{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"snowflake-cortex","slug":"snowflake-cortex","name":"Snowflake Cortex","type":"platform","url":"https://www.snowflake.com/en/data-cloud/cortex","page_url":"https://unfragile.ai/snowflake-cortex","categories":["deployment-infra","rag-knowledge"],"tags":[],"pricing":{"model":"usage-based","free":false,"starting_price":"$0.12/credit"},"status":"active","verified":false},"capabilities":[{"id":"snowflake-cortex__cap_0","uri":"capability://text.generation.language.sql.callable.serverless.llm.function.invocation","name":"sql-callable serverless llm function invocation","description":"Exposes foundation models (Claude, GPT-4, Llama, Mistral) as SQL functions callable directly within Snowflake queries, eliminating data movement by executing inference inside the data warehouse boundary. Models are accessed via Snowflake's managed serverless endpoints rather than direct API calls, with results returned as SQL result sets for immediate downstream processing.","intents":["Run LLM inference on warehouse data without exporting to external APIs","Chain LLM outputs into SQL pipelines for ETL-integrated AI workflows","Invoke multiple LLM calls in a single query for batch processing","Access multiple foundation models through a unified SQL interface"],"best_for":["Data engineers building AI-augmented data pipelines","Analytics teams integrating LLM analysis into BI workflows","Organizations with strict data residency requirements"],"limitations":["No direct model parameter tuning exposed in SQL interface — temperature, max_tokens, etc. configuration scope unknown","Latency characteristics not documented — cold start behavior and per-request overhead unknown","No built-in prompt versioning or A/B testing framework for comparing model outputs","Consumption-based pricing per inference not transparently disclosed — cost per token or per request unknown","Model versions and update cadence not specified — unclear when Claude/GPT-4 versions are refreshed"],"requires":["Snowflake account with Standard Edition or higher","Data loaded into Snowflake tables or stages","API credentials for selected LLM provider (Anthropic, OpenAI, etc.) or Snowflake-managed credentials","SQL query execution permissions"],"input_types":["text (SQL strings, table columns)","structured data (table rows, JSON)","images (via multimodal functions, format unknown)"],"output_types":["text (LLM response as SQL string)","structured data (JSON, parsed into SQL columns)"],"categories":["text-generation-language","tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"snowflake-cortex__cap_1","uri":"capability://image.visual.multimodal.ai.function.execution.text.image.audio.analysis","name":"multimodal ai function execution (text, image, audio analysis)","description":"Cortex AI Functions support multimodal inputs beyond text, enabling image analysis, audio transcription, and cross-modal reasoning within SQL queries. Implementation details on how images/audio are ingested, encoded, and routed to appropriate model backends are not documented, but the capability suggests Snowflake handles format conversion and model selection internally.","intents":["Analyze images stored in Snowflake stages or external tables without exporting","Transcribe or analyze audio files as part of data enrichment pipelines","Extract text and metadata from documents (PDFs, images) for downstream processing","Perform cross-modal analysis (e.g., match images to text descriptions)"],"best_for":["Media and content companies processing images/video at scale","Document processing teams automating OCR and content extraction","Compliance teams analyzing unstructured media for regulatory requirements"],"limitations":["Supported image/audio formats not specified — unclear which codecs, resolutions, and file sizes are supported","No documentation on preprocessing or normalization — image resizing, audio sampling rates unknown","Multimodal model selection not transparent — unclear which models handle which modalities or how routing works","Batch processing limits for large media files unknown — potential timeout or payload size constraints","No explicit mention of streaming audio or video — appears limited to file-based inputs"],"requires":["Snowflake account with Cortex AI Functions enabled","Media files staged in Snowflake internal/external stages or accessible via URL","Sufficient compute credits for multimodal inference (pricing not detailed)"],"input_types":["image (JPEG, PNG, WebP — formats not officially confirmed)","audio (MP3, WAV, OGG — formats not officially confirmed)","text (for cross-modal queries)"],"output_types":["text (transcription, analysis results)","structured data (extracted metadata, JSON)"],"categories":["image-visual","text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"snowflake-cortex__cap_10","uri":"capability://automation.workflow.end.to.end.observability.and.cost.tracking.for.ai.workloads","name":"end-to-end observability and cost tracking for ai workloads","description":"Cortex integrates observability into Snowflake's monitoring and governance framework, providing visibility into LLM function execution, resource consumption, and costs. The system tracks which models are invoked, how much compute is consumed, and how results are used downstream — though specific metrics, dashboards, alerting capabilities, and cost optimization tools are not detailed.","intents":["Monitor LLM function performance and identify bottlenecks","Track AI workload costs and optimize spending across models","Audit which users accessed which models and what data was processed","Set up alerts for cost overruns or performance degradation"],"best_for":["FinOps teams managing AI infrastructure costs","Compliance teams auditing AI-assisted data processing","Platform teams optimizing Cortex performance and resource allocation"],"limitations":["Specific metrics not documented — unclear whether latency, throughput, error rates, or token counts are tracked","Dashboard and visualization capabilities not specified — unclear what pre-built dashboards are available","Alerting configuration not detailed — unclear whether custom alerts can be set for cost, latency, or errors","Cost breakdown by model/provider not specified — unclear whether costs are attributed to specific models or providers","Historical data retention not documented — unclear how long observability data is retained","Integration with external monitoring tools not mentioned — unclear whether Prometheus, Datadog, or other tools are supported","No mention of cost optimization recommendations — unclear whether the system suggests cheaper models or query optimizations"],"requires":["Snowflake account with observability features enabled","Appropriate role-based access controls to view cost and usage data"],"input_types":["LLM function execution (automatic tracking)"],"output_types":["observability metrics (latency, cost, errors)","audit logs (user, model, data accessed)"],"categories":["automation-workflow","safety-moderation","deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"snowflake-cortex__cap_11","uri":"capability://code.generation.editing.sql.native.model.deployment.and.inference","name":"sql-native model deployment and inference","description":"Enables deployment of trained ML models (including fine-tuned LLMs) as SQL functions, making inference callable directly from SQL queries without external APIs or application code. Supports batch inference on large datasets, real-time inference in stored procedures, and integration with Snowflake's query optimizer for efficient execution. Models are versioned and can be rolled back or A/B tested within SQL.","intents":["Deploy a fine-tuned classification model as a SQL function and use it to label millions of records in a single query","Integrate a trained recommendation model into a stored procedure for real-time personalization","A/B test two model versions by routing queries to different functions based on user ID","Batch score a dataset with a trained model without exporting data or writing Python code"],"best_for":["data teams who want to deploy models without learning Python or managing APIs","organizations with SQL-first workflows who need to integrate ML into existing pipelines","teams needing to version, test, and rollback models within SQL"],"limitations":["Supported model types (LLMs, classifiers, regressors, embeddings) are not enumerated","Model versioning and rollback mechanisms are not documented","A/B testing framework and routing policies are not detailed","Inference latency and throughput for batch vs. real-time scoring are not published","No documented support for custom model formats or bring-your-own-model (BYOM) scenarios"],"requires":["Snowflake account with Cortex enabled","Trained model (via Cortex fine-tuning or external training)","SQL knowledge for defining and calling model functions"],"input_types":["structured data (input features from Snowflake tables)"],"output_types":["structured data (model predictions stored in Snowflake tables)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"snowflake-cortex__cap_2","uri":"capability://text.generation.language.natural.language.to.sql.conversion.cortex.analyst","name":"natural language to sql conversion (cortex analyst)","description":"Cortex Analyst translates natural language questions into executable SQL queries, enabling non-technical users to query data without writing SQL. The system likely uses an LLM fine-tuned or prompted with schema context to generate queries, though the exact prompt engineering approach, schema inference mechanism, and query validation strategy are not documented.","intents":["Enable business users to query data using conversational language","Reduce SQL writing burden for analysts and data scientists","Generate ad-hoc queries from natural language without manual SQL composition","Support interactive Q&A sessions over structured data"],"best_for":["Business intelligence teams democratizing data access to non-technical stakeholders","Self-service analytics platforms where users lack SQL expertise","Organizations seeking to reduce data analyst workload for routine queries"],"limitations":["Query complexity limits unknown — unclear whether it handles complex JOINs, CTEs, window functions, or only simple SELECT/WHERE patterns","Schema context requirements not specified — unclear how much schema documentation or metadata is needed for accurate translation","No explicit error handling or query validation — unclear how invalid or unsafe queries are caught before execution","Hallucination risk not addressed — LLM may generate plausible but incorrect SQL without human review","No mention of query explainability — users cannot see the generated SQL before execution in documented workflows","Performance optimization not discussed — generated queries may be inefficient compared to hand-written SQL"],"requires":["Snowflake account with Cortex Analyst enabled","Tables with documented schema and ideally descriptive column names","Natural language input (English assumed, other languages not confirmed)"],"input_types":["text (natural language question)"],"output_types":["SQL query (generated)","query results (structured data)"],"categories":["text-generation-language","code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"snowflake-cortex__cap_3","uri":"capability://search.retrieval.hybrid.semantic.and.keyword.search.with.vector.indexing","name":"hybrid semantic and keyword search with vector indexing","description":"Cortex Search combines text embeddings (semantic search) with traditional keyword matching to enable hybrid retrieval over unstructured data. The system automatically generates embeddings for indexed documents, stores them in a managed vector index, and routes queries to both semantic and keyword search paths, merging results via an undocumented ranking algorithm. No details on embedding model selection, index structure, or search latency are provided.","intents":["Search documents by semantic meaning rather than exact keyword matches","Combine semantic and keyword search for higher recall and precision","Build RAG systems that retrieve relevant context for LLM prompts","Index large document collections without managing vector databases separately"],"best_for":["RAG pipeline builders integrating retrieval with Cortex LLM functions","Knowledge management teams searching unstructured documents","Customer support teams building semantic search over help articles or FAQs"],"limitations":["Embedding model not specified — unclear which model generates vectors or whether custom embeddings are supported","Vector dimension and index type unknown — cannot assess memory footprint or search performance","Hybrid ranking algorithm not documented — unclear how semantic and keyword results are merged or weighted","No mention of re-ranking or semantic filtering — unclear whether results can be refined post-retrieval","Batch indexing latency unknown — unclear how long it takes to index large document collections","No explicit support for metadata filtering — unclear whether searches can be constrained by document attributes","Update frequency for indexed documents not specified — unclear whether indexes are real-time or batch-updated"],"requires":["Snowflake account with Cortex Search enabled","Documents staged in Snowflake tables or external stages","Text content in a queryable column (format and size limits unknown)"],"input_types":["text (documents to index)","text (search query)"],"output_types":["ranked list of documents (with relevance scores)","text (document content)"],"categories":["search-retrieval","memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"snowflake-cortex__cap_4","uri":"capability://planning.reasoning.data.agent.orchestration.for.structured.and.unstructured.data","name":"data agent orchestration for structured and unstructured data","description":"Cortex Agents coordinate multi-step workflows across structured tables and unstructured documents, routing queries to appropriate data sources and combining results. The agent likely uses an LLM to decompose user requests into sub-tasks, execute SQL queries and semantic searches, and synthesize results — but the exact orchestration logic, tool selection mechanism, and error recovery strategy are not documented.","intents":["Answer complex questions requiring data from multiple sources (tables + documents)","Automate multi-step data workflows without manual orchestration code","Build conversational interfaces that reason over mixed data types","Reduce manual data integration work by letting agents route queries intelligently"],"best_for":["Enterprise teams building AI-powered data assistants","Organizations with hybrid data landscapes (structured + unstructured)","Data teams automating complex analysis workflows"],"limitations":["Agent reasoning process not transparent — unclear how agents decide which data sources to query or in what order","No explicit tool registry or custom tool support — unclear whether agents can call arbitrary functions or only built-in Cortex functions","Error handling and fallback strategies not documented — unclear how agents recover from failed queries or ambiguous requests","Conversation memory and context management not specified — unclear whether agents maintain session state or require full context per request","Cost and latency for multi-step workflows unknown — unclear how many LLM calls are made per agent request","No mention of agent explainability or audit trails — users cannot see the reasoning steps or data sources consulted","Scalability limits unknown — unclear whether agents can handle high-concurrency or long-running workflows"],"requires":["Snowflake account with Cortex Agents enabled","Structured data in Snowflake tables","Unstructured data indexed via Cortex Search","Natural language input describing the task"],"input_types":["text (natural language request)"],"output_types":["text (agent response with synthesized results)","structured data (extracted from multiple sources)"],"categories":["planning-reasoning","tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"snowflake-cortex__cap_5","uri":"capability://code.generation.editing.model.fine.tuning.and.custom.model.deployment","name":"model fine-tuning and custom model deployment","description":"Cortex supports fine-tuning foundation models on proprietary data and deploying custom models, though implementation details are minimal in available documentation. The capability likely involves uploading training data, configuring hyperparameters, and deploying fine-tuned models as SQL-callable functions — but the exact training infrastructure, supported model architectures, and deployment process are not specified.","intents":["Adapt foundation models to domain-specific language or tasks","Deploy proprietary models trained outside Snowflake","Improve model accuracy on specialized datasets without retraining from scratch","Maintain model versions and rollback to previous versions"],"best_for":["Organizations with proprietary datasets requiring model customization","Teams with existing ML infrastructure seeking to integrate with Snowflake","Enterprises needing compliance-sensitive model deployments"],"limitations":["Fine-tuning process not documented — unclear whether it supports parameter-efficient methods (LoRA, QLoRA) or full fine-tuning","Supported model architectures unknown — unclear which models can be fine-tuned or deployed","Training data requirements not specified — minimum dataset size, format, and preprocessing steps unknown","Hyperparameter configuration scope unknown — unclear which parameters are exposed or auto-tuned","Training time and cost not documented — unclear how long fine-tuning takes or how it is priced","Model versioning and rollback not detailed — unclear how versions are managed or how to revert to previous models","No mention of model evaluation or validation — unclear how model quality is assessed before deployment","Custom model format support unknown — unclear whether ONNX, TensorFlow, PyTorch, or other formats are supported"],"requires":["Snowflake account with fine-tuning/deployment capabilities enabled","Training data in Snowflake tables or stages","Compute credits for training (pricing unknown)","Base model selection (supported models not specified)"],"input_types":["structured data (training examples)","text (training data)"],"output_types":["fine-tuned model (deployed as SQL function)","model metadata (version, performance metrics)"],"categories":["code-generation-editing","automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"snowflake-cortex__cap_6","uri":"capability://automation.workflow.consumption.based.serverless.compute.with.elastic.scaling","name":"consumption-based serverless compute with elastic scaling","description":"Cortex executes LLM and ML workloads on Snowflake's serverless compute infrastructure, automatically scaling resources based on demand without requiring users to provision or manage clusters. Pricing is consumption-based (per-credit model), though specific per-request, per-token, or per-second costs are not disclosed. The underlying compute hardware specifications, scaling policies, and cold-start latency are not documented.","intents":["Run variable-load AI workloads without pre-provisioning compute capacity","Pay only for actual inference usage without idle capacity costs","Scale from single requests to batch processing without infrastructure changes","Avoid managing Kubernetes clusters or containerized inference servers"],"best_for":["Organizations with unpredictable or bursty AI workloads","Teams seeking to minimize infrastructure management overhead","Enterprises with strict CapEx constraints preferring OpEx consumption models"],"limitations":["Pricing transparency lacking — per-token, per-request, or per-second costs not disclosed; only 'consumption-based' mentioned","Compute hardware specifications unknown — GPU/CPU types, memory, and performance tiers not specified","Scaling policies not documented — unclear how quickly resources scale up/down or whether minimum concurrency is enforced","Cold-start latency unknown — unclear whether first request incurs startup overhead","Throughput limits not specified — unclear whether there are per-account or per-query concurrency limits","Regional availability not detailed — unclear which cloud regions support serverless Cortex compute","No SLA documentation — uptime guarantees, latency targets, or availability zones not specified","Cost optimization tools not mentioned — unclear whether cost tracking or budget alerts are available"],"requires":["Snowflake account with Standard Edition or higher","Sufficient Snowflake credits or on-demand consumption budget","Network connectivity to Snowflake cloud region"],"input_types":["any (routed to appropriate compute backend)"],"output_types":["any (returned from compute backend)"],"categories":["automation-workflow","deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"snowflake-cortex__cap_7","uri":"capability://safety.moderation.secure.data.residency.with.governance.enforced.processing","name":"secure data residency with governance-enforced processing","description":"Cortex processes data within Snowflake's secure perimeter, ensuring that sensitive data never leaves the warehouse for external LLM API calls. The system enforces data governance policies (encryption, access controls, audit logging) throughout the AI pipeline, though specific encryption algorithms, audit log formats, and policy enforcement mechanisms are not documented.","intents":["Process sensitive or regulated data (PII, PHI, financial) without external data movement","Meet compliance requirements (HIPAA, GDPR, SOC 2) for data residency","Maintain audit trails for AI-assisted data processing","Enforce role-based access controls on LLM function execution"],"best_for":["Healthcare organizations processing patient data","Financial institutions handling regulated data","Government agencies with strict data residency mandates","Enterprises with data sovereignty requirements"],"limitations":["Encryption details not specified — unclear whether data is encrypted at rest, in transit, or both; encryption key management not documented","Audit logging scope unknown — unclear what events are logged, retention periods, or log access controls","Policy enforcement mechanism not detailed — unclear how data governance policies are defined, applied, or validated","No explicit mention of data masking or PII redaction — unclear whether sensitive fields can be automatically masked before LLM processing","Compliance certifications not listed — unclear which regulatory frameworks (HIPAA, GDPR, SOC 2) are formally certified","Data deletion and retention policies not specified — unclear how long data is retained or how deletion is enforced","No mention of differential privacy or federated learning — unclear whether privacy-preserving techniques are available"],"requires":["Snowflake account with Business Critical Edition or higher (implied for compliance features)","Data governance policies configured in Snowflake","Role-based access controls (RBAC) configured for users and service accounts"],"input_types":["sensitive data (PII, PHI, financial records)"],"output_types":["processed results (with governance controls applied)"],"categories":["safety-moderation","automation-workflow","deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"snowflake-cortex__cap_8","uri":"capability://tool.use.integration.marketplace.integration.for.pre.built.data.and.applications","name":"marketplace integration for pre-built data and applications","description":"Snowflake Marketplace hosts 3,400+ listings of ready-to-use datasets, applications, and services that can be directly accessed or deployed within Cortex. The marketplace enables one-click access to third-party data providers and pre-built AI applications without manual integration or data staging, though the exact listing types, deployment mechanisms, and revenue sharing models are not detailed.","intents":["Access third-party datasets for enrichment without manual data integration","Deploy pre-built AI applications (e.g., sentiment analysis, forecasting) without custom development","Discover and integrate business data providers (750+ documented) for live data access","Monetize custom Cortex applications by listing them in the marketplace"],"best_for":["Organizations seeking to quickly augment data with third-party sources","Teams deploying pre-built AI applications without custom ML expertise","ISVs and data providers monetizing applications through Snowflake's ecosystem"],"limitations":["Listing composition unknown — unclear what percentage are datasets vs. applications vs. services","Deployment process not documented — unclear whether listings are auto-deployed or require manual configuration","Pricing and revenue sharing not specified — unclear how marketplace listings are priced or how revenue is split","Quality assurance and vetting not mentioned — unclear what standards listings must meet or how they are reviewed","Integration scope unknown — unclear whether marketplace applications can access all Cortex capabilities or only specific functions","Support and SLAs not documented — unclear what support is provided for marketplace listings","Listing discoverability not detailed — unclear how users find relevant applications or datasets"],"requires":["Snowflake account with marketplace access enabled","Appropriate data sharing agreements for third-party datasets","Sufficient compute credits for deploying marketplace applications"],"input_types":["marketplace listing (selected by user)"],"output_types":["deployed application or dataset (accessible in Snowflake)"],"categories":["tool-use-integration","automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"snowflake-cortex__cap_9","uri":"capability://text.generation.language.multi.provider.llm.access.with.unified.sql.interface","name":"multi-provider llm access with unified sql interface","description":"Cortex provides SQL-callable access to multiple foundation models from different providers (Anthropic Claude, OpenAI GPT-4, Meta Llama, Mistral Large 2) through a unified interface, abstracting away provider-specific API differences. Users select models via SQL function parameters, and Snowflake routes requests to the appropriate provider's endpoint — but model version selection, provider failover, and cost allocation across providers are not documented.","intents":["Compare outputs from different LLM providers without rewriting code","Switch between models for cost optimization or performance tuning","Access specialized models (e.g., Llama for open-source, Claude for reasoning) from a single interface","Avoid vendor lock-in by supporting multiple LLM providers"],"best_for":["Teams evaluating multiple LLM providers for specific use cases","Organizations seeking cost optimization by comparing provider pricing","Enterprises with multi-vendor strategies to reduce dependency"],"limitations":["Model versions not specified — unclear which versions of Claude, GPT-4, Llama, or Mistral are available or when they are updated","Provider failover not documented — unclear whether requests automatically retry with alternative providers if one fails","Cost allocation across providers unknown — unclear how usage is tracked and billed for each provider","Model-specific parameters not exposed — unclear whether provider-specific features (e.g., vision, function calling) are available through the unified interface","Provider authentication not detailed — unclear how API keys are managed for each provider or whether Snowflake-managed credentials are available","Rate limiting and quotas not specified — unclear whether per-provider limits are enforced","Model availability by region not documented — unclear which models are available in which Snowflake regions"],"requires":["Snowflake account with Cortex LLM functions enabled","API credentials for selected LLM providers (or Snowflake-managed credentials if available)","SQL query execution permissions"],"input_types":["text (prompt)","model selection parameter (provider and model name)"],"output_types":["text (LLM response)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"snowflake-cortex__headline","uri":"capability://data.processing.analysis.ai.and.ml.platform.for.secure.data.cloud.integration","name":"ai and ml platform for secure data cloud integration","description":"Snowflake Cortex is an integrated AI and ML service that allows users to run foundation models directly within a secure data cloud, offering serverless LLM functions and fine-tuning without moving data outside its governance boundary.","intents":["best AI and ML platform","AI platform for secure data integration","serverless LLM functions for data cloud","ML model deployment in Snowflake","vector search capabilities in data cloud"],"best_for":["organizations needing secure AI solutions","businesses with large datasets"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"low","permissions":["Snowflake account with Standard Edition or higher","Data loaded into Snowflake tables or stages","API credentials for selected LLM provider (Anthropic, OpenAI, etc.) or Snowflake-managed credentials","SQL query execution permissions","Snowflake account with Cortex AI Functions enabled","Media files staged in Snowflake internal/external stages or accessible via URL","Sufficient compute credits for multimodal inference (pricing not detailed)","Snowflake account with observability features enabled","Appropriate role-based access controls to view cost and usage data","Snowflake account with Cortex enabled"],"failure_modes":["No direct model parameter tuning exposed in SQL interface — temperature, max_tokens, etc. configuration scope unknown","Latency characteristics not documented — cold start behavior and per-request overhead unknown","No built-in prompt versioning or A/B testing framework for comparing model outputs","Consumption-based pricing per inference not transparently disclosed — cost per token or per request unknown","Model versions and update cadence not specified — unclear when Claude/GPT-4 versions are refreshed","Supported image/audio formats not specified — unclear which codecs, resolutions, and file sizes are supported","No documentation on preprocessing or normalization — image resizing, audio sampling rates unknown","Multimodal model selection not transparent — unclear which models handle which modalities or how routing works","Batch processing limits for large media files unknown — potential timeout or payload size constraints","No explicit mention of streaming audio or video — appears limited to file-based inputs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:28.695Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=snowflake-cortex","compare_url":"https://unfragile.ai/compare?artifact=snowflake-cortex"}},"signature":"GC1u5WCdbaX0ILnU5/k9gskc9blc8lI8bQlMt2ZMNV+4t+y2SCCK8ibx0Lk+X1HrllVlzRp56v+htsMWS3xpCw==","signedAt":"2026-07-08T05:06:57.925Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/snowflake-cortex","artifact":"https://unfragile.ai/snowflake-cortex","verify":"https://unfragile.ai/api/v1/verify?slug=snowflake-cortex","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}