Budget is approved, but the use case is still fuzzy
You need to decide where AI is worth funding before a larger program starts.
Use AI where the workflow, controls, and economics justify it.
We help C-level teams decide what to fund, how to structure the system, and what has to change for it to run in production.
Customers & Partners
Why executives trust us with high-stakes AI work.
GEICO
Former CTO
$1.2B technology remit / $150M annual savings
Citi
Head Architect
Architecture leadership across 3,000 engineers
LoanSnap
Former CTO
30 days to 24 hours / 5.6x productivity
FamilyLink.com
Former CTO
100M users / 40M monthly active users
50%
Audit Time Reduction
Financial services
5.6x
Productivity Gain
AI mortgage platform
$50M
Annual Cloud Savings
Enterprise optimization
$75M
Annual Savings
Healthcare org redesign
When teams usually bring us in
A pilot worked, but operations are unresolved
Human review, controls, ownership, and reporting are still undefined.
The data layer is blocking production AI
The workflow depends on brittle inputs, unclear ownership, or poor traceability.
Leadership alignment is slowing delivery
The mandate is real, but the business case and operating model are not yet shared.
From mandate to operating system — with controls and ownership built in.
Every engagement ends with a system that runs without us: clear controls, named owners, and a handoff your team can operate.
How an engagement works
1
Assess the mandate
Clarify what to fund, where AI fits, and what the business case requires.
2
Design the system
Define the workflow, data contracts, and controls before writing code.
3
Build with controls
Ship production-grade AI with confidence thresholds, human review, and decision tracing.
4
Hand off to operators
Name owners, write runbooks, and make sure the system runs without us.
Controls are designed in, not added later
Confidence floor0.92
Human reviewRequired on exceptions
Decision traceEnabled
Fallback pathRules + manual queue
Ownership lives around the system
Executive sponsor
ROI, operating constraints, and decision pace.
Domain lead
Policy logic, edge cases, and exception decisions.
Engineering owner
Accuracy, observability, releases, and system health.
Operations owner
Runbooks, training, and escalation handling.
How we think about funding, delivery, and operating change.
Six Critical AI Questions
Use this before funding an initiative that sounds promising but is still vague on value.
Read the InsightFour Pillars for Enterprise AI
A practical model for structuring agents, data, economics, and workforce change together.
Read the InsightProducer / Consumer Data Model
How to fix the data layer when AI is limited by brittle inputs and unclear ownership.
Read the InsightBring the workflow, the constraint, and the business case.
We will help you decide whether AI belongs in the answer and what it would take to make it work.