Personalized feeds, built and deployed in minutes
Build your recommendation algorithm for personalized onchain asset recommendation. Serve personalized feeds via a single API call. 250ms inference.
Generic feeds are killing your engagement
One-size-fits-all feeds don't convert
Every user sees the same content regardless of their interests, trading history, or risk profile. Most of it is irrelevant noise.
Building recs in-house takes years
ML pipelines, feature stores, model training, A/B testing infrastructure. Most teams don't have the data science headcount or the onchain training data.
Cold start kills new user retention
New users see generic content, get no personalization, and churn before your system learns anything about them. By then it's too late.
Turn passive browsing into active engagement
2.5x engagement lift
Personalized feeds surface the content each user actually cares about. More relevant items = more clicks, more swaps, more time in app.
A feed worth coming back to
When every session shows fresh, relevant content tailored to their interests, users build a daily habit. Generic explore pages don't create that.
Discovery to Fees
Every feed item is a CTA. Users discover tokens, markets, and content they wouldn't have found, and act on it. Direct path to transaction revenue.
From idea to production feed in minutes
Build your recommendation algorithm, compose it with other pipelines, and deploy.
Build Your Algorithm
Use the Studio SDK to define your search, scoring, and ranking pipeline. Filter candidates, apply ML models, add personalization, all in code.
import { StudioConfig, StudioV1 } from "mbd-studio-sdk";const mbd = new StudioV1({config: new StudioConfig({apiKey: process.env.MBD_API_KEY})});// Search trending prediction marketsconst candidates = await mbd.search().index("polymarket-items").size(50).include([{ filter: "term", field: "active",value: true },{ filter: "numeric",field: "volume_24hr",operator: ">=", value: 10000 }]).execute();// Score with ML modelconst scored = await mbd.scoring().model("polymarket-rerank-v1").userId("0xab2...e44").itemIds(candidates.map(c => c._id)).execute();
Compose & Deploy
Compose multiple algorithms together, preview results, and deploy with a single call. Get a Feed ID that's ready to serve.
// Compose search + scoring + ranking// Deploy as a single pipelineconst pipeline = mbd.pipeline().search({index: "polymarket-items",size: 50,filters: [{ filter: "term", field: "active",value: true }]}).scoring({model: "polymarket-rerank-v1",personalized: true}).ranking({strategy: "personalized",diversity: 0.3});// Deploy and get a Feed IDconst { feed_id } = await pipeline.deploy({name: "trending-markets-feed"});// feed_id: "feed_abc123"
Serve via API
Pass a wallet address and your Feed ID. Get back ML-ranked, personalized results at 250ms. One SDK call in production.
import { getClient } from "@embed-ai/sdk";const client = getClient(process.env.API_KEY_EMBED!);// Serve personalized feedconst feed = await client.feed.byWalletAddress("0xab2...e44","feed_abc123",{ top_k: 25 });
One platform, every feed type
Same pipeline, same SDK. Different data indexes and scoring models for each use case.
Trading Feeds
Personalized token discovery based on each wallet's trading history. Surface trending tokens, new listings, and smart money moves, ranked by relevance to each user.
Prediction Market Feeds
Recommend markets each user is likely to trade on. Rank by volume, relevance, and past betting patterns across Polymarket and other platforms.
Social Feeds
Personalized social content from Farcaster, Lens, and onchain activity. Surface casts, posts, and profiles relevant to each wallet's onchain graph.
Purpose-built recommendation models for onchain data
Not a generic ML platform. Models specifically designed for wallet-level behavior and onchain interactions.
Live in production in under two weeks
Battle-tested on real crypto data
Powered the Base App social feed
Our models re-ranked the Base App feed in real time using in-app events + on-chain data. At peak, 50% of transactions originated from personalized feeds, with 2.5x engagement lift.
Years of ML infra, now an API
The largest crypto exchange spent years and a full ML team building user embeddings, a targeting engine, and an ML notification platform. We put the same capability in an API you integrate in two weeks.
Common questions
Today we primarily serve prediction market feeds and trading feeds. Rank prediction markets by relevance to each wallet, surface trending tokens personalized to each user's trading history, and recommend assets based on onchain behavior. The platform is composable, so you can combine search, scoring, and ranking to fit your use case.
The largest crypto companies have spent years building internal ML personalization: user embedding models, targeting engines, feature stores, and ranking platforms. That requires a dedicated ML team, streaming infrastructure, and ongoing maintenance. Embed delivers the same capability via API. What takes an internal team years to build, you integrate in two weeks.
Embed's recommendation engine is built on three layers: (1) Wallet embeddings from onchain interactions across protocols. (2) User-item pair features computed at request time for contextual relevance. (3) An HRNN ranking model trained on 130M+ onchain interactions, delivering ranked results at 250ms per API call. Personalization kicks in from just 2 user interactions.
Nothing to get started. Our models are pre-trained on onchain data. For personalization, we use the wallet address your user connects with. No PII, no user accounts. If you want to incorporate in-app events (clicks, views, swaps), you can send those via our ingestion API to improve relevance.
Minimal. Call a REST endpoint or use our TypeScript SDK. Most teams integrate in 1-2 weeks with no changes to existing infrastructure. We plug into your stack, not replace it.
General-purpose tools like AWS Personalize or Algolia Recommend don't understand crypto. They have no concept of wallet behavior, protocol interactions, or onchain trading patterns. Embed is crypto-native: our models are trained on prediction market bets, token swaps, and onchain activity. We understand what a wallet does across the ecosystem, not just within one app.
Yes, that's a core feature. You can chain search filters, multiple scoring models, and ranking strategies into a single pipeline. For example: search trending markets, score by wallet affinity, re-rank by recency, apply diversity rules. Save the whole pipeline as one Feed ID.
Pricing scales with your usage, not your user count. We offer flexible structures depending on your use case and growth stage. Get in touch and we'll find the right fit.
Embed was founded by a former AWS and Chainlink data scientist and is backed by a16z crypto, Polymorphic Capital, Mask Network, Forward Research, WAGMI Ventures, and Social Graph Ventures. The team includes ML engineers who've built personalization and recommendation systems at Amazon, Discovery+, and Bloomberg.
Sign up at console.mbd.xyz to get an API key. Use the Studio SDK to build your first feed pipeline, or try the AI feed builder to generate one from a natural language description. Deploy, get a Feed ID, and start serving with the Feed SDK.
Stop showing every user
the same feed
Build, deploy, and serve personalized feeds with a single API. From idea to production in minutes.