SeekSphere
He worked at SuperTokens. Of course the search SDK is pitched like Clerk and SuperTokens. 'Show me red running shoes under three thousand that are actually in stock' is just sessions for people who sell shoes. The satire writes itself. The pipeline does not: intent, entities, SQL, millisecond reads off a pre-processed catalog.
SeekSphere is a natural-language search layer for e-commerce. The product bet is that filter drawers are a UI for a query planner humans should not have to operate. A shopper talks. A classifier pulls intent, brands, categories, attributes. An LLM step, exposed through MCP, compiles that into SQL against a catalog that has already been shaped for retrieval. The developer surface is an SDK you drop in the way you drop in auth.
The problem, as the product states it
E-commerce search still mostly means a text box plus a stack of facets. That works if the shopper already knows the schema. It fails the moment the query is a sentence: constraints, negations, stock, price, brand, 'like this but not that.' SeekSphere's public architecture is a four-step path from that sentence to a result set, with the interesting work in the middle two steps — classification and compilation — not in the search box.
Transform complex filter-heavy experiences into simple, conversational queries. The future of intelligent search for e-commerce platforms.
- Natural language in. A classifier reads intent, entities, and context — brands, categories, attributes, preferences.
- LLM processing over MCP turns that structure into optimized SQL, not a keyword soup scored by hope.
- Reads are aimed at a pre-processed catalog so the interactive path stays in milliseconds even when the product set is large.
- MCP connectors sit in front of catalogue APIs and inventory databases instead of pretending one schema is universal.
Our classifier analyzes intent, entities, and context. Advanced NLP converts to optimized SQL queries.
Developer-first, on purpose
The go-to-market is two-tier, and the interesting one is the SDK. Large catalogs get an embeddable client in the same shape as an auth provider: install, configure, search(query). Smaller shops get a plugin. That split is a SuperTokens lesson applied to retrieval — the hard product is not the demo query, it is the integration surface that does not force a rewrite of the storefront.
Easy integration with existing e-commerce platforms. Similar to Clerk and SuperTokens model.
The public snippet is the whole contract: install @seeksphere/sdk, call seeksphere.search(query), get results. Behind that call is the classifier, the LLM-to-SQL step, and the connector layer. The portfolio joke is that this is auth-shaped infrastructure. The technical claim is that search quality lives in how you compile language into a query plan, and how you keep that plan off the hot path of an unindexed product table.