Leadership
Senior technology and operating leaders with experience across the US, APAC, and Europe. Kellogg MBAs with experience leading global engineering teams, carrying P&Ls, and engaging with boards and executive teams.
We are an AI engineering firm. We find where AI will create the most value, prove it, build it, measure it, and scale what works. We call this AI Value Engineering.
Human judgment. Machine intelligence. Business value.
You are the captain. We are the copilot.
What we do
Find the value. Prove it. Build it. Measure it. Scale it.
Most companies don’t have an AI problem. They have a prioritization problem.
There are hundreds of ways to apply AI. The challenge is knowing where it will matter most, what it’s worth, whether it can actually work, and how fast you can realize the value.
We don’t start with a technology. We start with the economics of the business. Every engagement begins with one question: what business metric are we trying to move? Revenue. Cost. Cycle time. Capacity. Quality. Risk. Customer experience.
Then we engineer backwards from the outcome.
Map the workflows where AI could materially improve revenue, cost, speed, quality, risk, or capacity.
→ AI opportunity map
Establish the baseline. Quantify the upside. Calculate the economics.
→ Business case
Rank opportunities by value, feasibility, time-to-value, risk, and readiness.
→ AI value roadmap
Test the highest-value opportunity against your real data and real workflows.
→ Working proof
Engineer the system, integrations, controls, and operating model required for production.
→ Production system
Track the business outcome, not just model performance.
→ Measured value
Turn what works into repeatable enterprise capability.
→ AI capability, not another one-off
AI agents and agentic workflows · AI-powered applications · decision-support systems · intelligent automation · process orchestration · knowledge and reasoning systems · data and AI infrastructure · enterprise integrations · evaluation and testing · monitoring and observability · human-in-the-loop workflows · security, governance, and controls.
The model is only one component. We engineer everything around it that makes the system useful, trustworthy, and economically viable.
AI opportunities rarely sit inside an AI department. They live in the workflows that run the company.
Quote-to-cash, sales operations, pricing, proposals, forecasting.
Supply chain, procurement, planning, quality, field operations, service delivery.
Hire-to-retire, recruiting, onboarding, employee support, knowledge management.
Service, success, personalization, retention, experience.
Order-to-cash, financial operations, forecasting, compliance, risk, fraud.
How we work
The hard part isn’t the model. It’s building the system around it. This is steps 04 to 06 in detail: one workflow, taken all the way.
At week six, before production spend. If the numbers don’t clear the bar we agreed at the start, we say so and stop. Our job isn’t to make every AI project succeed. It’s to make sure you invest in the ones that should.
We sit with the people doing the work, understand the systems behind it, and establish the baseline we will be measured against. If the foundation is not ready, we fix the necessary data paths, deployment paths, instrumentation, tests, and integration points before building anything on top.
Before anything gets authority to act, we test it against your own case history and live workflows. We measure how it performs before it touches production decisions.
It goes live with approval thresholds, permissions, audit trails, monitoring, fallback behavior, and rollback. We give it more room only when the numbers hold up.
Your engineers build alongside ours from the beginning. The tests, runbooks, architecture, documentation, and operating knowledge stay with you.
Measure first. If we can’t establish a baseline, we won’t pretend to scope an outcome.
Tests ship with the system. Evaluations run again whenever the system changes.
Security starts on day one. Permissions, auditability, governance, and rollback are architecture, not retrofit.
Model- and cloud-neutral. We don’t resell licenses, so we have no reason to over-specify a particular platform or model.
Shared risk. Where it makes sense, a meaningful portion of our fee rides on the metric we agree at the start.
Your work stays yours. Your data, settings, business logic, documentation, and operational knowledge remain yours.
You don’t need a three-year transformation program. Start where the economics are most compelling. Find it. Fix it. Measure it. Then find the next one.
Where to start
Each one has a defined exit, written into the agreement.
Find out where AI is actually worth pursuing. We identify and prioritize opportunities, establish baselines, test feasibility, and build the business case.
Two weeks, fixed fee. Written go / no-go. No obligation to continue.Turn scattered experimentation into an economic roadmap. We map opportunities across the business, quantify them, and sequence them by value and feasibility.
For leadership teams setting enterprise AI investment strategy.Take a high-value opportunity from idea to production. A focused team works alongside yours to build, integrate, test, deploy, and measure one or two priority workflows.
Twelve weeks. Billed monthly. Stop-gate at week six. Part of the fee rides on the metric we agree up front. Thirty-day exit with full handover.Keep it performing after go-live. Quality, cost, drift, reliability, adoption, and business outcome, monitored and continuously improved.
Retained. Ninety-day exit, documented handover.We score candidates on three tests. Two out of three isn’t enough.
If we can’t measure it today, we can’t prove it tomorrow.
That’s where the money leaks.
Someone who can make the call.
Team
Our core team includes technology leaders, engineers, and operators who have built and run enterprise systems at Fortune 500 companies including Cisco and UnitedHealthcare, and at startups.
Senior technology and operating leaders with experience across the US, APAC, and Europe. Kellogg MBAs with experience leading global engineering teams, carrying P&Ls, and engaging with boards and executive teams.
AI, data platforms, cloud, distributed systems, networking, enterprise software, process automation, IoT, and wearables.
Builders. Buyers. Advisors. A 360-degree view of how technology is evaluated, funded, built, deployed, operated, and adopted inside enterprises.
Inference, model usage, infrastructure, observability, and architecture choices cost money every day a system runs. We understand what systems cost to build, and what they cost to operate.
We staff to the problem and the outcome. If a capability isn’t needed for your workflow, it isn’t on the invoice.
A pilot doesn’t own the aircraft. A pilot helps you navigate it. You know where the company needs to go. We help work out where AI can take you, and make sure the aircraft is capable of getting there.
An hour on a call. We’ll look for a measurable baseline, expensive exceptions, and a clear owner, then tell you what we’d prioritize.
If we don’t think AI will create enough value, we’ll tell you that too.
Book an hour or write to us.