What happens to Pawssier when AI gets better?

As we've built Pawssier, we have challenged ourselves to think about what will happen to us as general AI continues to get better.

The extraction gap will continue to narrow

In a previous post we wrote about the five specific ways general AI fails on pet health records today: unit ambiguity, field confusion, silent assumptions, no validation logic, no field-level provenance. Those failures are consistent and specific enough that we can benchmark against them. In practice, they show up as things like a general model grabbing the printed date on a certificate instead of the administered date, so a compliant pet looks overdue, or filling a missing lot number with something plausible that then passes downstream QA unchallenged.

But the next generation of general models is going to get meaningfully better at document parsing. The extraction gap will narrow, and anyone who builds their entire competitive position on performing better than the current best model is building on a shrinking foundation.

So if extraction quality alone isn't the answer, what is?

For us, the answer has less to do with the extraction itself and more to do with what gets built around it.

Integration depth. Platforms that integrate Pawssier build their downstream workflows around our output format. Their onboarding flow expects a vaccines array in a specific structure. Their compliance checks reference administered dates and due dates as distinct fields. Their dashboards are built on confidence scores and provenance tracking that tells them which fields to trust and which to flag for review. We also offer transforms, so when a platform has its own preferred output format, we adapt to that instead of requiring them to adapt to ours. Either way, the integration depth builds in both directions. Once a platform's intake pipeline is built around a specific output, migrating away means rebuilding that pipeline from scratch, re-mapping every field, re-testing every downstream dependency. The PSRF itself is replicable. The switching cost that builds up once a platform is live on it is what actually compounds over time.

For smaller platforms, the value is simpler: Pawssier handles the parsing layer so you don't have to build it yourself, and the output scales with your product as it grows. You don't need a compliance dashboard or a structured QA process on day one to benefit from structured output on day one.

Data depth. Veterinary records are a mess. Handwritten notes, multilingual vaccine stamps, documents printed from a dozen different practice management systems that never agreed on a format, scans taken at weird angles on someone's phone. Building a model that handles all of that requires exposure to all of that, across hundreds of document types, in 27+ languages, with labeled ground truth for what each field actually means in context. General AI will get better at parsing, but it can't shortcut the domain-specific foundation we've been building through our synthetic data pipeline, self-healing QA, and observed error patterns. We don't train on customer data.

What compounds over time is the synthetic corpus, the edge cases our QA surfaces, and the patterns we observe across document types. The data scarcity problem in veterinary AI is structural, and that foundation gets harder to replicate the longer we run it.

Neutral positioning. A practice management company could build a parser, and some surely will. But they'll build it to pull more clinics and downstream platforms into their own ecosystem, because that's their business model. A boarding facility or insurer receiving records from fifty different clinics across ten different practice management platforms doesn't want a parser tied to any one of them. They need something with no stake in which system a clinic uses. Pawssier has no stake in that. We're not trying to win the clinic relationship or expand into practice management. That structural neutrality is something a PMS company can't credibly offer, because serving everyone equally runs against their incentives.

What this looks like as AI keeps improving

The platforms already using Pawssier today are the clearest answer to the original question. When general AI gets better at document parsing, those platforms don't need to do anything. The extraction underneath improves and they absorb it, because the layer they built to stays the same. The switching cost for a platform that's already live on Pawssier isn't "find a better parser," it's "rebuild your entire intake pipeline," and that asymmetry is what makes infrastructure durable in a way that a point solution built on raw extraction quality isn't.

The integration depth, data foundation, and neutral positioning we've described above exist today, not in theory. A new entrant building toward the same position would need to replicate years of edge-case exposure, sign partners who already have a working solution, and establish trust in an ecosystem where the standard is already being set. The window to build this from scratch is narrowing, which means the platforms choosing an infrastructure partner now are making a longer-term decision than it might appear.

Stripe's durability comes from every developer having already built to their API, not from having invented payment processing. The PSRF becomes the schema standard platforms assume exists. The partner network becomes the data flywheel that makes every new integration smarter than the last. And that dynamic gets stronger as AI improves, not weaker, because a better extraction engine underneath a schema standard is just a better version of the thing, not a replacement for it.

The broader question

Pawssier isn't the only company that needs to think about this. The same question applies across most of the AI product landscape right now.

AI scribes are doing something genuinely useful today, but if the next generation of models handles clinical documentation natively inside the EHR, what's the standalone product built on? Apps that can be vibecoded in an afternoon by a developer with the same tools as everyone else don't have much protection when the underlying capability becomes a commodity. Agents that automate a specific workflow are only as durable as the gap between what the model can do and what the workflow actually requires.

The businesses that come out of the next few years with something durable are probably the ones that did one or more of a few things: built deep integration into the workflows their customers can't easily migrate away from, accumulated proprietary data that compounds in a way a general model can't replicate, or established a structural position in their ecosystem that doesn't get disrupted when the underlying model improves.

AI is constantly getting better, so it's critical we evaluate whether what we've built sits on top of that improvement or gets replaced by it.