Opportunities › Multimodal search and discovery for ecommerce › Proposed app
Findwell
Text and photo product search you can add to your shop in days
- Score
- 62
- confidence 48
- Evidence
- 8
- items on the page
- Rivals
- 0
- named in sources
- First version
- 35 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2019
- Established market · AI estimate
ScreensConcept
Desktop app: Engineers set up feeds, compare search results side-by-side, review zero-result reports, and configure ranking rules—all dense, keyboard-heavy work. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Catalog
Index healthy12,480
About this appAI proposal
Findwell is a hosted search layer for online shops. Connect your product feed, and shoppers can search by typing, by uploading a photo, or by mixing both, such as a picture of a lamp plus the words "in brass". Results are ranked by meaning, not only by matching keywords.
It is made for the engineers and operators who run search for a store of any size and do not want to build a vector pipeline from scratch. You get a drop-in search box, an API, and a dashboard showing which queries return nothing useful.
Unlike keyword search with filters, Findwell reads product images and text together. Unlike a large commercial suite, you can see how results are ranked, tune them with simple rules, and start small.
- Tagline
- Let shoppers search the way they think.
- Voice
- ClearPracticalCalm
- Why this name
- Plain words for a plain promise: shoppers find things well, and the search works well for the team running it.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Search by text, photo, or both
Shoppers type, upload an image, or combine them. One query returns products ranked on both the picture and the description.
No member yet
Feed import in minutes
Point to a CSV, JSON or product feed URL. Images and text are indexed automatically and re-synced on a schedule.
No member yet
Drop-in search box and API
Add a script-based search widget or call the REST API from your own storefront code.
No member yet
Zero-result report
See the queries that found nothing useful, grouped by theme, so you know which products or synonyms are missing.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#0F4C4ADeep Teal · primary#FAF6EEWarm Paper · surface#1B2422Ink · text#E8A317Saffron · accent#8FB5A5Sage · data
Typography
Aa
Findwell
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Let shoppers search the way they think.
PricingAI proposal
The material gives no competitor prices. These tiers, based on catalog size and monthly searches as the opportunity suggests, are a starting point to test.
Free
Free
- Up to 1,000 products
- 5,000 searches a month
- Search API and widget
Shop
$49 a month
- Up to 25,000 products
- 100,000 searches a month
- Zero-result report
- Ranking rules
Growth
$199 a month
- Up to 250,000 products
- 1M searches a month
- Side-by-side test
- Priority support
Would you pay Shop · $49 a month?
Your answer tells PWAS which prices people accept. It changes no score.
No answers yet.
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$194,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
What products in this market make
- AlgoliaEstimate$20,833,333 a month, worth about $404,483,000 at that multiple · sacra.com
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- Visible inside customers’ products. The search box runs on customers' storefronts. A small 'Search by Findwell' note on the free tier is shown to shoppers and to engineers who inspect the site.
- Do they become customers?
- Shoppers are not buyers, but shop engineers and operators who see it might be. Test whether free-tier badge clicks lead to sign-ups from other shops.
- First customers
- Engineers at independent online retailers with 5,000 to 50,000 products in visual categories like home goods or fashion, who run their own storefront.
- Where to reach them
- GitHub and dev communities: Publish a benchmark and a how-to on multimodal search, linked to a free trial with a feed import.
- Direct outreach: Email shop engineers with a side-by-side demo built on their public catalog.
- First milestone
- Five shops each run a side-by-side test, and three of them pay for a plan within 60 days.
Why people already pay for this
- VerifiedGitHub repository gained 5028 stars in 1538 days
- EstimateAlgolia: $250M ARR (July 2026), up from $230M at end of 2025 (July 2026)
- EstimateAlgolia: ~$334M total; $2.25B valuation at $150M Series D (July 2021) (2021 / 2026)
- Public claimConstructor: Revenue doubled for third year in a row (2024)
- Public claim
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · Vector searchPostgres pgvector or a managed vector indexStores embeddings for products and queries and returns nearest matches fast.
- This app · Embedding modelHosted or open-source multimodal embedding modelTurns product images, text and shopper photos into comparable vectors.
RisksAI inference
- TechnicalSearch must answer in well under a second for shop use. Image embedding at catalog scale and keeping the index fresh could raise latency and cost.
- CommercialTeams may prefer building on open-source tools they already know, and see little reason to pay for a hosted layer.
- AIVisual similarity can return look-alikes that are wrong for the shopper, such as the wrong size or material. Ranking rules and testing must catch this.
- DataProduct feeds are messy: missing images, bad titles, duplicates. Poor input will produce poor results and blame on Findwell.
- PlatformPlugins for major shop platforms may be needed for adoption, and each platform's review rules and API limits add work.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.