundaries explicit
- Documented an inspectable target architecture and release sequence
Sanitized architecture pattern; not a deployed customer system.
Workflow state-machine infrastructure
Durable AI and automation workflows need explicit state and actor boundaries when retries, time, human decisions, and partial failure matter.
state = lights.initial_state
assert state.value == "green"
state = lights.transition(state, "TIMER")
assert state.value == "yellow"An excerpt of the documented traffic-light example: an explicit event changes the state. This is source evidence, not production adoption.
Read the complete exampleBeyond client work
Products we build and keep improving.
From reviewing CRM evidence with Pipeline Basis to planning meals with PrepPlate, our seven owned products put our research and engineering into everyday experiences.
Explore our productsYour principal
Jovani Pink
Jovani Pink leads discovery, architecture, implementation decisions, and handoff. The studio is intentionally sized for one active implementation engagement at a time, with specialists added only when the scope and client agreement call for them.
About Jovani and the practiceBefore we talk
Do we need an AI prototype already?
No. Bring one workflow and a question worth resolving. The Sprint can establish whether building is justified.
What does the Sprint cost?
A fixed fee is confirmed in the written proposal before kickoff. It depends on the workflow, participants, data access, and review needs.
What happens after two weeks?
You receive a recommendation to proceed, reshape, or stop. A production pilot is a separate decision and agreement.
Tell us about your workflow.
Jovani reviews each inquiry and replies about fit and a useful next step. Name, email, company, and your workflow problem are required.
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