An intent-based product discovery API interprets the underlying goal of a query, such as 'durable laptop for field engineers,' rather than just matching keywords. This requires shifting from traditional search to semantic search using vector embeddings. You'll map product attributes and vague user intents into a shared vector space, enabling the API to find relevant matches based on conceptual similarity, not just text. This foundational change is critical for serving autonomous AI buyers who reason about needs, not just specifications.
Guide
How to Design an Intent-Based Product Discovery API
A step-by-step guide to building a product discovery API that interprets vague buyer intent using embeddings, vector search, and agent-aware ranking.
Move beyond keyword matching to build an API that understands what buyers truly need.
To implement this, you need a vector database like Pinecone or Weaviate to store and query product embeddings. Your API must accept natural language queries, convert them to vectors, and perform a nearest-neighbor search against your product catalog. The ranking algorithm should prioritize agent-specific criteria—like reliability and compatibility—over generic popularity. This design directly enables the autonomous workflows described in our guide on How to Architect an AI Buyer-Ready Product API.
CORE INFRASTRUCTURE
Vector Database Comparison
Selecting the right vector database is critical for low-latency, high-recall semantic search in your Intent-Based Product Discovery API. This table compares the leading options across key operational dimensions.
| Feature / Metric | Pinecone (Serverless) | Weaviate (Self-Hosted) | pgvector (PostgreSQL Extension) |
|---|---|---|---|
Primary Architecture | Fully-managed serverless | Self-managed or cloud hybrid | PostgreSQL extension |
Multi-Tenancy Support | |||
Native Hybrid Search (Vector + Keyword) | Requires custom SQL | ||
Maximum Dimensions per Vector | 20000 | 65535 | Limited by PostgreSQL (typ. 2000) |
Typical Query Latency (p95) | < 100 ms | < 50 ms | 100-300 ms |
Pricing Model | Per-query & storage | Infrastructure cost | Database instance cost |
Best For | Rapid scaling, zero ops | Data control, custom modules | Simplicity, existing Postgres stack |
Enabling Efficiency, Speed & Accuracy
Intelligent Analysis, Decision & Execution
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
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Search across company data
Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.
Useful when people spend too long searching or get different answers from different systems.
Enterprise searchRAGPermissions
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Automate internal workflows
Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.
Useful when repetitive work moves across multiple tools and teams.
AI agentsWorkflow automationGovernance
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Add AI to products and internal tools
Build assistants, guided actions, or decision support into the software your team or customers already use.
Useful when AI needs to be part of the product, not a separate tool.
AI integrationDecision supportModel routing
Read moreINTENT-BASED API DESIGN
Common Mistakes
Avoid these critical errors when moving from keyword search to an API that interprets nuanced buyer intent for AI agents.
Intent-based discovery interprets the goal behind a query, not just its keywords. A traditional API matches 'laptop' to products with that word in the title. An intent-based API interprets 'durable laptop for field engineers' to mean a device with specific attributes: ruggedness, long battery life, and outdoor visibility. The difference is moving from lexical matching to semantic understanding. This requires representing products and queries as embeddings—numerical vectors that capture meaning—and using vector search to find the closest matches. Without this shift, AI agents cannot perform the complex, contextual product research they are designed for.
About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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