One Ontology per Industry.
PH—004
A healthcare commercialization recommendation does not look like a PE portfolio one. The model knows why. The reason is not in the base model. It is in the translator layer that sits above it. This is the shape of that layer, and why a generalist model cannot approximate it without it.
I Why the Ontology Has to Be Per-Industry
Every vertical operates inside three constraints that a generalist model does not encode: regulatory reality, economic reality, and operating vocabulary. A hospital and a logistics firm may both have an “ops team.” The phrase means opposite things in each. The regulations that govern the hospital do not apply to the logistics firm. The unit economics that make one profitable would bankrupt the other.
A generalist model approximates all three. It gets close on average. It fails on the specifics that matter most. A recommendation that is close to right in healthcare is illegal. A recommendation that is close to right in logistics loses money. The tolerance is different in every vertical, and the generalist does not know where the tolerances are.
We code them.
II What the Translator Codes
The per-industry ontology encodes three layers:
- Regulatory surface. What actions the industry legally permits, requires, and forbids. HIPAA rules. SOC 2 gates. FDA 21 CFR Part 11 for regulated biomed. ISO 27001 for enterprise data posture. The ontology does not know law in the abstract. It knows which actions the law permits inside this industry.
- Economic surface. How the industry makes money, where margin lives, what the unit economics look like at steady state. A recommendation that moves a KPI but breaks the margin structure is wrong in a way that a generalist model never catches.
- Vocabulary surface. How the industry talks about the work. A “case” in healthcare, legal, and insurance are three different objects. The ontology disambiguates.
III Seven Verticals
Terrein currently ships seven per-industry ontologies. Public innovation, wellness and luxury, home services, hospitality, real estate, sports and NIL, consumer and creative. Vol 13 expands these one by one; what follows is what the filter does differently in each at the recommendation layer.
In healthcare (shipping Q3, not yet public), the filter refuses any recommendation that implies patient-facing automation without human review. In PE portfolio operations, the filter refuses any recommendation that commits capital across a quarterly close boundary without a board signal. In public innovation, the filter refuses any recommendation that compromises civic data posture.
The word “refuses” is exact. The illegal paths are never proposed. The recommender cannot recommend them. They do not appear in the ranked stack at all.
IV What Happens Without It
Without the ontology, the recommender defaults to the generalist’s approximation. We have tested this. In a controlled internal benchmark across three regulated verticals, the generalist produced non-compliant recommendations at a rate of fourteen percent. The ontology-filtered recommender produced non-compliant recommendations at zero percent. The cost is the ontology itself.
Non-compliant recommendations are never the customer’s problem to catch. That is what the filter is for.
V The Compounding Effect
The per-industry ontology compounds in a way a generalist model cannot. Every install adds signal to its vertical’s ontology. The fifth home-services install teaches the ontology things the first one could not. The seventh install inherits all of that teaching. A competitor with the same base model and no industry install base cannot catch up by training harder. They can catch up only by installing, and they do not install.
That is the moat, expressed as a publishing commitment: we will ship the ontology papers one per industry, across the next four quarters. The claim of defensibility becomes an object you can read.
VI What This Requires of Us
Maintaining seven ontologies in parallel is expensive. It requires subject-matter work we cannot outsource. Three of the seven ontologies are maintained by former operators from inside the vertical. Four of them are maintained by engineers who have shipped in the vertical for at least two years. We do not hire generalists to maintain ontologies. It does not work.
The trade is worth it because nothing else we do compounds like this. The base model improves on its own calendar. The engineering bench stabilizes. The ontology, uniquely, gets more valuable with every install. That is the layer that is ours.