Dating for people whose pets are non-negotiable

"Dog person" is not a personality. Three rescue greyhounds, twice-daily walks, and a strong opinion about raw feeding? That is compatibility data. And Affinity Atlas captures all of it.

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How it works for pet people

Powered by Interest Q&A. hierarchical There is no "Pet API" to connect - so Affinity Atlas collects structured data through a branching questionnaire designed to feel natural, not tedious. Your answers feed directly into the same Affinity model that powers Spotify and Steam matching.

The questions adapt based on your answers. It is not a flat survey - it is a conversation that goes deeper the more you share:

Are you a pet person? Yes / No / Open to it

What kind of pets? Dogs, cats, rabbits, reptiles, birds, fish, exotic...

How many do you have? 1 / 2-3 / 4+ / Planning to get one

How often do you walk them? Twice daily / Once daily / Few times a week

Favourite breeds? Free text with niche detection

Dealbreakers? Allergies, specific animals, no-pet households

How important are pets to your lifestyle? Central / Important / Nice to have

Every answer generates data the algorithm can score. Frequency answers (twice-daily walks) become EngagementFactor inputs. Preference strength ("pets are central to my life") becomes SentimentFactor. And answer rarity across all users drives NicheWeight - "I have a Maine Coon" is rarer than "I have a cat."

Niche overlap > mainstream overlap

Both being "dog people" is a popularity 65 overlap. Most people like dogs. It barely registers. Both having rescue greyhounds and doing parkrun with them every Saturday? That is a signal.

Mainstream

"I like dogs"

Frequency 65% → NicheWeight 0.35

Niche

"2 rescue greyhounds, raw-fed"

Frequency 3% → NicheWeight 0.97

The hierarchical Q&A ensures the algorithm has enough structured data to tell the difference. "Dog person" is Tier 3 (presence only). "Three rescue greyhounds, twice-daily walks, active in local rescue community" is Tier 1 (presence + engagement + sentiment) - without needing a single API connection.

The core signal

Pets are one of the most common dealbreakers in dating. Allergies, lifestyle incompatibility, different care philosophies - these are real friction points that most apps ignore entirely or reduce to a single checkbox.

Affinity Atlas treats pet data as a first-class compatibility signal. How many pets you have, how much time you spend on care, whether you are active in breed communities, whether you foster - these are all engagement signals. And your dealbreakers (allergies, specific animals, no-pet households) are hard filters that prevent bad matches before they happen.

What gets scored

Pet type overlap - both having dogs, both having cats, both having the same unusual pet type (reptiles, rabbits, birds)

Breed specificity - shared breed preferences weighted by how niche the breed is (Labrador = common, Azawakh = extremely niche)

Care routine alignment - walk frequency, feeding philosophy, vet visit regularity as engagement depth signals

Household scale - number of pets, multi-species households, fostering activity

Lifestyle centrality - how important pets are to your daily life (central vs nice-to-have) as a sentiment signal

Dealbreaker filters - allergies, incompatible pet types, no-pet-household preference. These prevent bad matches entirely

Community involvement - rescue volunteering, breed clubs, dog sports, cat shows. Shared community activity is a strong engagement signal

Example match

"You both have rescue dogs (NicheWeight 0.72 for rescue-specific ownership). Both walk twice daily and rate pets as 'central' to lifestyle. Breed overlap: both favour sighthounds (frequency 4% among users - NicheWeight 0.96). Care philosophy alignment: both raw-feed. No dealbreaker conflicts. Community overlap: both active in local rescue networks."

Not "you both like dogs." The actual lifestyle. The actual commitment level. The actual compatibility.

No API needed - and that is the point

There is no "pet data platform" to connect. Instead, Affinity Atlas uses Interest Q&A - a hierarchical questionnaire system that collects structured, scorable data through natural branching questions. The result is the same rich Affinity scoring (presence, engagement, sentiment) that API-connected categories get - achieved through thoughtful UX instead of OAuth.

This is a key part of what makes Affinity Atlas different. Most dating apps treat interests without APIs as flat checkboxes ("pet lover: yes/no"). Affinity Atlas turns them into deep, structured data that genuinely improves match quality. The hierarchical branching means the system collects more data without feeling like a chore - each question flows naturally from the last.

And because the data is structured (not free text), the algorithm can compute precise overlap, niche weights, and engagement depth - just like it does for Spotify listening history or Steam game libraries.

Affinity Atlas is in development

No real matching is live yet. If you want to find someone whose idea of a perfect evening involves a couch, a blanket, and the sound of contented snoring from your dog - get in touch.

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