Ulysse
About

Why Ulysse exists

The information that would fill a veterinary clinical trial is already written down. It is written by vets, in practice, every day. It is simply not in a form anyone can search.

The story

Ulysse was founded by Perle Roche, a veterinary surgeon who spent the first part of her career in the clinical reality behind veterinary data: consultations, diagnoses, treatment pathways and the conversations with owners that decide what actually happens to an animal. A consultation note is a rich clinical document. It records the presentation, the diagnosis in the vet's own words, the bloods, the medications and the comorbidities. It is also free text, in whichever practice management system that particular clinic happens to run, and often a scanned PDF from the practice before.

The second part of her career was spent on the other side of the same industry, as a healthcare strategy consultant at ClearView Healthcare Partners in London, working on animal health commercial due diligence and performance improvement. From there the view is different. Animal health companies spend hundreds of millions each year developing new medicines, and each of the top five sponsors runs an R&D budget in the region of half a billion to a billion. Yet when it comes time to run a pivotal study, the way they find eligible patients is to ask people they know: KOL relationships, personal networks, CRO site outreach and manual chart review.

Two vantage points on the same problem. The records that answer the question, and the sponsors with no way to ask it.

That is the gap Ulysse was started to close. Not a better clinical trial services business, and not another registry, but the missing layer in between: a system that connects to veterinary practice systems, standardises what is in the records, and lets a sponsor see where eligible animals are, then recruit them through the vet who already treats them.

Why now, and not five years ago

Three things changed at once. Practice ownership consolidated, so a single data agreement with one corporate group or one cloud practice management vendor now reaches hundreds of clinics rather than one. Language models became good enough to read free-text clinical prose and scanned histories at a cost per document that makes national coverage realistic. And in human medicine, real-world data networks such as TriNetX and Flatiron proved the model completely, building exactly this layer for human trials. No veterinary equivalent exists.

Why trial recruitment first

Trial recruitment is the wedge, not the destination. It is the use case where the pain is sharpest and the buyer is clearest, and where a sponsor will pay for an answer today. It also happens to be the use case that funds the hardest part of the business, which is assembling and structuring the data itself. Every practice connected for recruitment makes the dataset more useful for post-marketing surveillance, for prescribing and outcomes intelligence, and for the benchmarking that clinics themselves will pay for. The order matters: the long-term platform is only reachable through a first product someone wants now.

Built, not just described

The riskiest assumption in the whole idea is not commercial. It is whether messy, inconsistent, multi-language veterinary records can genuinely be turned into a searchable cohort without a human templating every format. So rather than describe that in a deck, it was built first. The feasibility engine now runs end to end on 320 consultation notes drawn from four different practice systems in two languages, grades its own accuracy against ground truth, and returns a cohort in seconds. That is the case example, and it is open for anyone to use.

What we believe

Three convictions the company is built on

The trusted vet is the channel

An owner decides whether their animal joins a study based on what their own vet recommends. No amount of sponsor marketing replaces that. Any recruitment model that routes around the treating vet is working against the only relationship that matters in animal health.

Practices must be paid, not asked

Research participation has historically been a favour asked of busy clinics. Revenue share, trial income and analytics returned to the practice make it a commercial decision instead. Data partnerships that only benefit one side do not survive.

Compliance is a design constraint, not a policy page

De-identification, owner consent managed at practice level by the treating vet, transparent data-use terms and veterinary clinical governance are built into the first integration. Retrofitting trust onto a data business does not work.

Why us

Clinical credibility meets commercial strategy

Perle Roche
BVetMed (Hons) MRCVS
Founder

Veterinary surgeon

BVetMed, Royal Veterinary College. Fluent in the clinical reality behind the data: practice workflows, diagnoses, treatment pathways and owner conversations.

Healthcare strategy consultant

ClearView Healthcare Partners, London. Experience across animal health business diligence and performance improvement, including market sizing, commercial due diligence and value creation.

Team

The team we are building

Ulysse is currently a company of one with a working technical proof point. These are the first roles, in order.

Technical co-founder, CTO

A data-trained leader from data engineering or machine learning, ideally with healthcare or life-sciences data experience. Owns the data engine, the practice management system integrations and the security architecture. Equity partner from day one.

Founding data engineers, one to two

Building the integration pipelines and the language-model layer that structures free-text clinical records. Hired with proof-of-concept funding.

Veterinary advisory board

Practising first-opinion vets and specialists, for clinical governance, protocol input and credibility with the practice network. Advisory equity.

Fractional specialists

Data-privacy and regulatory counsel, plus a pharma business-development advisor, as the first pilot approaches.

The plan

Eighteen months to a proven pilot

1

Months 0 to 4 · Validate

30+ discovery interviews across pharma clinical operations, CRO leads, practice management vendors and corporate group chief medical officers. Confirm willingness to pay. Define the MVP data model with two to three pilot practices.

2

Months 4 to 10 · Build and pilot

First data integrations live with one practice management system and one practice group. Feasibility-count MVP on the cloud data engine. Paid pilot with one sponsor on a real protocol.

3

Months 10 to 18 · Prove and raise

Demonstrate recruitment uplift against a manual baseline. Expand to 200+ practices and a second sponsor. Seed round to scale integrations and team.

Where partners and incubators accelerate this. Expert feedback on concept and pricing from veterinary and data-science reviewers. Clinical development and regulatory input to shape the first pilot protocol. Introductions to a technical co-founder, corporate groups, practice management vendors, sponsor clinical operations and pre-seed investors. Collaboration on veterinary data standards and real-world evidence. Proof-of-concept funding for the first integrations ahead of an institutional round.