Wednesday, February 4, 2026
Sanity Embeddings: Vector Search for AI-Ready Content
Most enterprise CMS platforms treat AI as an add-on module. Sanity treats it as core infrastructure.
The difference matters for B2B SaaS companies evaluating headless CMS platforms. Gartner predicts that 40% of enterprise applications will feature task-specific AI agents by the end of 2026, up from less than 5% in 2025. That trajectory requires content infrastructure capable of supporting semantic search, AI chatbots and personalization at scale.
Sanity's Embeddings Index API provides the only native, production-ready vector search infrastructure among major headless CMS platforms, though the feature remains in beta (Growth plan and above). For enterprise technology evaluators and website managers, this represents a significant architectural decision point: Build vector search yourself with external infrastructure or use Sanity's managed embeddings solution.
How Sanity Embeddings Work
Vector embeddings convert text content into numerical representations that capture semantic meaning. Instead of matching exact keywords, vector search understands that "authentication process" and "login workflow" refer to the same concept.
Sanity's Embeddings Index API operates through a five-step process:
- Document selection: A Graph-Relational Object Queries (GROQ) filter query determines which content enters the embeddings index
- Field projection: A GROQ projection selects specific fields for embedding, not entire documents
- Vector generation: Selected content routes to OpenAI's API for embedding creation
- Vector storage: Vectors store in a Sanity-managed Pinecone database with no separate account required
- Automatic synchronization: Webhooks update the index as content changes
The system focuses on textual content from documents. Once indexed, queries submit via POST requests to the Embeddings Index API and return documents with contextually similar content rather than exact keyword matches.
Optimization tip: Sanity's documentation recommends creating embeddings on summarized versions of documents instead of full documents for better semantic matching accuracy.
Management options include a CLI tool for programmatic control, a Studio UI for visual workflows and a full Management API for custom integrations. The feature requires a Growth plan or above and remains in beta status.
Seven Production-Ready Use Cases
Vector search capabilities map to specific B2B content challenges. Each use case addresses problems that traditional keyword search cannot solve.
Semantic Search for Documentation
Developers searching for "how to authenticate API requests" can successfully retrieve documentation using different terminology like "authorization headers" or "token-based access." This demonstrates how semantic search enables meaning-based content discovery beyond keyword matching. NearForm's implementation documents this pattern using a four-stage pipeline combining Sanity's embeddings with OpenAI integration for vector generation and semantic similarity search across technical documentation.
This capability matters most for enterprise documentation sites, knowledge bases and product information repositories where technical users need to find complex information without knowing exact terminology.
AI Chatbot Integration with RAG
Enterprise sales and support chatbots integrated with headless CMS through vector search and retrieval-augmented generation (RAG) can surface different content based on customer role, industry vertical or account type while maintaining compliance requirements. This architecture uses CMS as the source of truth for all content, providing governance capabilities that include version control, approval workflows, audit trails and content lifecycle management. These capabilities matter for B2B companies with regulatory obligations.
Automated Content Recommendations
Vector search enables related content suggestions by computing semantic similarity without manual tagging. According to Sanity's documentation, the Embeddings Index API automatically clusters content by semantic similarity, enabling automated "related content" recommendations based on conceptual proximity and surfacing relevant materials without requiring manual relationship definition or tagging.
For enterprise websites with extensive resource libraries, this eliminates the content editor workload of manually defining and maintaining relationships across hundreds or thousands of assets.
Personalized Content Delivery
Embeddings enable advanced personalization by matching user intent and context against content vectors in real-time. According to Sanity's AI guide, this approach generalizes to new content and contexts automatically, unlike rule-based personalization that requires explicit if/then logic for every scenario.
Account-based marketing particularly benefits from vector search capabilities, where content delivery must adapt to company size, industry vertical, technology stack and role-based requirements without manually programming rules for every permutation. This approach eliminates the content editor workload of manually defining personalization rules that cannot scale in enterprise environments.
Developer Portal Enhancement
Developer documentation requires specialized search that understands code concepts and technical terminology. According to technical implementation guides, the vector search toolkit for documentation provides GitHub Actions for automated content ingestion, Edge Functions for sub-100ms query processing at global scale and vector similarity search across documentation pages, code snippets and API references. This enables developers to find code examples and integration guides based on conceptual understanding rather than string matching.
Content Gap Analysis
Beyond retrieval, embeddings enable analytical use cases. Content teams can identify semantic gaps in coverage, detect duplicate content through clustering and analyze distribution across topic areas. This provides objective data for content modeling audit and strategy refinement.
Multilingual Discovery
Modern embedding models enable cross-language semantic search without translation at query time. According to the NearForm case study, embedding models like OpenAI's text-embedding models map multiple languages into a shared semantic space, enabling queries in one language to return relevant content in other languages.
Implementation Considerations
Enterprise implementations require careful architectural planning regardless of platform choice.
Performance Requirements
Enterprise vector search implementations should target specific latency budgets to ensure responsive user experiences for content discovery and AI-powered retrieval systems.
Key performance targets include:
- Search query response times under 100ms at p95
- Efficient embedding generation that scales with content volume
- End-to-end search experiences optimized for user responsiveness
Hierarchical Navigable Small World (HNSW) indexing is recommended for most CMS use cases, providing sub-millisecond query performance for datasets under 10M vectors with a memory overhead of approximately 1.5x vector data size.
Security Requirements
Enterprise security best practices outline core requirements: AES-256 encryption at rest, TLS 1.3 in transit, integration with enterprise key management, role-based access control (RBAC) for search operations and row-level security filtering results based on user attributes.
Compliance requirements include data residency (deploy vector databases in same region as CMS for GDPR), audit logging and hard deletion capabilities for GDPR Article 17 compliance.
Hybrid Search Strategy
Production implementations should combine vector similarity with traditional keyword search. According to Aplyca's enterprise guide, result fusion methods like Reciprocal Rank Fusion (RRF) or weighted fusion (0.7 × vector_score + 0.3 × keyword_score) provide optimal results.
The Strategic Decision
Vector search is becoming table stakes for enterprise CMS platforms. According to IDC's 2025 MarketScape, Sanity is currently the only headless CMS platform offering native, production-ready vector search infrastructure, representing a significant competitive advantage in the emerging semantic search landscape.
For B2B SaaS companies evaluating headless CMS platforms, the decision framework maps to specific priorities:
- Semantic search priority: Sanity provides the only production-ready managed solution
- AI content operations and governance: Contentful offers mature capabilities with composable flexibility
- Self-hosting and data sovereignty: Strapi provides open-source infrastructure with custom implementation options
- Full DXP with maximum governance: Contentstack delivers enterprise-grade management
Sanity CMS offers composable architecture capabilities designed for B2B SaaS companies, with the Embeddings Index API providing native vector search infrastructure for semantic search, AI chatbots and content recommendations. The platform's unified architecture treats AI as core infrastructure rather than optional add-ons through AI Assist (offering automated translation, SEO optimization and metadata generation) and the Embeddings Index API (enabling semantic search through OpenAI embeddings and managed Pinecone storage). The right CMS choice depends on your specific technical requirements: whether native vector search matters, your preferred pricing model and your governance and compliance obligations.
What This Means for Your CMS Strategy
Enterprise content platforms face a clear inflection point. As AI capabilities become central to content discovery and personalization, the infrastructure decisions you make today will determine your operational flexibility for years ahead. Sanity's native vector search provides a meaningful head start for teams prioritizing semantic search, RAG-powered chatbots and automated content recommendations without the overhead of managing external vector databases.
The competitive landscape will shift as other platforms add similar capabilities. The question is whether waiting makes sense for your roadmap. For B2B SaaS companies already planning AI-powered content experiences, building on infrastructure designed for these use cases reduces technical debt and accelerates time to value.
Your CMS architecture should enable marketing team autonomy while supporting the AI capabilities your product and growth teams need. That alignment requires intentional platform selection, not incremental bolt-ons.
Ready to evaluate headless CMS platforms for your AI-ready content infrastructure? Talk to Webstacks about implementing a composable architecture that supports semantic search, personalization and scalable content operations.
