AI for marketplace apps

Marketplace apps, made smarter without a rewrite.

We add the AI features that move a marketplace, natural-language search, smart listing creation, AI-assisted pricing, and trust and fraud signals, to the app you already ship.

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You have liquidity. Discovery and trust are the ceiling.

Your marketplace works. Buyers and sellers show up, listings get created, money changes hands. The next unit of growth is not more inventory, it is helping the right buyer find the right listing and trusting the person on the other side. That is where AI actually earns its keep: search that understands intent, listings that write themselves, prices that are not a guess, and a moderation layer that scales past what a human queue can read.

The constraint in a marketplace is not the model, it is the two-sided flywheel you already have running. A rebuild risks the liquidity you spent years building. So we do not rebuild. We add AI as a layer beside your listing, search, and checkout flows, wired into the data you already collect. Your catalog, your payments, and your existing users stay exactly where they are.

What we build

Four AI features that earn their place in a marketplace.

These are the patterns we see convert in two-sided products. Each one sits beside your existing listing and search flow, feeding on the catalog and transaction data you already have.

Natural-language search

Search that understands a vague, long-tail query the way a person means it, "cozy vibe restaurants" or "cheap standing desk near me", instead of only matching exact keywords.

What it changes: Buyers stop bouncing off a search box that only rewards the exact word on the listing, and long-tail demand starts finding the long tail of your inventory.
How it is built: Query enrichment with an LLM to expand intent, then semantic matching over listing embeddings, with a staged fallback so the search always returns something usable even when the embedding pass comes up short.

Smart listing creation

Turning a photo and a few words into a full listing: a title, a category, a description, and suggested attributes, so a seller finishes in seconds instead of abandoning halfway.

What it changes: The friction that kills supply drops. More sellers finish more listings, and the catalog gets more consistent because the machine fills the fields the same way every time.
How it is built: A vision-plus-language pass that reads the image and drafts the fields, with the seller reviewing and correcting before anything goes live.

AI-assisted pricing

A defensible price estimate for an item, built from comparable sales and item attributes, so neither side is pricing in the dark.

What it changes: Sellers list closer to a clearing price and buyers trust the number, which tightens the loop between listing and sale.
How it is built: Comparable-sales data plus a weighted valuation model and an LLM to explain the estimate, with the reasoning shown so the number is trusted rather than mysterious.

Trust and fraud signals

A moderation and risk layer that reads listings and messages, flags scams and policy violations, and surfaces the risky few for review instead of the entire firehose.

What it changes: Your trust-and-safety team stops reading everything and starts reading the flagged minority, and buyers meet fewer bad actors before they lose faith in the platform.
How it is built: A classification pass over listing text and images plus messaging, tuned to your policy, feeding a review queue rather than auto-banning on a model guess.

Proof

What we have actually shipped in marketplaces.

We name only work we can stand behind, and we are precise about who delivered it.

The LYVE App

Inseed project, live on both stores

A two-sided marketplace and community for carnival-goers across the US and the Caribbean to buy, sell, and resell event tickets and costumes, with a social layer for events, mas bands, and influencers. Our founder led the project and owned the mobile build end to end.

3,000+ users across iOS and Android
200+ marketplace transactions on a Stripe escrow model
Multi-tier real-time chat: per-carnival, per-event, and influencer channels plus DMs

Read the LYVE case study

Playlists

Delivered by our team via Toptal

A social app for saving and sharing places with an AI vibe search that finds places from natural-language queries like "cozy vibe restaurants". Work our team delivered on Toptal; our founder built the React Native frontend and the Python recommendation engine behind the search.

GPT-4o query enrichment plus text-embedding-3-small semantic search
Staged matching pipeline that always returns usable results
Friend-aware recommendations that fuse two users into one query

How we build semantic search

Artizan

Delivered by Inseed

An AI marketplace and intelligence app for art collectors and investors: browse artists, track market trends, and get AI price valuations for artworks across auctions from 17+ houses. Our founder took the app over at roughly half-built and shipped it to production across mobile and web, and built the AI price-estimation layer.

AI price estimation from image, dimensions, artist, and comparable sales
Weighted valuation models with an explanation screen so the number is trusted
Live on the App Store and Play Store, with a Next.js web app

How we build with React Native

Creators Closet

Inseed project

A two-sided creator resale marketplace: creators list used items from a mobile app, each gets a public storefront at their Instagram-handle subdomain, and buyers check out as guests via Stripe. Built end to end (creator app, storefront, admin) though not launched, as the client stepped away before release.

Per-creator subdomain storefronts resolved by Instagram handle
Guest Stripe checkout with order confirmation emails
One Supabase backend serving mobile, public web, and admin

Straight answer on the gap: our marketplace track record is strong on the platform side (escrow payments, two-sided flows, real-time chat, subdomain tenancy) and real on the AI side (vibe search in Playlists, price valuation in Artizan). We have not yet run an AI moderation layer at high volume in production. The audit is where we prove the specific feature on your marketplace, in a throwaway prototype, before you commit to a build.

Borrowed proof, labeled honestly

React Native holds up under marketplace load.

React Native runs in production at Mercari, Shopify, and Coinbase, marketplaces and commerce apps moving real money at scale. These are not our clients. They are proof that the framework we build on carries a high-traffic transactional product, so the question for yours is the feature, not the foundation.

How it starts

It starts with an audit, not a rebuild.

We do not open with a six-figure proposal. We open with one week of work that tells you exactly where AI fits in your marketplace, what it costs to run, and whether it is worth building at all.

Step 1

Mobile App Audit

$5K to $8K

1 week

A written assessment of where AI fits in your app, a working proof-of-concept of the top feature, and a cost model you can take to your board. The fee credits toward a Sprint signed within 30 days, or is refunded if the report is not useful.

Step 2

AI Integration Sprint

$15K to $30K

2 to 4 weeks

One AI feature shipped into your existing app, as a thin integration layer with fallbacks, caching, and guardrails. Your architecture stays yours.

Step 3

AI-Native Mobile MVP

$25K to $75K

6 to 12 weeks

A production-ready AI-native mobile MVP when the answer is a new product rather than a feature. Fixed price, fixed milestones, both stores.

See full pricing

FAQ

Marketplace questions, answered straight.

Will AI search work without perfect listing data?

Yes, and that is the point of designing it in stages. In Playlists the search enriches a vague query with an LLM, then runs semantic matching, then falls back through progressively looser passes so it always returns something usable. Messy real-world listings are exactly why a single embedding lookup is not enough on its own.

Can you add search without ripping out our current search?

Yes. Semantic search sits beside your existing keyword search rather than replacing it. A common pattern is to route long-tail natural-language queries to the AI path and keep exact-match queries on your current index, so you improve the hard cases without risking the ones that already work.

How do you keep AI pricing from being a black box?

By grounding it in comparable sales and showing the reasoning. In Artizan the estimate is built from comparable-sales data and a weighted valuation model, with a transparency screen that explains how the number was reached. A price nobody trusts is worse than no price, so the explanation is part of the feature, not an afterthought.

Can smart listing creation handle our categories?

It adapts to your taxonomy. The vision-plus-language pass drafts the title, category, and attributes against your category tree, and the seller reviews before publishing. You keep control of the schema; the model just fills it faster and more consistently than a person typing on a phone.

Does AI moderation replace our trust-and-safety team?

No, it feeds them. We build a classification layer that flags risky listings and messages for review rather than auto-banning on a model guess. Your team stops reading the entire firehose and starts reading the flagged minority. Humans keep the final call on anything consequential.

Is React Native robust enough for a payments-heavy marketplace?

Yes, and we can point at live evidence. LYVE runs a Stripe escrow marketplace with multi-tier chat in production. Artizan runs Stripe subscriptions across mobile and web. Beyond our work, Mercari, Shopify, and Coinbase run React Native on high-traffic transactional apps.

Ready to add AI to your marketplace app?

30 minutes, free. We will look at your app and tell you honestly where AI fits, what it costs, and what we would build first. Shuhel will be on the call.

Book a 30-minute call