PawPlanr: AI Dog Breed Matcher

Problem Statement

First-time dog owners and families struggle to find breeds that genuinely match their lifestyle, living space, and activity level. Most breed-finder tools are simple filter dropdowns that don't account for nuanced personal factors — resulting in poor matches and, too often, surrendered pets.

Solution

PawPlanr lets users either take a guided lifestyle quiz or describe themselves in plain language. An AI model analyzes their inputs and returns ranked breed matches with trait-by-trait side-by-side comparisons — making the decision informed and personal.

Our Approach

We mapped the qualitative signals that predict a good owner-breed match — living space, activity level, allergies, work schedule, and household composition — then built two intake paths into one matching pipeline: a structured multi-step quiz for users who want guidance, and a free-text field for users who'd rather describe their life in their own words. Both paths feed the same AI scoring engine, which ranks every breed in the dataset against the user's inputs and returns confidence-weighted matches instead of a single best guess.

Key Features

• •Dual intake modes — guided quiz or free-text lifestyle description — feeding one matching engine
• •AI-generated ranked breed matches with trait-by-trait side-by-side comparison cards
• •Lifestyle-aware scoring that weighs space, activity level, and experience instead of static filters

Results

PawPlanr shipped as a fully working MVP in weeks rather than months, validating that natural-language lifestyle descriptions could be parsed into structured breed-matching criteria without a bespoke NLP model. The side-by-side comparison pattern became a reusable template we carried into later quiz-style builds in the portfolio.

Challenges

The hardest part wasn't the AI call — it was making the matching feel trustworthy. Early prototypes returned confident-sounding matches for ambiguous inputs, so we added short reasoning snippets to each result explaining why a breed was suggested, which cut the 'black box' feeling and made comparisons easier to act on.

Lessons Learned

Free-text and structured-quiz inputs need separate prompt strategies even when they feed the same model — treating them identically produced noticeably weaker matches for quiz users, who expect their answers weighted more literally.

Tech Stack

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