A Working System, Then a Plan
V.E.T.S. is not a proposal for a platform. It is a platform, in production, with a proposal attached.
This page puts the evidence first. What the system already contains, what the AI layer already does, and only then what it would cost to finish and how it earns. Everything in the first half can be checked against the database. The figures in the second half are ours to state and yours to question.
What is already built
The V.E.T.S. database holds:
Scale on its own proves very little. The figure that does the real work is this one: the platform manages 47,062 animal records across its tenants. That is a live count rather than a projection, and it is the difference between a system that runs and a system that demonstrates.
See what 2,834 procedures are actually doing →
The AI layer is deployed, not planned
Fourteen AI assistants, called minions, are configured across 78 page contexts in the application. Behind them sits a retrieval index of 2,333 documents, deployed on Vertex AI Vector Search as vets_deployed_v3.
The architectural decision worth an executive’s attention is duller than either of those numbers. No model name is written into the application. Every model string lives in stbl_System_CodesGlobal and is read at runtime through astp_AI_GetModelConfig: gemini-2.5-flash for interactive chat, gemini-2.5-flash-lite for nightly batch work, gemini-3.1-pro-preview for premium evaluation.
That is why the premium tier moved from gemini-2.5-pro to gemini-3.1-pro-preview as a data change rather than a deployment. In a market where the best available model changes every few months, the cost of switching is a business input, and ours is close to zero.
One boundary worth stating plainly, because cautious buyers ask it first: the vector index holds documentation about the system — help text, knowledge-base articles, and generated descriptions of database objects. Your animal records are never embedded and are not in the index.
How the retrieval and minion layer fits together →
Why there is a market here at all
Physical assets depreciate. A barn, a stock trailer, an ultrasound unit are each worth less every year they are owned. Knowledge assets move the other way. A treatment protocol that forty practitioners have corrected is worth more than it was the day it was written.
Almost no animal-care organization owns its knowledge in a form that can appreciate. It sits in the heads of experienced staff, in paper files, and in the margins of software built for billing. When someone retires, most of it leaves with them.
What fragmented animal knowledge costs →
The commercial consequence is what makes this a market rather than a cause. Software can be copied in a quarter. A corpus that thousands of practitioners have spent years correcting cannot be, and every correction makes the next one more valuable. The defensible asset is the curated knowledge and the community maintaining it.
How expert corrections compound →
$12 Million Five-Year Open Development Plan
Everything above this line can be verified. Nothing below it can. This is a budget, and a budget is a statement of intent. We publish it in full because an open development plan that hides its own numbers is not open.
| Block | 5-Year |
| Omega Consulting & Development | $6,000,000 |
| Humble Hairpin R&D Facility | $4,000,000 |
| Operations & Administration | $2,000,000 |
| Total | $12,000,000 |
Omega Consulting & Development — $6,000,000
Development and architecture work delivered against Appframe, the enterprise framework the platform is built on.
| Category | Monthly | 5-Year | Purpose |
| Platform Development | $60,000 | $3,600,000 | Core feature development, AI integration, modernization |
| Architecture Consulting | $25,000 | $1,500,000 | Enterprise patterns, scalability, security hardening |
| Support & Maintenance | $15,000 | $900,000 | Bug fixes, updates, production stability |
Humble Hairpin R&D Facility — $4,000,000
A working ranch in Texas dedicated to IOT research with horses, cattle and exotics. Animals and husbandry expertise come from the existing ranch operation, which is why the labor line funds instrumentation staff rather than a herd.
Labor — $2,400,000
| Role | Monthly | 5-Year | Purpose |
| Research Director | $12,000 | $720,000 | IOT strategy, vendor relations, protocol development |
| Field Technicians (2) | $16,000 | $960,000 | Sensor installation, data collection, equipment maintenance |
| IOT Systems Engineer | $7,667 | $460,000 | On-site technical integration, sensor deployment, data pipelines |
| Ranch Hands (stipend) | $4,333 | $260,000 | Husbandry and animal handling |
Construction — $1,200,000
| Category | 5-Year | Purpose |
| IOT Lab Buildout | $400,000 | Sensor workshop, calibration station, network infrastructure |
| Animal Housing | $500,000 | Instrumented stalls, paddock sensors, covered arenas |
| Data Center | $300,000 | Edge computing, local storage, weather station |
Maintenance — $400,000
| Category | 5-Year | Purpose |
| Equipment | $200,000 | Sensor replacement, calibration, upgrades |
| Facilities | $150,000 | Fencing, structures, utilities |
| Veterinary Care | $50,000 | Research animal health, emergency care |
Operations & Administration — $2,000,000
Labor — $1,200,000
| Role | Monthly | 5-Year | Purpose |
| Operations Manager | $10,000 | $600,000 | Business operations, vendor management, compliance |
| Administrative Support | $6,000 | $360,000 | Bookkeeping, scheduling, communications |
| Part-time Specialists | $4,000 | $240,000 | Legal, accounting, marketing as needed |
Construction — $300,000
| Category | 5-Year | Purpose |
| Office Buildout | $200,000 | Workspace, meeting facilities, demo area |
| IT Infrastructure | $100,000 | Network, servers, security systems |
Maintenance — $500,000
| Category | 5-Year | Purpose |
| Technology | $300,000 | Cloud services, software licenses, hardware refresh |
| General Operations | $200,000 | Insurance, utilities, supplies, travel |
Revenue model: four income streams
1. IOT ecosystem
Sensors woven into the environment an animal already lives in, rather than strapped to the animal: water-trough consumption, gate counts, thermal imaging, arena footing, feed-bin levels. Each sensor is both a recurring subscription and a data point that makes the platform better at its main job.
Revenue: hardware sales, installation services, monthly monitoring subscriptions, premium analytics packages.
2. Contextual intelligence advertising
Not banner advertising. A veterinarian opening a protocol and finding they are two doses short is a moment when a supplier’s next-day price is useful information rather than an interruption. The same is true of a breeding-window calculation, or equipment overdue for replacement. The test is whether the reader would have wanted the information without the sponsorship.
Revenue: cost-per-action on purchases and quote requests, premium placement, vendor subscription tiers.
3. The compute economy
Every AI platform has a compute bill that grows with usage. V.E.T.S. treats two kinds of compute as real. Silicon compute is tokens, embeddings and vector queries, measurable in dollars. Biological compute is expert correction, protocol validation and domain judgment, and no quantity of API calls will generate twenty years of clinical experience.
That inverts the usual pricing logic. In most software the heaviest users cost the most and should pay the most. Here, the expert who corrects a shoeing protocol improves a knowledge chunk that answers questions for everyone who asks later. Their expertise subsidizes the platform, so the pricing should recognize it rather than penalize it.
Four tiers follow. Explorers use platform-funded compute at a limited rate. Contributors supply their own model API key, so their usage runs on their own compute. Patrons consent to a share of their compute improving community knowledge and pay less for doing so. Expert curators generate more value in corrections than they consume in inference, and at the top of that tier the relationship reverses into a revenue share.
Revenue: tiered subscriptions in which active expertise reduces cost, bring-your-own-model integration, premium analytics, and revenue sharing with curators whose contributions carry measurable weight.
4. White label implementations
Branded instances for breed registries and associations, veterinary hospital networks, large livestock operations, and agricultural universities. Custom development, data migration, integrations and dedicated support on the same framework. Each implementation becomes the case study that sells the next.
Where to go next
The business case is the near view. If you want the argument for why a knowledge platform for animals matters beyond the next five years, that is a different page and a longer horizon.
Where this goes beyond animals →