Personalized feeds, built and deployed in minutes
Write your feed as code — or let the AI builder write it for you. Search live onchain indices, boost by each wallet's interests, and serve ranked, hydrated results with a single API call.
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
Compose the algorithm, blend and deploy it as a feed config, and serve — from the Console or the API.
Build Your Algorithm
Write it in the Studio DSL: search any live index, filter, and boost by the wallet's AI-labeled interests — or describe the feed in plain English and the AI builder generates the code for you.
// Candidates: active, liquid marketsconst candidates = await mbd.search().index("polymarket-items").include().term("active", true).numeric("volume_1wk", ">=", 10000).exclude().term("closed", true).boost().groupBoost("polymarket-wallets","ai_labels_med", polymarketWallet,"label", 1, 5, 10).execute();mbd.addCandidates(candidates);
Rank, Blend & Deploy
Mix scores with weights and diversity rules, then group algorithms into a feed config — weighted blending, two fallback tiers for cold start, cache TTLs, and impression-aware exclusion rules. Preview real output, then deploy.
// Blend signals, keep varietyconst ranking = await mbd.ranking().sortingMethod("mix").mix("topic_score", "desc", 40).mix("user_affinity_score", "desc", 40).mix("score_trending", "desc", 20).diversity("semantic").lambda(0.5).horizon(20).execute();mbd.addRanking(ranking);// Save it, then group algorithms into// a feed config: weighted blend, two// fallback tiers, caching, exclusions// POST /v3/studio/deploy/configs
Serve via API
One REST call with a wallet address. Cache-first responses with session pagination and a new-items endpoint for pull-to-refresh badges. Every item arrives hydrated with live context.
POST /v3/studio/deploy/serve{"config_id": "feed_abc123","inputs": {"polymarketWallet": "0xab2...e44"}}// -> { served_id, num_pages, feed: [...] }// paginate a sessionGET /deploy/serve/page?page=2// "N new items" for pull-to-refreshGET /deploy/serve/new_items
One platform, every feed type
Same pipeline, same DSL — different indices: markets, tokens, trades, wallets, and positions across Polymarket, Kalshi, Hyperliquid, and DEXs on Base, Ethereum, and Solana.
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 Kalshi.
Smart-Money & Wallet Feeds
Feeds built from ranked trader profiles and live positions. Surface top-trader leaderboards, whale moves, and the trades of wallets most similar to each user.
A recommendation pipeline purpose-built for onchain data
Not a generic ML platform — an engine designed around wallets, markets, and live trading behavior.
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 engine is built on three layers: (1) Live indices of markets, tokens, trades, wallets, and positions — streaming-updated, with trader scores refreshed every 15 minutes. (2) Wallet-level personalization: AI-labeled interests, semantic embeddings, and similar-wallet affinity computed against each candidate. (3) A composable ranking stage with weighted mixing, diversity rules, and optional ML re-ranking for prediction markets. Serving is cache-first with tiered warmup, so production calls typically return in the low hundreds of milliseconds.
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. Inside one algorithm you chain search filters, boosts, and ranking strategies — search trending markets, score by wallet affinity, re-rank with diversity rules. Then a feed config blends multiple algorithms with weights (say 70/30) and adds fallback tiers, caching, and exclusion rules. The whole thing serves under one config 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. Build your first algorithm in the Studio DSL, or let the AI feed builder generate one from a natural-language description. Deploy it to get a config ID, and serve it with a single REST call.
Stop showing every user
the same feed
Build, deploy, and serve personalized feeds with a single API. From idea to production in minutes.