Step 1 · query capture
Discovery engine for modern bookselling
Semantic search and recommendation APIs for book platforms. Your catalogue. Your stores. Your experience.
Multiple distinct modes of discovery
Use the same engine across multiple surfaces: embedded UIs for readers, and a callable API and MCP for your platform and partners.
User prompt ready search
Convert nuanced prompts into ranked discovery cards ready for in-app placement.
Example A · Complex emotional query
Step 2 · results page 1
Step 3 · results page 2
Example A · Complex emotional query
Step 1 · query capture
Example A · Complex emotional query
Step 2 · results page 1
Example A · Complex emotional query
Step 3 · results page 2
Example B · Combine book tones query
Step 1 · query capture
Step 2 · results page 1
Step 3 · results page 2
Example B · Combine book tones query
Step 1 · query capture
Example B · Combine book tones query
Step 2 · results page 1
Example B · Combine book tones query
Step 3 · results page 2
Chat-assisted discovery
Support conversational follow-ups and return targeted recommendations within a single module.
Example C · chat-assisted in store discovery
Step 1 · chat flow
Example C · chat-assisted in store discovery
Step 1 · chat flow
From Catalog To Delivery
A view into how each title becomes production-ready discovery.
Catalog Intake
We take your source catalog feed as-is.
{
"isbn": "string",
"title": "string",
"description": "string",
"optional_store_data": {
"about author": "string",
"publisher": "string",
"reviews": ["string"]
},
"source_url": "string",
"image_url": "string"
}
Annotation Layer
Each catalog record is enriched for product-ready discovery.
Enrichment outputs
- Theme tags and intent vectors
- Reader-intent modeling grounded in reviews and descriptions
Retrieval Engine
Enriched records are compiled into production indexes.
Index build steps
- Index embeddings of your catalog
- Optimized for fast retrieval
- Relevance-based ranking
- Query understanding and refinement
Fleet + Delivery
We serve globally from our fleet and return interfaces your store can ship directly.
MCP
API
Web Components
How it works
Integration flow from feed to customer-facing discovery
-
Volume I
Send your catalogue
Items, metadata, review signals, and your store directory.
Feed Layer Catalog Intake souida -
Volume II
We build searchable textures
We enrich your catalogue, generate semantic embeddings, then build a search index.
Semantic Layer Index Build souida -
Volume III
Your platform queries
Search, recommendations, and AI assistant integration through one API surface.
API Layer Query Runtime souida -
Volume IV
Discover better books
Give every book in your catalog a chance to be discovered.
Commerce Layer Discovery Engine souida
What this enables
Commercial and product outcomes
-
Result I
Better conversion on existing traffic
Readers find books they actually want to buy across search, browse, and every discovery entry point you support.
Outcome Conversion Lift souida -
Result II
New discovery channels
Expose a partner-ready endpoint (API + MCP) so connected assistants can resolve recommendations to your checkout—without exporting your catalogue or reviews.
Outcome Assistant Reach souida -
Result III
Demand intelligence
Every query is a signal for underserved demand, merchandising opportunities, and branch-level behavior.
Outcome Demand Signals souida