About
HAP is built from Austin Stewart's cross-domain path through neuroscience, medicine, business, and AI research.
The service exists as a hands-on alternative to online AI info-lecture classes for people who want direct implementation, real boundaries, and a research-informed guide rather than generalized enthusiasm.
Founder background
Austin Stewart earned his B.S. in neuroscience from Tulane University in 2004.
He later earned his M.D. in 2014, but became disillusioned by the direction of healthcare and chose to work independently as a healthcare advocate until Covid disrupted that business.
He then worked for a family office in Covington, performing due diligence on biotech and biopharma companies that primarily focused on non-opioid pain relief and recovery medicine using NAD+ and other non-dependent opioid approaches.
He also earned his MBA from LSU Shreveport with a concentration in entrepreneurialism and international business.
Shortly thereafter, he began splitting time between Orange, Virginia and New Orleans, Louisiana to pursue cross-domain research using AI.
In December 2025, he formed Ozymandius Applied Research Lab LLC, DBA HAP the AI Coach, to create a hands-on alternative to the passive online AI lecture classes that dominate the market.
Why that background matters
HAP is not presented as a generic prompt-coaching service. Its posture comes from a founder whose work moved across science, medicine, advocacy, investment diligence, business training, and AI research.
That cross-domain path is why HAP emphasizes architecture, behavior, incentives, boundaries, and practical implementation rather than tool hype alone.
Research foundation in AI
One strand of the broader research work is the Theory Translation Layer Protocol, or TTLP, a unified manuscript that frames cross-domain reasoning as a boundary- preservation problem rather than as free-form analogy.
In that work, behavioral modeling, market forecasting, and other high-stakes transfers are treated as variants of the same architectural challenge: how to move a useful structure from one domain into another without contaminating the target.
The manuscript defines explicit transfer steps, anti-bleed rules, typed gating logic, and measurement layers. A separate assessment of the work recognized those elements as genuine strengths even while arguing that the research still needs a more locked empirical validation layer.
That matters for HAP because the service is not positioned as generic AI coaching. It comes out of a larger attempt to think more rigorously about translation, contamination, constraint, and disciplined reasoning across domains.
Publicly, the cleanest formulation is this: disciplined cross-domain transfer without contaminating the target. That is a stronger and more useful claim than vague promises about AI intuition or novelty.
Who it is for
HAP is especially well suited to private individuals, owner-led firms, and micro-SMBs that need leverage but do not want to build a full internal AI function or sit through abstract classes that never touch their real workflows.