What AI does
AI helps discover, organize, compare, and summarize a curated set of expert reviews, product-specific videos, owner discussions, and official specifications. It surfaces repeated praise, complaints, and disagreements for review.
Phavai / Methodology
How Phavai Uses AI to Research Products
Shoppers should not have to compare a dozen review pages, videos, owner threads, and product specs just to buy one thing. AI helps Phavai organize selected public evidence; a human editor reviews the synthesis, makes the editorial decision, and approves what gets published.
AI saves the research time. A human owns the recommendation.
The system is designed to make scattered public evidence easier to compare—not to manufacture experience or replace accountability.
What the human does
Ben Yardley reviews source-to-product matches and important claims, removes or demotes weak evidence, applies category context, and approves the final recommendation. Running and outdoor coverage also draws on his category experience; other categories rely more heavily on cited specialist evidence.
What this is not
This is a curated evidence sample, not every opinion on the internet. AI does not create first-hand experience, and Phavai does not claim hands-on testing unless a guide explicitly documents it.
1. Start with the buying problem
A good pick depends on the shopper and the category. Trail shoes need fit and terrain context. Office chairs need long-day comfort. Coffee makers need consistency and cleaning reality. The guide starts with that buying problem.
2. Use category-specific sources
A specialist trail-running lab does not automatically become a trusted office-chair source. We weigh sources by how much they know about the category, how clearly they test the product, and whether the link helps a shopper understand the choice.
3. Put the best evidence first
When available, category-relevant expert reviews set the strongest baseline. Product-specific video reviews can show real use, setup, fit, and annoyances. Owner feedback can catch recurring praise, buyer regret, durability complaints, and support problems. Coverage varies by product, so a missing channel is treated as a limitation rather than implied evidence.
Channel formula: sum(evidence score x evidence quality weight) / sum(evidence quality weights).
4. Keep public sources curated
Buying guides aim to show a compact set of exact, buyer-helpful public sources for each product. The number and mix vary, and some older or developing source sets remain thinner than the target. Broader discovery notes can remain internal, but they are not presented as public evidence until an exact useful URL is available.
5. Calculate the pick score
The default weighting is Expert 40%, Creator 30%, and Owner Feedback 30%. If a channel is missing, available channels are normalized rather than padded with a guess.
BestPick formula: sum(channel score x category channel weight) / sum(available category channel weights).
6. Show whether the proof lines up
AI helps surface whether experts, creators, and owners are pointing in the same direction; the human editor checks those patterns against the sources. A product can score well but still deserve caution if owners repeatedly flag fit, durability, or support issues.
7. Keep weak proof from looking stronger than it is
We look at source count, source diversity, freshness, reviewer quality, sample strength, and channel coverage. Not every guide has reached the same evidence depth yet. Thin evidence should be disclosed and improved over time instead of being hidden behind a clean score.
8. Match the shortlist to the buyer
Buying guides let readers choose practical priorities such as fit, comfort, value, or durability. The fit finder explains which ranked product best matches those needs without rewriting the editorial score or its evidence.
9. Leave noisy social data out for now
Broad social media can be useful, but it is noisy and hard to audit cleanly. Phavai leaves it out until collection is reproducible enough to deserve influence.
10. Keep retail links separate
Retail links may point to Amazon, brand stores, or other retailers for convenience. They do not affect pick score, source quality, ranking order, or product conclusions.
What makes a recommendation fail
A recommendation fails if it hides meaningful tradeoffs, uses unverifiable evidence, overstates certainty, or ranks a product because the link is more convenient. Trust is the product.
Source coverage is an active editorial backlog. Readers can flag a missing, mismatched, or stronger source through the corrections process.