We start with your business problem. Not the technology.
Every engagement begins with a simple question: where can AI increase revenue, cut costs, or speed up operations? We figure that out, then we make it happen.
Let's TalkWhy most AI projects fail.
They start with technology and work backward. A vendor pitch. A shiny demo. A pilot that never scales.
88% of AI proofs-of-concept never reach production. Not because the technology failed. Because the project was designed to succeed in a demo, not survive real operations. Pilots use clean data, limited scope, and forgiving conditions. Production means messy data, edge cases, and real users who do not follow the script.
The companies that succeed treat AI differently. They pick one pain point, define how they will measure success, and prove value with real data before committing resources.
We do the same thing. We start with your business, your operations, your revenue model. We find the specific problems where AI creates the highest impact. Then we build, test, and prove value before scaling.
Read: Your AI Pilot Worked. That's the Worst Thing That Could Have Happened.How we work together.
Every company is different. The engagement fits your situation, not the other way around.
AI Strategy and Implementation
Best for: Companies that know they need AI but are not sure where to start or have tried and stalled.
We look at your operations, your costs, your revenue model. We identify the highest-impact opportunities using a simple filter: is the task repeatable, rule-based, and measurable? If yes, AI can probably help. If it requires judgment, context, or creativity, keep humans on it.
What you get: A clear roadmap tied to business outcomes. Working AI that delivers measurable results. Not a strategy deck that sits on a shelf.
Typical engagement: 3-6 months. Starts with a focused assessment (2-4 weeks), then moves to implementation.
Technical Leadership
Best for: Companies at a turning point. Preparing for an acquisition. Scaling the engineering team. CTO gap.
We step in as CTO, advise your existing technical team, or lead specific initiatives. Architecture reviews, hiring decisions, vendor evaluations, AI strategy, roadmap ownership. Whatever the situation requires.
What you get: Senior technical leadership with real operating experience. Not a consultant who reads frameworks. An operator and builder who has led technical teams, shipped products, and sold a company.
Typical engagement: Flexible. Some companies need 3 months of intensive leadership. Others need an ongoing advisory relationship.
Targeted Advisory
Best for: CEOs and operators who need a senior technical advisor for specific decisions.
Board-level strategy. Technical due diligence for acquisitions. Vendor evaluations. Second opinions on critical technology choices. We go deep on the specific problem and give you a clear-eyed assessment.
What you get: Direct, actionable advice from someone who has been on both sides of every table. No padding. No equivocation. Straight answers.
Typical engagement: Project-based. A few weeks for due diligence. Ongoing for advisory relationships.
Before you commit 90 days, answer six questions.
Most AI projects fail because they are scoped like technology rollouts, not process experiments. We work through these six questions with every client.
What specific business metric will improve?
Not "efficiency" or "productivity." A number. Revenue per rep. Time to close. Error rate. Cost per transaction.
Can you measure the baseline today?
If you cannot measure where you are now, you cannot prove AI made it better.
Is the process repeatable and rule-based?
AI is excellent at consistent, high-volume, rule-following work. It is poor at novel judgment. Match the tool to the task.
Do you have the data?
Not "big data." The right data. Clean enough, accessible enough, and representative enough to train or configure an AI system.
Who owns the outcome?
A person, not a committee. Someone who will be measured on whether this succeeds.
What happens when the AI is wrong?
Every AI system makes mistakes. The question is whether the cost of those mistakes is acceptable and whether you have a process to catch them.
If you can answer all six clearly, you have a viable AI project. If you cannot, the project needs more scoping before it needs more technology.
Read: Before You Bet 90 Days on AI: An Operator's Scoping ChecklistWe know what is real and what is marketing.
Most of what vendors call "AI" is not AI. When we built an AI agent from scratch, the actual AI component was a small fraction of the total system. The rest was traditional software engineering: data pipelines, validation logic, error handling, integration code, and monitoring.
This matters because it changes how you evaluate vendors, estimate costs, and plan implementations. When someone tells you their product is "AI-powered," we look at what is actually under the hood.
We also evaluate AI privacy and security. We compared the privacy policies of 11 major AI platforms and found that every consumer tier trains on your data. Every one. The enterprise tier is the minimum for business use.
The process.
Discovery call
We talk about your business, your challenges, and what you are trying to accomplish. No pitch. No slide deck. Just a conversation about whether we can help.
Assessment
We work through the six scoping questions. We look at your operations, technology, and team. We identify the highest-impact opportunities. Typically 2-4 weeks.
Execute
We do the work. We build, test, and iterate. We report progress in plain language tied to the metrics we agreed on. No jargon. No hand-waving.
Prove and scale
Once something works, we document what we learned and build the playbook for the next initiative. The goal is to make your team self-sufficient.
What makes this different.
Operator and builder, not observer.
I spent 10 years as CEO of an enterprise ML company. Built every function from scratch. We know the difference between advice that sounds good in a boardroom and advice that survives Monday morning.
Technical depth that goes all the way down.
Princeton PhD in Computer Science. We evaluate architecture, technical debt, AI capabilities, and team quality at a level most advisors cannot.
AI since before the hype.
I have been building and deploying ML products since the early 2000s. This is not a pivot into a hot market. This is 20+ years of pattern recognition applied to your business.
Business outcomes, not technology demos.
I have contributed to over $50M in enterprise software sales. We connect technology to revenue. Every engagement ends with a measurable result.
"I benefited greatly from Stef's mentorship, his tremendous depth in technology and skill managing a complex business. He builds organizations of universally highly talented people and strong relationships with everyone he interacts with. Stef will never compromise on quality, requiring excellence from everyone in the organization. I would work for him again in a heartbeat."
Patrick Austermann
Engineering Leader | 2x founder | 2 exits | 1 IPO
Go deeper.
Common questions.
How does Zaruko's AI consulting process work?
Every engagement starts with a discovery call, followed by a 2-4 week assessment where we work through six scoping questions to identify highest-impact opportunities. Then we execute: build, test, and iterate, reporting progress tied to agreed metrics. Once something works, we document the playbook and build your team's self-sufficiency.
What types of engagements does Zaruko offer?
Three models: AI Strategy and Implementation (3-6 months, for companies that need AI but aren't sure where to start), Technical Leadership (flexible, stepping in as CTO or advising technical teams), and Targeted Advisory (project-based due diligence, vendor evaluations, or board-level strategy).
What should I know before starting an AI project?
Answer six questions: What specific business metric will improve? Can you measure the baseline today? Is the process repeatable and rule-based? Do you have the right data? Who owns the outcome? What happens when the AI is wrong? If you can answer all six clearly, you have a viable AI project.
Let's talk.
Tell us about your situation. We'll be straight with you about whether we can help.