AI Shopping Funding Tracker (89 deals)

Our funding tracker covering the AI shopping market

Below is the table listing all the deals. You may find our methodology at the end of this page.

And, if you want to gain a deeper understanding of the market and its current dynamics, get our report covering the AI Shopping Market.

AI shopping funding: what the market actually looks like today

Is AI shopping actually a big funding market right now?

AI shopping is already a real venture market today, with 89 financings across 79 companies and at least $678.4 million of documented capital.

The scale becomes easier to understand once we look past that headline. We found a median round of $3.77 million, compared with a $7.71 million average. The typical startup is operating well below that market-wide average.

There is plenty of activity, though. Across the 32 complete months we tracked, investors funded an average of 2.72 deals a month. The market now spans consumer shopping agents, product search, recommendations, merchandising software and commerce decision tools.

What AI shopping still lacks is a deep population of heavily funded companies. We found no $100 million-plus round and very little funding beyond Series B. A meaningful startup ecosystem already exists, while the scale-up layer remains quite small.

How many AI shopping startups are actually getting funded?

AI shopping funding is spread across a surprisingly broad group of companies, but most startups only show up once.

We tracked 79 companies in total. Seventy of them, or 88.6%, had a single financing during the period. Eight appeared twice and only one company appeared three or more times.

That leaves a repeat-fundraising rate of just 11.4%. The market is much wider than it is deep: investors are willing to finance many different ideas, yet only a small group has come back for another round inside the period we studied.

Those repeat fundraisers still punch above their weight. Nine companies with multiple financings captured $199 million, or 29.3% of all documented capital. Getting funded once is common enough here. Building a visible multi-round financing history is much rarer.

Is AI shopping funding concentrated in a few companies?

Yes. AI shopping funding is fairly concentrated once we stop counting deals and look at how much each company has actually raised.

The ten best-funded companies collected 53.1% of the market's documented capital while representing only 12.7% of funded companies. FERMÀT leads with $62 million across two rounds, followed by Gensmo with more than $60 million, Daydream with $50 million and Phia with $43 million.

No single company controls the market. FERMÀT represents 9.1% of total funding, so AI shopping is far less dependent on one giant company than some other AI categories.

The long tail is where the contrast becomes obvious. Some 60.8% of funded companies raised no more than $5 million cumulatively. Serious capital exists, but it sits with a fairly short group at the top.

Company	Financings	Capital raised in the period
FERMÀT	2	$62.0M
Gensmo	1	≥$60.0M
Daydream	1	$50.0M
Phia	2	$43.0M
Cimulate	1	$28.0M
Remark	2	$26.3M
Constructor	1	$25.0M
Vizit	1	$25.0M
Spangle	2	$21.0M
Lily AI	1	$20.0M

What does a normal AI shopping funding round look like today?

A normal AI shopping round today is much closer to $3 million or $4 million than to the $7.71 million market average.

More than six in ten known financings were below $5 million. Those 54 rounds generated only 16.9% of the market's capital, so small checks dominate the number of announcements without dominating the money.

The $5 million to $20 million range is where the biggest pool of capital sits. We counted 26 deals there, responsible for $275.9 million and 40.7% of all documented funding. A further six rounds between $20 million and $50 million contributed $178 million.

For a founder trying to benchmark a realistic raise, the median is more useful than the average. A $10 million round is already clearly above the norm in this market. A $20 million-plus round puts a company into a very small group.

Round size	Deals	Share of known deals	Capital	Share of capital
Below $5M	54	61.4%	$114.5M	16.9%
$5M to below $20M	26	29.5%	$275.9M	40.7%
$20M to below $50M	6	6.8%	$178.0M	26.2%
$50M to below $100M	2	2.3%	$110.0M	16.2%
$100M+	0	0%	$0	0%

Are giant AI shopping seed rounds distorting the market?

A few huge Seed rounds push AI shopping averages upward, although the market does not collapse once we remove them.

Gensmo is the most extreme case. The company disclosed more than $60 million of angel and Seed funding after launching its fashion AI product, making the exact market total impossible to pin down: we conservatively count the round as $60 million, so the $678.4 million market total is a minimum.

Daydream is almost as striking. It raised $50 million before launching its consumer product publicly, backed by Forerunner Ventures, Index Ventures, GV and True Ventures. The company was building a large fashion search and discovery platform from day one, combining generative AI, machine learning, computer vision and a substantial branded product catalog.

Those deals show why “Seed” can be misleading in AI shopping. The label covers everything from sub-$1 million experiments to companies starting with infrastructure, senior teams and tens of millions of dollars.

Still, only one known round is strictly above $50 million and none reaches $100 million. Removing Gensmo's conservatively recorded $60 million leaves at least $618.4 million behind. Large Seeds distort what an ordinary financing looks like; they do not create the whole market by themselves.

Which parts of AI shopping are getting the most money?

AI Shopping Assistants have raised the most money overall, while AI Product Search stands out for attracting much bigger checks relative to its number of deals.

Shopping Assistants account for $185.8 million and AI Merchandising Tools for $182.6 million. Product Search sits just behind at $175.1 million, despite having only 16 financings versus 24 for Shopping Assistants and 25 for Merchandising.

That gives Product Search the highest funding intensity in the market. Its 18% share of deals attracted 25.8% of the dollars, and its average round reached $10.95 million. Product Advice Engines sit at the other end, with eight financings, $36.9 million of capital and a $1.98 million median.

The larger checks increasingly sit close to the underlying commerce stack: product data, search, catalog intelligence, merchandising and personalization. The lighter advice layer still gets funded, but usually with less money behind it.

AI shopping category	Deals	Capital	Median round	Funding intensity
AI Shopping Assistants	24	$185.8M	$3.30M	1.02×
AI Merchandising Tools	25	$182.6M	$4.00M	0.96×
AI Product Search	16	$175.1M	$4.66M	1.44×
Commerce Decision Support	8	$55.0M	$3.48M	0.90×
AI Recommendations	8	$43.0M	$3.53M	0.71×
Product Advice Engines	8	$36.9M	$1.98M	0.60×

Why is AI product search attracting bigger funding rounds?

AI product search is getting bigger checks because a serious shopping search product now needs much more than a conversational interface.

Daydream shows how quickly that complexity builds. The company started with a $50 million Seed to create an AI-native fashion search platform. Lately, it has been serving more than 1.5 million shoppers across roughly three million products and 10,000 brands, while expanding discovery into visual search and on-device experiences.

Channel3 offers another angle. When it announced its $6 million Seed, the company said its product graph already covered more than 50 million products across tens of thousands of brands. The job involves keeping prices, variants, availability and product attributes usable by AI agents in real time.

Shopify's latest commerce infrastructure makes the same point at much larger scale. The company says AI-driven traffic to Shopify stores grew eightfold year over year in Q1 2026, while orders coming from AI-powered searches rose nearly thirteenfold. Shopify also reports that searches powered by its structured Catalog convert at twice the rate of searches relying on scraped product data.

Product search is increasingly a data problem as much as an interface problem. Companies have to understand vague human intent, structure huge catalogs, rank products well, keep commercial information current and increasingly make that data readable by external AI agents. That workload helps explain why Product Search supports larger rounds than simpler recommendation or advice products.

Is AI merchandising where AI shopping startups are actually scaling?

AI merchandising currently has one of the clearest paths from small rounds to real scale-up funding.

Among 25 merchandising financings, we found 18 Seed rounds, five Series A rounds and two Series B rounds. That progression is unusual in this market.

FERMÀT is the best example. It raised $17 million at Series A and then $45 million at Series B, taking its financing inside our period to $62 million. The company has expanded from personalized shopping journeys into behavioral commerce data, AI search, merchandising and tools that expose commerce data to AI systems.

Vizit's $25 million Series B gives the category another scaled company, while firms such as ReFiBuy are now raising larger Seeds to help brands prepare product catalogs for AI-driven discovery. ReFiBuy raised $13.6 million after a much smaller initial financing and has lately extended its product-data infrastructure into Claude-based commerce agents.

There is a fairly practical reason investors can put more capital to work here: these products sell into brands and retailers with existing commerce budgets. The customer and the ROI can be easier to identify than for a new consumer shopping app that still has to build distribution from scratch.

How early-stage is AI shopping funding right now?

AI shopping is still overwhelmingly an early-stage funding market, with 95.2% of classified rounds sitting at Seed or Series A.

We counted 66 Seed deals, 14 Series A deals and only four Series B deals. Five additional financings could not be assigned a reliable stage from public information.

Round size changes sharply once a company moves up the ladder. The median Seed is $2.72 million, compared with $15 million at Series A and $25 million at Series B. Investors are clearly prepared to step up the check once a company has moved beyond the initial experiment.

The scarcity sits in the number of companies reaching those stages. Seed is crowded. Series A is much thinner. Series B is a tiny club.

That pattern fits what we see commercially these days. AI shopping products are being launched quickly, while proving durable distribution, retailer adoption or repeatable revenue takes longer.

Where does AI shopping funding get stuck?

AI shopping funding gets stuck between Seed and Series A, and the pipeline becomes extremely thin beyond Series B.

There are 4.7 Seed financings for every Series A in our research. The next step narrows again, with 3.5 Series A financings for every Series B.

Phia shows what it takes to move through that filter unusually fast. The shopping app raised an $8 million Seed and followed it with a $35 million Series A only a few months later. By the Series A announcement, Phia said it had passed one million users, worked with more than 6,200 brands and grown revenue elevenfold since launch.

That kind of jump helps explain the gap between the $2.72 million median Seed and $15 million median Series A. The companies that clear the bar can get a dramatically larger check.

After Series B, our market effectively runs out of visible funding stages. We found no Series C, Series D or Growth Equity deal. AI Product Search and AI Merchandising Tools are the only categories that reach Series B at all.

Plenty of companies can get an initial AI shopping idea financed. Far fewer have yet shown the traction required to keep climbing.

Are investors putting more money into AI shopping companies they already know?

Yes. AI shopping investors are putting substantially more capital into companies that have already raised before.

Follow-on financings represent 59.6% of deals and $452.4 million of capital. First financings account for $223.6 million.

The size difference is also clear at the median: $4.19 million for a follow-on versus $2 million for a first financing.

We still found 35 first financings, so new AI shopping startups continue to enter the market at a healthy pace. The larger checks increasingly land after somebody has already financed the company once.

That is a normal venture pattern, but it is useful here because the headline AI boom can make every new startup look equally fundable. The financing record says otherwise. Investors will fund experimentation broadly; larger commitments are much more selective.

Is AI shopping funding mostly a US story?

AI shopping startup formation is global, but the big checks are still overwhelmingly concentrated in North America.

North America produced 46 financings and $537 million of capital, or 79.2% of the market total. The United States alone accounts for 45 deals, 38 companies and $534.5 million.

Europe has a meaningful startup base with 24 deals, yet those deals add up to $87.9 million. Asia-Pacific recorded 12 financings and $35.7 million. Their median rounds are also much smaller: $2.84 million in Europe and $1.38 million in Asia-Pacific versus $7.5 million in North America.

No region outside North America produced a round above $50 million in our research. Europe and Asia are clearly building AI shopping companies; for now, they are doing it with much smaller amounts of venture capital.

Region	Deals	Capital	Share of capital	Median round
North America	46	$537.0M	79.2%	$7.50M
Europe	24	$87.9M	13.0%	$2.84M
Asia-Pacific	12	$35.7M	5.3%	$1.38M
Middle East	5	$14.9M	2.2%	$2.00M
Latin America	2	$3.0M	0.4%	$1.48M

Who keeps investing in AI shopping startups?

AI shopping has attracted plenty of recognizable investors, but almost nobody keeps leading deals again and again.

Lead-investor information is available for 77 of the 89 financings. Across them, we identified 92 distinct lead or co-lead names. Only NewRoad Capital Partners and Galion.exe show up as a lead on more than one deal.

That is an unusually scattered investor base. Forerunner and Index backed Daydream, Bain Capital Ventures led FERMÀT's Series A, VMG Partners led its Series B, Spark Capital led Cimulate, Sapphire Ventures backed Constructor, Notable Capital led Phia's Series A and Bessemer co-led Malachyte. The list gets long quickly.

Corporate investors are present without becoming the core source of funding. We found strategic investors in 10 deals, or 11.2% of the market. Those rounds had a $9 million median, compared with $3.28 million without a strategic investor. Stripe appears in both Remark financings, while other examples include GV, eBay Ventures, Rakuten Capital and Stadium.

The corporate interest around AI shopping has also become more concrete lately. Shopify and Google are pushing the Universal Commerce Protocol, while Mastercard has launched Agent Connect to link merchants, agents and payment providers across discovery, recommendation and checkout. Large commerce companies are building around the same shift these startups are chasing, even though they are not repeatedly underwriting the startup market themselves.

Does AI shopping funding arrive steadily or in bursts?

AI shopping funding is lumpy today, with a few busy or expensive months sitting far above the normal pace.

Across 32 complete months, we counted an average of 2.72 deals per month and a median of two. The busiest month reached seven deals, or 3.5 times the median.

Capital swings even more sharply. The average complete month brought $21.1 million of funding and the median $13.6 million, while the strongest month reached $116.35 million.

That $116.35 million month came from only four financings. Gensmo's conservatively recorded $60 million and FERMÀT's $45 million Series B generated roughly 90% of it.

The busiest month by deal count was a different month altogether. Funding activity and dollars move on separate rhythms: one or two large rounds can completely change the amount of capital raised without creating a broader surge in startup activity.

For anyone following AI shopping through funding headlines, that distinction is worth keeping in mind. A huge monthly total can sometimes mean two companies had a very good month.

What should we actually take away from the funding data?

AI shopping currently looks like a broad early-stage market with a much smaller group of companies beginning to pull away.

We found 79 funded companies, so there is already substantial experimentation across search, agents, recommendations, merchandising and commerce infrastructure. Most of those companies have only appeared once in the financing record, which tells us that the market has created breadth faster than financing depth.

A normal startup here is raising a few million dollars. The companies shaping the headline funding totals are operating on another scale, with $20 million to $60 million rounds concentrated around a relatively short list of names.

Search and merchandising deserve particular attention. Their products increasingly sit underneath AI commerce itself: structured product data, catalog intelligence, ranking, personalization, behavioral data and infrastructure that lets agents understand what can actually be bought. Recent moves from Shopify, Google, Mastercard, Daydream, Channel3 and ReFiBuy make that shift much easier to see today than it was even a year ago.

The hardest step remains turning an initial funded product into something that deserves a Series A. Companies that clear that bar can see their financing jump dramatically, while the market still has almost no visible late-stage layer.

Geography remains another major split. North America takes close to four-fifths of the capital, with far larger checks than Europe or Asia-Pacific.

Taken together, the market is already substantial enough to matter, young enough for its category leaders to change quickly, and selective enough that the overall funding number should never be confused with what an ordinary AI shopping startup can realistically raise.