Our Approach
Builders, not theorists.
We engage differently because we come from the engineering side of AI, not the advisory side. That distinction matters in every part of the work.
Philosophy
The difference between knowing and doing is everything.
Most AI advisory firms are staffed by people who have studied AI—its capabilities, its risks, its business applications. That knowledge has value, but it has a ceiling. When the question is "will this actually work in our environment," theoretical knowledge runs out.
Our leadership team has built and deployed AI systems in production: systems that had to perform under load, handle messy real-world data, survive organizational change, and deliver measurable output. That experience is the foundation of everything we do.
01
We have shipped production AI systems.
Our advice is grounded in direct experience building and deploying AI in production environments—not in vendor documentation or analyst reports. We know what actually breaks, what takes longer than estimated, and where the real complexity lives. That context shapes every recommendation we make.
02
Business outcomes, not technology adoption.
AI is a means to an end. We measure our work by business results—revenue protected, costs reduced, time recovered, risks avoided. We will tell you when the right answer is not to build an AI system. That objectivity is the core of the engagement.
03
We transfer capability, not create dependency.
Every engagement is designed with an exit in mind. We document what we build, train the people who will own it, and structure our work so that your organization is stronger—not more reliant on external advisors—when we leave.
04
No conflicts. No commissions. No agenda.
We take no referral fees, vendor commissions, or affiliate arrangements. Our only financial relationship is with you. That means our analysis of vendors, tools, and strategies reflects technical and business reality, not our own incentive structure.
Engagement Model
How an engagement works.
Every engagement follows a structured progression. Each phase has clear deliverables and decision points. Nothing moves forward without your sign-off.
Phase 01
Audit
Duration
2–3 weeks
Understand the current state.
We map your existing AI investments, vendor relationships, data infrastructure, and internal capabilities. We identify what is working, what is not, and where the real gaps are. This is not a questionnaire—it is a structured set of conversations with your leadership, technical teams, and operators.
Phase 02
Strategy
Duration
2–4 weeks
Define what success looks like.
Based on the audit, we develop a prioritized strategy: which capabilities to build, buy, or retire; where AI creates defensible advantage; and what the roadmap looks like with honest timelines and resource requirements. The output is a document your CFO and CTO can both read and debate.
Phase 03
Pilot
Duration
4–8 weeks
Build something real.
We identify the highest-value, lowest-risk pilot—the one most likely to demonstrate measurable ROI and build internal confidence. We design it for production from day one, with defined success criteria, a measurement framework, and a plan for what happens after the pilot ends.
Phase 04
Scale
Duration
Ongoing
Operationalize what works.
Pilots that succeed get operationalized. We manage the transition from experimental to production, including change management, team training, vendor contract renegotiation if needed, and the governance frameworks that keep the system accountable over time.
Start the conversation
The audit starts with a single conversation.
Thirty minutes. No slides. We want to understand your situation before we say anything about how we can help.