🐾 Pre-seed · SAFE · 2026

Waze for pet parenting.

Pet social networks died because they were only entertainment. PupCat — a pre-seed consumer social and pet tech startup — uses entertainment as the distribution channel — and builds a community data product behind it: millions of individual pet observations, merged into answers no search box can give.

✓ Working product, built & tested — demo & TestFlight access on request ✓ Waitlist live — current count shared in the deck Pre-launch — live metrics start at launch; we won't invent any

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The thesis

“Didn't pet social already die?” Yes. That's the point.

The graveyard is the strongest argument for the model — every failure had the same missing layer.

✗

Klooff — shut down. Cute-photo feed, no durable utility, no revenue model.

✗

Petzbe — plateaued. ~450K users in 8+ years; couldn't raise VC. Fun without utility doesn't retain.

✗

The common failure mode: Instagram already has pet content — a separate network needs a reason to exist beyond the feed.

✓

Fun is our distribution, not our product. Free-vote weekly contests acquire users at near-zero CAC.

✓

Utility is the retention anchor. The Findings Engine answers “who else's pet went through this?” — structured, anonymized, breed- and age-matched.

✓

Data is the compounding asset. Food, litter and observation data accumulate into a corpus later entrants can't recreate.

Market — proven by others

Every number here is a competitor's, not ours

400K

entries in a single pet contest

America's Favorite Pet 2025 — demand proven; paid-vote model in a trust crisis (Slate)

$20M

ARR from pet training subscriptions, no VC

Woofz — 21M+ downloads, no community layer

80M+

US households with a pet

Context for the wedge — near-universal category

~$150B

US pet industry per year

Context only — see the honest SAM below

The honest SAM — bottom-up

We don't touch the food transaction, so $150B is context, not our market. What we actually sell into: pet-brand digital media & sponsorship budgets (category-exclusive placements, sellable from 10K users) and consumer subscriptions at the price band Woofz already proved (~$100/yr for training-led premium). Sponsorship starts the revenue line; subscriptions scale it; anonymized brand insight compounds it.

Wedge → expansion

Enter through social + contests (near-zero CAC, high frequency). Expand into findings + training (high LTV, proven monetization). Long-term: data products, televet partnerships, ecosystem revenue. Each stage funds the next — no “boil the ocean” step anywhere in the plan.

Competition

Everyone owns one vertical. Nobody owns the loop.

Including the incumbent nobody lists: unstructured free text on Reddit and Facebook.

Who	What they proved	What they're missing
Reddit, Facebook breed groups, Nextdoor	This is where US owners actually ask today — the real incumbent	Unstructured, unsearchable, no breed/age matching, answers evaporate in the scroll
Contest platforms (AFP, KingPet)	400K entries — demand is huge	Paid votes → chronic trust crisis; single-use, no community
Training apps (Woofz, Dogo)	$20M ARR — monetization works	No social layer, no community data, aggressive funnels
Health / televet (Airvet, PetMD, Vetster)	Funded and growing	Static triage or transactional visits — no “similar pets” community data
Pet social (Petzbe, Klooff)	The desire for a pet network exists	Dead or stalled — fun without utility

PupCat's combination — free-vote contests + structured community findings + anonymous brand stats in one loop — is not offered by any player above. Full competitive research with sources is available with the deck.

Moat & the cold-start answer

A weekend clone gets the feed. It doesn't get the corpus.

Contests bring owners in

Shareable badges and regional titles produce their own distribution — acquisition cost stays near zero.

Owners log findings

Structured observations — breed, age, food, behavior, outcome — accumulate into a dataset that exists nowhere else.

Matching gets better

Better matches → stronger retention → more findings. The loop tightens with every cohort. That's the Waze dynamic.

Cold start, answered directly

Matching needs density, so we don't launch thin everywhere — we launch deep in one pet-dense city. Regional contests concentrate users in the same metro; vet-reviewed reference content seeds the findings experience before the corpus is large; and k-anonymity (results shown only when n≥5) keeps early matches honest instead of creepy.

Privacy architecture as an asset

Built and tested, not promised: findings are shown only as breed + age band + outcome (never a name or photo), analytics events carry no personal identifiers, legal pages ship CCPA/CPRA-first (English live on this site; EN/ES/TR built in the product), and we don't sell personal information. In a data business, this is the license to operate.

The asymmetric channel

The data layer assistants will reference for pet questions

This is the part of the thesis we think is most underpriced. PupCat's API is AI-surface-first from day one — OpenAPI + MCP, ready for ChatGPT Apps and assistant ecosystems. When someone asks their assistant “why is my cat meowing at night?”, the grounded answer comes from a structured, breed-matched community corpus — ours. No pet incumbent is positioned for this, and it's an acquisition channel that doesn't run through the App Store.

WHAT AN ASSISTANT CALLS — SAMPLE RESPONSE (ILLUSTRATIVE DATA)

GET /v1/findings/{id}/similar

{
  "total": 412,
  "by_breed":     [ { "breed": "British Shorthair", "count": 388 }, … ],
  "by_age_group": [ { "group": "3-5", "count": 341 }, … ],
  "outcome_distribution": {
    "resolved_food_change": 268,
    "seasonal_behavior": 91,
    "vet_visit": 29
  },
  "samples": [ { "body_excerpt": "…", "age_group": "3-5",
                 "breed": "British Shorthair", "outcome": "…" } ],
  "disclaimer": "NOT_MEDICAL_ADVICE"
}
// samples: [] whenever total < 5 — k-anonymity by contract
// no pet_id, no owner, no photo: excluded from the schema itself

Why this matters

This is the real response contract — the shape the product's own mobile app consumes today (OpenAPI 3.1, typed clients; MCP server on the same contract). The numbers above are illustrative; the schema is not. Identity fields aren't stripped by a policy layer — they don't exist in the schema at all, and that's covered by tests. Full API documentation is available with the deck, alongside a live demo.

Business model

Revenue does not wait for scale

The social layer stays free forever — monetization never taxes the growth loop.

Stream	Timing	Note
Food brand sponsorship	Day one	Category-exclusive placement on contests and badges — sellable at 10K users
Premium subscription	Month 6–12	AI-personalized training + advanced stats, at the price band Woofz proved (~$100/yr)
Anonymous brand reports	Year 2	Breed/region insight from the data moat — sold only after critical mass (~50K+ active profiles, est.)
Ecosystem	Year 2–3	Televet partnerships, services, commerce — partnerships, not builds

Full financial model, sponsorship unit pricing and use of funds are in the deck — deliberately, not accidentally.

In the deck

What we share in conversation, not on a public page

Strategy stays gated; facts stay public. Here's exactly what the deck adds:

Launch city & seeding playbook

The metro we launch in, and the step-by-step local seeding plan for it.

Financial model

Sponsorship unit pricing, premium conversion assumptions, CAC/LTV math, use of funds.

Current waitlist count

Measured continuously; the up-to-date number is shared in the deck.

Full competitive research

The sourced version of the competitive landscape, player by player.

Live demo & TestFlight

A walkthrough of the working product, and build access on request.

Terms

Cap and round terms — discussed live, not published.

No NDA required for the deck.

18-month plan

What this round proves

Every metric below is a target we commit to measuring in public — not a claim.

Ship — MVP live in one city

iOS + Android + public web profiles · weekly contests running · Findings Engine v1. Launch dense in a single high-pet-density US metro, seeded through local groups, dog parks, shelters and vets. The city — and the seeding playbook — are named in the deck.

Prove — the loop works

Viral factor K > 0.5 · DAU/MAU ≥ 25% · ≥40% of actives voting weekly · ≥3 similar-finding views per logged finding. The dashboard for all four is already built.

Earn — first revenue

First brand sponsorship signed · premium launched at the proven band · 25–50K users. These are the metrics a seed round is priced on.

Team

Capital-efficient by design

Two founders and a hands-on engineer, a working product, and an AI-assisted development workflow — small check, long runway.

Nergiz Rahimzade

Engineer & US Growth

Computer engineering undergrad, entering her third year. Builds product features and runs PupCat's live demos; leads campus community and first users in the US.

Hasan Rahimzade

Co-Founder

US-based; graduate student in California. Leads fundraising, partnerships and the US go-to-market — on the ground, in person.

Barış Yüceses

Co-Founder

Product + full-stack engineer; CRM/SaaS background. Built and tested the working PupCat product with an AI-assisted workflow; leads PupCat's Europe presence.

We're assembling a veterinary advisory board for launch — if you can make an introduction, we'd genuinely welcome it.

The ask: $500K pre-seed on a SAFE

18 months of runway to launch, win one city, sign the first sponsor, and hit seed-round metrics. Pet social died because it was only fun — we use fun as distribution and build a Waze-style data product behind it.

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Hasan Rahimzade & Barış Yüceses · [email protected]