Structured decision-making without the research rabbit hole
Built independently
Context
Most non-trivial purchase decisions follow the same pattern. You start with a question, open a number of tabs, read conflicting reviews, compare specifications across different sites, then either make a quick decision or defer it altogether.
Search is not the problem. Finding information is easy. The difficulty is turning that information into something structured and useful.
Comparia focuses on that step. You describe the decision, define what matters and receive a structured comparison with a recommendation and clear reasoning.
Problem
Research is fragmented. Information is spread across review sites, forums, manufacturer specifications and retail listings. Each source presents data differently and with its own bias.
Opinions conflict. One reviewer prioritises gaming performance, another focuses on colour accuracy. There is no shared framework, so comparisons are difficult to trust.
Decisions become harder over time. The more options you consider, the less certain the outcome feels.
Most tools are not personalised. Rankings are based on general criteria rather than what the individual actually cares about.
Approach
Three-step interaction
The flow is deliberately simple:
- Describe the decision in natural language
- Set priorities using relative importance
- Receive a scored comparison with a recommendation
Each option is scored against the chosen criteria. Scores are weighted by the user’s priorities and combined into a final ranking.
Transparent reasoning
Every score includes an explanation. The recommendation shows how it was derived.
This was a deliberate choice. Opaque recommendations are difficult to trust. Showing the reasoning allows users to challenge or adjust the result.
Pre-built comparisons
Alongside the custom flow, the product includes curated comparisons across common categories such as televisions, laptops and smartphones.
These provide immediate value and demonstrate how the system works without requiring input.
Technical decisions
SvelteKit frontend, Supabase for authentication and data and OpenAI for analysis. Deployed on Vercel with the marketing site and application on separate subdomains.
Authentication uses the PKCE flow with HTTP-only cookies.
Monetisation
The product is free to use. Revenue comes from affiliate links to retailers.
Affiliate relationships do not influence scores or rankings and this is stated clearly.
Tradeoffs
AI-dependent output
Recommendation quality depends on the underlying model and available data.
For well-documented consumer products this works well. For niche or recently released products, the system may have limited information. In those cases, uncertainty is surfaced rather than hidden.
Free-only model
There is no paid tier. Revenue relies on affiliate conversion.
This keeps the product accessible but introduces a dependency on user behaviour. If that does not scale, the model would need to change.
UK-first content
Pre-built comparisons focus on UK pricing and availability.
The core system works internationally, but the editorial layer is intentionally localised.
Simplicity over control
The interaction model limits how much users can configure.
More advanced controls could be added, but the goal is to provide an answer quickly rather than replicate a spreadsheet.
Outcome
The product is live and used for decisions across consumer electronics, education, career choices and property.
The comparison library drives organic traffic and allows users to understand the system without signing up.
The most consistent feedback relates to transparency. Users trust the output because they can see how each recommendation is constructed.