MaxGradient

Your data never has to leave your environment. I design prototypes around your security constraints from the start, not as an afterthought.

In practice, that means options like local models that run entirely on your infrastructure, private cloud deployments, or API configurations with enterprise agreements that contractually prohibit training on your data. Retrieval-augmented generation (RAG) architectures let the AI reference your documents without sending them to a third party.

The right architecture depends on what the prototype needs. A text classification task might run fine on a small local model. A complex document analysis might need a frontier model's reasoning, in which case we use an enterprise API tier with data protection guarantees. That's part of what the assessment determines.

I work under NDA and can operate within whatever access controls, VPN requirements, or data handling agreements your security team needs.

Model selection is part of the assessment, not a predetermined choice. The right model depends on the task, the data sensitivity, and the economics of running it at scale.

Some use cases need a frontier model's reasoning depth for nuanced document analysis or complex decision support. Others work just as well with a smaller, cheaper model that runs locally and costs almost nothing per query. The evaluation measures which approach actually performs for your specific use case.

For companies with data sensitivity requirements, major providers offer enterprise tiers with contractual guarantees: your data is not used for training, is not stored beyond the request, and does not leave specified regions. These aren't theoretical options. They're how production AI deployments work at companies that take IP protection seriously.

The roadmap I deliver includes model recommendations and the reasoning behind them, so your team can make informed decisions as models improve and pricing changes.

Then the assessment tells you that, and it's still a valuable outcome. An engagement that prevents a six-figure failed build is one of the highest-ROI results I deliver.

When the answer is "don't build this," the assessment doesn't stop at no. It explains why: is the data insufficient? Is the tolerance for error too low? Is there a simpler, non-AI solution that solves 80% of the problem? You get a specific recommendation, not a shrug.

The strategic assessment and 12-month roadmap still have value even when one particular use case doesn't pass evaluation. Your business likely has multiple places where AI creates value. The assessment redirects investment toward the ones that actually work.

The engagement is designed to stand on its own. The roadmap includes build-vs-buy recommendations, infrastructure prerequisites, and sequencing, so your team or a vendor can execute against it without me in the room.

The prototype isn't a throwaway demo. It's a working system with documented architecture, evaluation criteria, and known limitations. Your engineering team inherits something they can build on, not reverse-engineer.

If you need ongoing advisory support as you execute the roadmap, that's a conversation we can have. But the engagement isn't structured to create dependency. You get a plan and the evidence behind it, and you own both.

That's the point, not the limitation. The failure mode in enterprise AI is the gap between the people who set strategy and the people who build it. Recommendations get misinterpreted. Prototypes miss the business context. Evaluations measure the wrong thing.

I've spent 20+ years working across product leadership, engineering, and AI implementation at companies like eBay and New Relic. The person mapping your workflows is the same person writing the evaluation code and measuring results. No handoffs. No translation layer.

This approach works because the engagement is scoped to what one senior practitioner does well: diagnosing the problem, building a focused prototype, and measuring whether it works. It's not a full platform build. It's the strategic foundation that tells you what to build and why.

Typically 4 to 6 weeks from kickoff to final deliverables. The biggest variable is on your side: how quickly you can provide data access and domain expert time.

The first week is the most intensive. That's when I need 6-8 hours with your domain expert to map workflows and understand the problem. After that, the build and evaluation phases require less of your team's time, mostly review sessions and feedback on prototype output.

The final readout includes all four deliverables: strategic assessment, working prototype, evaluation scorecard, and 12-month roadmap. Your decision-maker sees the evidence and the recommendation in one session.