End-to-End AI Solutions Tailored to Your Business

I operate as a solo practitioner by design, which means there is no team to staff and no incentive to oversell. You get senior expertise from day one - not a partner who sells and a junior who delivers. My career spans McKinsey, investment banking, and managing a 300-person data program, combined with fourteen years of hands-on data science. That combination of executive-level business judgment and deep technical execution is not something the market usually delivers in one person.

The full spectrum. I advise on AI strategy, build prototypes, convert them to production systems, help you recruit and coach your first data scientists, and stay available for hard problems long after the initial engagement ends. A common thread is cross-pollination: techniques and insights I bring from other industries often unlock the most impactful breakthroughs. Engagements can be as short as a couple of hours of expert guidance on the phone, or as long as a multi-year embedded collaboration.

Yes, and that is by design. A single senior practitioner who covers strategy, modeling, and deployment eliminates the handoff losses that plague larger teams. When I am at capacity, I migrate to the highest-value tasks first and help you build or hire the team that will take over the rest.

I start with a focused workshop: we map the business problem, review what has been tried, audit the available data, and frame an agile action plan designed for maximum early impact. You walk away from the first week with a clear picture of what will be built, in what order, and why.

It depends entirely on where you are in your AI journey. A strategic assessment can take a few days. A prototype that proves a business case typically takes a few weeks. Converting that into a production system, recruiting your team, and coaching them to autonomy can span several months. Most clients engage me at multiple stages over time, as their needs evolve.

Both. I work with clients worldwide and adapt to whatever format is most effective for the engagement - whether that means being on-site for workshops and team coaching, or working remotely for focused development and research.

Access to the relevant data, and ideally a technical point of contact who knows the data landscape - someone who understands the upstream systems, their constraints, and their quirks. Beyond that, I navigate autonomously, whether I need to collaborate with business managers, research scientists, or production engineers.

Those are exactly the situations where I add the most value. Extreme situations - tiny datasets, high stakes, noisy signals - are where mathematical depth matters most and where off-the-shelf tools fail. I have built production models on datasets as small as a few hundred observations. On the other end, I also love situations where we have billions of records or features - in such situations only cutting-edge techniques allow extracting the signal in real time.

No - and this is a pattern I see often. Companies spend six months to three years building pipelines that never deliver useful data, because they were unsure which data elements carried the real predictive signals before they started. The right approach is to build rough prototypes in parallel, in commando mode, to discover which data actually matters. The pipeline work then becomes focused and far cheaper.

Yes. If a project is fundamentally product development dressed up as data science - a dashboard, a standard integration - I am not the right fit. Similarly, if an organization has ambitious AI goals but does not yet collect the data needed to pursue them, I will tell you exactly what to do first and suggest we reconnect once the prerequisites are in place. I would rather help you spend your budget wisely than take on work that will not deliver meaningful results.

I default to open-source tools - Python and R for modeling, with the ability to write performance-critical components in C++, Go, or Rust. I integrate into existing client stacks including TypeScript, JavaScript, .NET, and Java. Open source is not an ideology; it is a practical advantage: it avoids vendor lock-in and gives you full control over your systems. However, where intellectual property is key - for example, a packaged software product resold to customers - I can reimplement or help reimplement the mathematical techniques used, in the target language and framework of your choice.

I bill for time spent. ML and AI work is research and development by nature - scoping a fixed price would mean either overcharging you or cutting corners. My value is highest on the hard problems - as soon as a task becomes routine production work, I help you hire or find more cost-effective resources to own it.

There is no minimum. I regularly take calls of a couple of hours to advise on a specific AI or ML question - whether it is a strategic decision, a mathematical problem, or a second opinion on a technical approach.

Typically within one week to one month, depending on my current workload and the nature of the engagement. For urgent advisory needs, I can often accommodate shorter timelines.

Every engagement is covered by an NDA and a contract that guarantees full ownership of all deliverables to the client. The only exception is code I bring from external sources - my own tools or third-party open source - which always remains under the client's control. I maintain no third-party partnerships and will never resell or reuse your work.

Yes. I am happy to connect you with former clients who can speak to the quality of the work and the nature of the collaboration.

Always. I never leave a client without a path forward. In practice, most clients call back months or years later when a new challenge arises - and I am happy to pick up where we left off, whether that means a quick phone call or a new engagement.