AI Shopping Assistants Compared (2026)

Two of the most-used fake review checkers are gone. Fakespot shut down on July 1, 2025, stranding more than 10 million users after Mozilla concluded the product "didn't fit a model we could sustain." ReviewMeta quietly went dark in early 2026 with no public explanation. The Chrome extension stopped receiving updates and the site appears unmaintained. For shoppers who relied on either tool, the landscape looks very different now than it did eighteen months ago.

That vacuum arrived at a particularly bad time. Fake reviews cost consumers an estimated $770.7 billion worldwide in 2025 — roughly $125 per shopper per year wasted on products that didn't live up to their inflated ratings, according to Capital One Shopping Research. The tools designed to catch those reviews have never mattered more, which makes it worth taking a clear-eyed look at what's actually available, what each one does well, and where each one falls short.

The Landscape Right Now

The category has consolidated fast. A handful of tools have survived or emerged to fill the space: BuyWise, Honey (now operating under PayPal despite serious reputational damage), Keepa, and a few newer entrants including FakeFind and SureVett. Each solves a slightly different problem. Grouping them all as "shopping assistants" flattens meaningful differences in what they actually do.

There's also a structural issue worth naming upfront. A 2025 study from researchers at Princeton University and the University of Washington tested 23 large language models on scenarios where a sponsored product was more expensive or inferior to a better alternative. Fifteen of the 23 models recommended the pricier sponsored option more than half the time. That finding should inform how much you trust any AI shopping tool that has monetization tied to recommendations.

Fakespot and ReviewMeta: What Was Lost

It's worth briefly acknowledging what these tools did before they disappeared, because their absence shapes what buyers now need from replacements.

Fakespot graded Amazon listings A through F based on review pattern analysis. It flagged unusual bursts of reviews, suspicious reviewer profiles, and rating distributions that didn't match organic purchasing behavior. ReviewMeta took a similar approach but published more granular methodology, letting users see exactly which signals triggered a flag. Both tools were browser extensions — zero friction, no extra tabs required.

Neither tool survived long enough to adapt to the FTC's Consumer Reviews Rule, which took effect October 21, 2024 and prohibits fake reviews, insider reviews, and suppression of negative feedback. The FTC sent warning letters to 10 companies on December 22, 2025 — its first public enforcement action under the rule, with civil penalties of up to $53,088 per violation. Enforcement pressure may clean up some listings over time, but it won't eliminate the problem, and it doesn't replace the tools shoppers used to detect manipulation themselves.

Honey: Useful for Price, Problematic for Trust

Honey still does one thing well: it finds coupon codes at checkout. If a working discount exists for a retailer, Honey's database usually has it. The extension covers hundreds of stores and the automatic application at checkout is genuinely convenient.

The trust problem is harder to overlook. A class action lawsuit filed December 29, 2024, alleged that Honey systematically diverted affiliate commissions from content creators by manipulating tracking cookies — a practice known as cookie stuffing. A federal judge dismissed the suit on November 21, 2025, but granted plaintiffs 45 days to amend, and a second amended complaint was refiled January 5, 2026. The case remains active.

The user impact was measurable. Chrome Web Store data showed Honey declining from over 20 million users before the December 2024 investigation to 14 million by July 2025. The core issue wasn't just legal exposure — it was that the extension's business model created incentives misaligned with the shopper's interests. For price-cutting, Honey remains functional. For anything requiring trust in its recommendations, the situation is unresolved.

Keepa: Price History, Nothing More

Keepa tracks Amazon price history with precision. It shows you whether the "40% off" sale you're looking at represents a genuine discount or a manufactured markdown from an inflated reference price — a common tactic that the FTC has targeted in other enforcement actions.

What Keepa doesn't do: analyze reviews, flag suspicious listings, or evaluate product quality signals. It's a single-purpose tool, and within that purpose, it's reliable. Keepa Basic is free. Keepa Premium runs €19/year and unlocks API access and additional data layers. For Amazon shoppers who want honest price context, Keepa is worth having regardless of what else you use.

FakeFind and SureVett: Newer Entrants

Several tools moved quickly to fill the Fakespot gap. FakeFind and SureVett are the most visible, and both offer review analysis with browser extensions. Neither has the track record or user base of the tools they're replacing, which makes them harder to evaluate objectively.

FakeFind offers a free tier with limited analyses per month and covers Amazon and a small number of additional retailers. SureVett's methodology is less transparent than FakeFind's, with limited public documentation on how its scoring works. Both are early-stage enough that recommending either with confidence isn't possible yet. Keep them on the watchlist.

BuyWise: What It Does Well and Where It Falls Short

BuyWise grades Amazon listings using a combination of review pattern analysis, rating distribution signals, and listing-level flags. The output is an A-through-D grade with a plain-language summary of what triggered the score. Across 50 BuyWise analyses in our current dataset, 32% of products graded D — and 16 of those 50 showed high suspicious review signals. That matches broader estimates of fake review prevalence across major platforms.

The red flags the model catches are consistent with known manipulation patterns. BuyWise recently flagged a listing where 92% of reviews were either 1-star or 5-star with an average of 4.9 — a bimodal distribution that organic purchasing behavior rarely produces. Another analysis caught a listing with 100% five-star reviews across only five total ratings, exactly the kind of thin, manufactured consensus that inflated products use to appear credible before accumulating real reviews.

Honest weaknesses:

• Retailer support is currently Amazon-heavy. Walmart coverage exists but is limited, and other major platforms aren't supported yet.
• BuyWise doesn't offer price history tracking — for that, pair it with Keepa.
• The tool analyzes review patterns and listing signals, not product quality directly. A listing with clean reviews can still contain a mediocre product.
• The extension is free, which means it has to sustain itself somehow. BuyWise's current model doesn't tie recommendations to affiliate commissions, but that's worth watching as the product matures.

The FTC's review rule creates a tailwind for tools like BuyWise — enforcement pressure may reduce the volume of fake reviews over time, but it won't eliminate them, and detection tools will continue to matter as manipulation tactics evolve alongside policy.

Side-by-Side Summary

Here's how the current field stacks up across the dimensions that matter most:

What to Actually Use

No single tool covers everything. The most practical combination for most Amazon shoppers right now: BuyWise for review credibility and listing flags, Keepa for price history and markdown verification. Neither requires a subscription. Both install in under a minute. Together, they replace the core functions that Fakespot and ReviewMeta provided.

Honey is defensible if you shop across a wide range of retailers and value automatic coupon application — just go in aware that its business model created documented conflicts of interest, and the litigation is unresolved. Don't rely on it for product quality signals it was never designed to provide.

For the newer entrants, FakeFind and SureVett are worth watching but not yet worth recommending broadly. Give them six months of real-world user data before treating their scores as reliable.

One broader point: Adobe measured a 4,700% year-over-year increase in shoppers using AI to find products between 2024 and 2025. That growth is real, and it's driving significant investment in AI shopping tools. But investment doesn't equal accuracy, and scale doesn't equal trustworthiness. The Princeton and University of Washington research makes that plain. The best defense is still a combination of tools with transparent methodology, no affiliate conflicts, and a track record you can actually check.