Questions we get asked.

Mavaos, LLC is a by-referral growth and AI consultancy based in Massachusetts and founded by Jordan Staab. It works on two core problems: growth systems measured to revenue rather than to activity metrics, and AI systems architected to survive rapid model turnover. Clients are typically enterprises, private equity and venture firms, and operators of established businesses.

Three things. First, engagements are led by an operator who has run the kind of business being advised, not by a partner who sells and delegates. Second, the practice concentrates in regulated categories where most agencies lack the judgment to know which winning tactic is a liability. Third, compensation is never tied to media spend, which removes the structural incentive to recommend spending more.

Enterprises needing practical AI and growth work that survives procurement and a board. Private equity firms needing diligence on AI-focused targets and value creation in portfolio companies. Venture firms needing technical and growth diligence, and help getting existing investments unstuck. Operators and founders of established businesses who need leverage quickly. Engagements are limited in number and come by introduction.

Lynnfield, Massachusetts, in the Boston area. Clients are across the United States and most work runs remotely.

Monthly retainer for ongoing engagements and fixed fee for scoped diagnostic, diligence, or build projects. Mavaos does not take percentage-of-spend compensation or vendor commissions. Percentage-of-spend rewards a firm for spending more rather than spending better; vendor commissions reward recommending software you may not need. Both quietly corrupt advice, so neither is used. Specific pricing depends on scope and is discussed directly.

Diligence and diagnostic projects usually run one to three weeks. Build and growth engagements typically run in multi-month arcs, structured so that the client's team can take over. The stated goal is to hand back a working system with documentation, not to become permanent infrastructure.

Yes, and it's frequently the better structure. A capable in-house or agency media team that lacks regulated-category experience doesn't need replacing — it needs the constraints made explicit and the unit economics rebuilt. Mavaos operates equally as a strategy and diligence layer above an existing team or as the team running the program.

When you need a large team deployed on-site. When the company is pre-revenue and there's no existing engine to diagnose. When you want a vendor to own a function permanently rather than build it and hand it over. When the requirement is legal advice — that comes from counsel, not from us. We say this early rather than scoping something we'd do adequately.

Yes, routinely. Mavaos works with confidential deal and business material as a matter of course and operates under whatever confidentiality and conflict terms an engagement requires.

Performance marketing in categories where the method of customer acquisition is governed by law rather than only by platform policy — consumer finance, debt relief, wealth management, insurance, home security, alcohol, and similar. The discipline is running campaigns that are both profitable and defensible: provable consent, substantiated claims, controlled data handling, and an audit trail. More on regulated growth.

The routing system that decides which buyer receives a lead and at what price. An incoming lead is offered to a ranked list of buyers, each bids or passes, and the lead sells to the best match within a fraction of a second. Ordering, bid rules, timeouts, and fallback tiers determine how much of the lead's value the seller captures — which is why two companies with identical traffic can have very different margins. More on lead economics.

Durability. Standard technical diligence assesses whether software is well-built, secure, and maintainable. AI diligence adds the question of whether the advantage is owned or rented: how much value depends on a third-party model, what happens when that capability commoditizes, whether the data position actually compounds, and how gross margin behaves as inference volume scales. More on AI diligence.

A design in which no application logic assumes a specific model or vendor. Model calls pass through an internal interface, prompts and context assembly are configuration rather than embedded code, and an evaluation harness built on the real workload lets a new model be scored before adoption. The test is whether a materially better model could be adopted in days without touching the application. More on private AI.

Email [email protected]. A short note describing the specific problem gets a substantially better response than a general inquiry. If you were referred, mention by whom.

Yes. "By referral" describes how most engagements have historically arrived, not a rule that blocks a good conversation. Tell us how you found us and what the problem is.