{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ibm-watsonx-ai","slug":"ibm-watsonx-ai","name":"IBM watsonx.ai","type":"platform","url":"https://www.ibm.com/watsonx","page_url":"https://unfragile.ai/ibm-watsonx-ai","categories":["model-training","code-review-security"],"tags":[],"pricing":{"model":"usage","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ibm-watsonx-ai__cap_0","uri":"capability://tool.use.integration.foundation.model.inference.with.multi.provider.support","name":"foundation-model-inference-with-multi-provider-support","description":"Provides hosted inference endpoints for IBM Granite and open-source Llama foundation models deployed across hybrid multi-cloud infrastructure (IBM Cloud, AWS, Azure, on-premises). Routes requests to optimized model instances with built-in load balancing and supports both synchronous REST API calls and asynchronous batch processing. Abstracts underlying hardware heterogeneity (GPU types, memory configurations) behind a unified inference interface.","intents":["I need to call a foundation model API without managing infrastructure or GPU allocation","I want to run the same model inference code across different cloud providers without refactoring","I need to deploy models in a hybrid environment with on-premises data centers and public clouds","I want to avoid vendor lock-in by using open-source models (Llama) alongside proprietary ones (Granite)"],"best_for":["Enterprise teams with multi-cloud strategies and hybrid data residency requirements","Organizations needing to keep sensitive data on-premises while leveraging cloud inference","Teams evaluating model performance across different hardware without infrastructure overhead"],"limitations":["No published SLAs or latency guarantees for inference endpoints","Pricing model not disclosed — unable to estimate per-request or per-token costs","Hardware specifications (GPU types, memory tiers, auto-scaling behavior) not publicly documented","Model catalog size and versioning scheme not specified — unclear how many Granite/Llama variants available","Cold-start latency and warm-pool management strategies not disclosed"],"requires":["IBM watsonx.ai account with API credentials","Network access to IBM Cloud or hybrid cloud infrastructure","Model selection from available Granite or Llama variants (specific versions unknown)"],"input_types":["text prompts","structured JSON payloads","batch datasets (format unspecified)"],"output_types":["text completions","structured JSON responses","token usage metadata"],"categories":["tool-use-integration","deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibm-watsonx-ai__cap_1","uri":"capability://text.generation.language.interactive.prompt.engineering.and.testing.lab","name":"interactive-prompt-engineering-and-testing-lab","description":"Provides a web-based 'Prompt Lab' interface for iterative prompt design, testing, and optimization against live foundation models without writing code. Supports side-by-side prompt comparison, parameter tuning (temperature, max tokens, top-p), and version control of prompt templates. Integrates with the inference API to show real-time model outputs and metrics (latency, token usage). Enables non-technical users and developers to collaborate on prompt refinement before deployment.","intents":["I want to experiment with different prompts and see results immediately without deploying code","I need to compare multiple prompt variations side-by-side to find the best performing one","I want to tune model parameters (temperature, top-p, max tokens) interactively and see the impact","I need to version and track changes to prompts as I iterate on them"],"best_for":["Product teams and non-technical stakeholders prototyping AI features without engineering overhead","Prompt engineers and ML practitioners optimizing prompts for specific use cases","Teams collaborating on prompt design where some members lack coding experience"],"limitations":["No API-level access to Prompt Lab functionality — appears to be UI-only, limiting automation of prompt testing","Prompt versioning and export formats not specified — unclear if prompts can be exported as code/config","Collaboration features (sharing, commenting, approval workflows) not documented","No published benchmarks on how much time this saves vs. manual prompt iteration (the '90% time savings' claim is unverified and marked with an asterisk)"],"requires":["IBM watsonx.ai account with web browser access","Access to at least one foundation model (Granite or Llama)","No coding experience required"],"input_types":["text prompts","model parameter configurations (temperature, top-p, max_tokens, etc.)"],"output_types":["model completions","performance metrics (latency, token count)","prompt templates (export format unknown)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibm-watsonx-ai__cap_10","uri":"capability://memory.knowledge.open.source.foundation.model.library.and.registry","name":"open-source-foundation-model-library-and-registry","description":"Provides curated library of open-source foundation models (Llama variants, potentially others) available for immediate deployment without licensing restrictions. Models are pre-optimized for watsonx.ai infrastructure and available in multiple sizes (small, medium, large — specific model variants unknown). Enables users to avoid vendor lock-in by using open-source models alongside proprietary Granite models. Supports model discovery via searchable registry with model cards documenting capabilities, limitations, and performance characteristics.","intents":["I want to use open-source models to avoid vendor lock-in with proprietary foundation models","I need to find a model that fits my use case by browsing model cards and performance benchmarks","I want to compare open-source and proprietary models on the same platform","I need to deploy a model with no licensing restrictions or usage limitations"],"best_for":["Organizations prioritizing vendor independence and open-source software","Teams evaluating multiple models before committing to a specific provider","Developers building on open-source models with no commercial licensing concerns"],"limitations":["Specific open-source models available not enumerated — only Llama mentioned, others unknown","Model card standards and available metadata not documented","Model size variants and performance characteristics not specified","Licensing terms for open-source models not detailed (Llama is Meta's license, others unknown)","Model update frequency and version management not disclosed","Community contribution mechanisms for new models not mentioned","Performance benchmarks and comparison data not provided"],"requires":["IBM watsonx.ai account","No additional licensing or API keys for open-source models"],"input_types":["model search queries","model selection criteria"],"output_types":["model registry with searchable results","model cards with metadata and performance data","deployed model endpoints"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibm-watsonx-ai__cap_11","uri":"capability://planning.reasoning.multi.model.ensemble.and.routing.orchestration","name":"multi-model-ensemble-and-routing-orchestration","description":"Enables creation of ensemble models that combine predictions from multiple foundation models, custom models, or fine-tuned variants. Supports routing logic to direct requests to different models based on input characteristics (query type, domain, complexity — routing criteria not documented). Implements ensemble aggregation strategies (voting, weighted averaging, stacking — strategies not specified). Manages ensemble versioning and A/B testing. Integrates with monitoring to track ensemble performance vs. individual models.","intents":["I want to combine predictions from multiple models to improve accuracy and robustness","I need to route different types of requests to specialized models (e.g., code generation vs. text summarization)","I want to compare ensemble performance against individual models to justify the added complexity","I need to gradually migrate from one model to another using weighted routing"],"best_for":["Teams optimizing for accuracy and robustness by combining multiple models","Organizations with specialized models for different domains wanting to route intelligently","ML teams experimenting with ensemble methods without managing infrastructure"],"limitations":["Supported ensemble aggregation strategies (voting, averaging, stacking, etc.) not documented","Routing logic configuration options and decision criteria not specified","Latency impact of ensemble inference not discussed — combining multiple models increases latency","Cost implications of ensemble inference not disclosed","Ensemble versioning and rollback capabilities not detailed","Custom routing logic and conditional model selection not mentioned","Ensemble performance monitoring and comparison methodology not described"],"requires":["IBM watsonx.ai account with ensemble capabilities","Multiple models deployed (foundation models, custom models, or fine-tuned variants)","Ensemble configuration (model selection, routing logic, aggregation strategy)"],"input_types":["inference requests","ensemble configuration (models, routing rules, aggregation strategy)"],"output_types":["ensemble predictions","individual model predictions (for debugging)","ensemble confidence scores"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibm-watsonx-ai__cap_2","uri":"capability://code.generation.editing.model.fine.tuning.and.adaptation.studio","name":"model-fine-tuning-and-adaptation-studio","description":"Provides 'Tuning Studio' interface for fine-tuning foundation models (Granite, Llama) on custom datasets without managing training infrastructure. Abstracts distributed training, gradient accumulation, and checkpoint management behind a UI-driven workflow. Supports parameter-efficient tuning methods (LoRA, QLoRA, or similar — not explicitly documented) to reduce compute costs. Outputs fine-tuned model artifacts that can be deployed as custom inference endpoints. Integrates with data preparation tools and tracks training metrics (loss, validation accuracy).","intents":["I want to adapt a foundation model to my domain-specific data without managing GPU clusters or training code","I need to fine-tune a model on proprietary data while keeping that data on-premises or in a private cloud","I want to reduce fine-tuning costs by using parameter-efficient methods instead of full model retraining","I need to version and compare multiple fine-tuned models to find the best one for my use case"],"best_for":["Enterprise teams with domain-specific use cases (legal, medical, financial) needing model customization","Organizations with proprietary data that cannot be sent to third-party fine-tuning services","Teams lacking ML infrastructure expertise but needing to adapt models to custom tasks"],"limitations":["Fine-tuning methods (LoRA, QLoRA, full fine-tuning) not specified — unclear which techniques are supported","Training time and cost estimates not provided — no guidance on expected duration or pricing","Data format requirements and maximum dataset sizes not documented","No information on distributed training support, multi-GPU orchestration, or training parallelization","Model artifact export formats and compatibility with external serving frameworks (TensorFlow, PyTorch, ONNX) not specified","Hyperparameter search and automated tuning capabilities not mentioned"],"requires":["IBM watsonx.ai account with Tuning Studio access","Custom training dataset (format and size limits unknown)","Base model selection from available Granite or Llama variants","Compute quota or credits for training (pricing unknown)"],"input_types":["structured training datasets (CSV, JSON, Parquet — formats unspecified)","text corpora","labeled examples for supervised fine-tuning"],"output_types":["fine-tuned model artifacts","training metrics and logs","deployable model endpoints"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibm-watsonx-ai__cap_3","uri":"capability://safety.moderation.enterprise.audit.trail.and.governance.logging","name":"enterprise-audit-trail-and-governance-logging","description":"Tracks all model inference requests, fine-tuning jobs, and prompt modifications with immutable audit logs including user identity, timestamp, model version, input/output, and parameters. Integrates with enterprise identity providers (LDAP, SAML, OAuth) for access control. Supports compliance reporting for regulatory frameworks (HIPAA, GDPR, SOC2 — frameworks not explicitly confirmed). Enables role-based access control (RBAC) to restrict who can deploy, modify, or invoke models. Logs are retained for configurable periods and queryable via governance dashboard.","intents":["I need to prove to auditors that all AI model usage is logged and traceable to specific users","I want to restrict access to sensitive models so only authorized teams can invoke them","I need to generate compliance reports showing model lineage, data provenance, and access patterns","I want to detect and investigate suspicious model usage patterns or unauthorized access attempts"],"best_for":["Regulated industries (healthcare, finance, legal) with mandatory audit and compliance requirements","Enterprise security teams implementing zero-trust access control for AI systems","Organizations subject to GDPR, HIPAA, or SOC2 compliance mandates"],"limitations":["Specific compliance frameworks supported (HIPAA, GDPR, SOC2, PCI-DSS, etc.) not documented","Audit log schema and queryable fields not specified — unclear what metadata is captured","Log retention policies and archival mechanisms not disclosed","RBAC model details (predefined roles, custom role creation, permission granularity) not documented","Integration with external SIEM or log aggregation platforms (Splunk, ELK, Datadog) not mentioned","Data encryption for audit logs (in-transit, at-rest) not specified","Audit log immutability guarantees and tamper-detection mechanisms not described"],"requires":["IBM watsonx.ai account with governance features enabled","Enterprise identity provider (LDAP, SAML, OAuth) for SSO integration","Compliance framework requirements (HIPAA, GDPR, etc.) defined by organization"],"input_types":["model inference requests","fine-tuning job configurations","prompt modifications","user access requests"],"output_types":["audit logs (JSON or structured format — unspecified)","compliance reports","access control decisions (allow/deny)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibm-watsonx-ai__cap_4","uri":"capability://safety.moderation.bias.detection.and.responsible.ai.monitoring","name":"bias-detection-and-responsible-ai-monitoring","description":"Analyzes model outputs and training data for statistical bias across demographic groups (gender, race, age, etc.) using fairness metrics (disparate impact, demographic parity, equalized odds — specific metrics not documented). Flags potentially biased predictions during inference and fine-tuning. Provides dashboards showing bias metrics over time and across model versions. Integrates with governance workflows to require human review of high-bias predictions before deployment. Supports custom fairness definitions and thresholds.","intents":["I need to detect and measure bias in my model's predictions across demographic groups before deploying to production","I want to understand how fine-tuning on my custom data affects model fairness compared to the base model","I need to comply with fairness requirements in regulated domains (hiring, lending, criminal justice)","I want to set up automated alerts when model bias exceeds acceptable thresholds"],"best_for":["Teams building AI systems for high-stakes decisions (hiring, lending, criminal justice, healthcare)","Organizations with fairness and responsible AI mandates","Compliance teams needing to document fairness testing for regulatory audits"],"limitations":["Specific fairness metrics supported (disparate impact, demographic parity, equalized odds, calibration, etc.) not enumerated","Bias detection methodology not documented — unclear if it uses statistical tests, causal inference, or other approaches","Demographic attribute detection method not specified — unclear how system identifies protected attributes in data","Custom fairness definition capabilities not detailed","Integration with model retraining workflows to mitigate detected bias not described","No published benchmarks on bias detection accuracy or false-positive rates","Limitations of fairness metrics (e.g., impossibility of satisfying multiple fairness criteria simultaneously) not acknowledged"],"requires":["IBM watsonx.ai account with bias detection module enabled","Training data or inference logs with demographic attributes (format unspecified)","Definition of protected attributes and fairness criteria for your domain"],"input_types":["model predictions and ground truth labels","demographic attributes (gender, race, age, etc.)","training data samples"],"output_types":["fairness metrics (disparate impact, demographic parity, etc. — specific metrics unknown)","bias reports and dashboards","alerts for high-bias predictions"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibm-watsonx-ai__cap_5","uri":"capability://automation.workflow.hybrid.cloud.model.deployment.and.orchestration","name":"hybrid-cloud-model-deployment-and-orchestration","description":"Enables deployment of models across heterogeneous infrastructure: IBM Cloud, AWS, Azure, and on-premises data centers. Abstracts cloud-specific APIs and container orchestration (Kubernetes, OpenShift) behind a unified deployment interface. Supports model routing and load balancing across deployment targets based on latency, cost, or data residency constraints. Manages model versioning, canary deployments, and rollback across all targets. Integrates with IBM Red Hat OpenShift for on-premises Kubernetes orchestration.","intents":["I need to deploy the same model across multiple clouds and on-premises without managing separate deployment pipelines","I want to keep sensitive data on-premises while using public cloud for non-sensitive workloads","I need to route inference requests to the nearest or cheapest deployment target based on latency or cost","I want to perform canary deployments and A/B testing across different cloud providers"],"best_for":["Enterprise organizations with multi-cloud strategies and hybrid infrastructure","Teams with data residency requirements preventing cloud-only deployments","Organizations optimizing for cost by distributing workloads across providers with different pricing"],"limitations":["Supported cloud providers not explicitly enumerated — documentation mentions 'any cloud' but specific integrations unknown","Model routing policies and decision logic not documented — unclear how system chooses deployment target","Load balancing algorithms and failover behavior not specified","Canary deployment configuration options and rollback triggers not detailed","Network latency and data transfer costs between deployment targets not discussed","Kubernetes version requirements and OpenShift compatibility matrix not provided","Data synchronization and consistency guarantees across distributed deployments not specified"],"requires":["IBM watsonx.ai account with multi-cloud deployment enabled","Kubernetes cluster or IBM Red Hat OpenShift for on-premises deployment","Network connectivity between deployment targets","Cloud provider accounts (AWS, Azure, IBM Cloud) with appropriate credentials"],"input_types":["model artifacts","deployment configuration (target cloud, resource requirements, routing policies — format unspecified)","inference requests"],"output_types":["deployed model endpoints","deployment status and health metrics","routing decisions and load distribution"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibm-watsonx-ai__cap_6","uri":"capability://memory.knowledge.data.governance.and.lineage.tracking","name":"data-governance-and-lineage-tracking","description":"Tracks data provenance and lineage for training datasets, fine-tuning data, and inference inputs through the model lifecycle. Records which datasets were used to train or fine-tune each model version, enabling traceability from predictions back to source data. Integrates with IBM Data Platform for metadata management and data cataloging. Supports data classification (sensitive, public, restricted) and enforces access controls based on data sensitivity. Enables compliance teams to demonstrate data governance for regulatory audits.","intents":["I need to trace which training data was used for each model version to understand model behavior","I want to identify all models trained on a specific dataset if that data is discovered to be biased or compromised","I need to classify data by sensitivity level and restrict access to sensitive training datasets","I want to prove to auditors that all training data was properly vetted and documented"],"best_for":["Regulated industries (healthcare, finance) with strict data governance requirements","Teams managing large numbers of models and datasets needing to track dependencies","Organizations implementing data catalogs and metadata management"],"limitations":["Integration with IBM Data Platform not detailed — unclear what metadata is captured or how it's queried","Data classification schema and custom classification support not documented","Lineage tracking granularity not specified — unclear if lineage is tracked at dataset, file, or record level","Data retention and archival policies for lineage metadata not disclosed","Integration with external data catalogs (Apache Atlas, Collibra, Alation) not mentioned","Performance impact of lineage tracking on training and inference not discussed","Data lineage visualization and query capabilities not described"],"requires":["IBM watsonx.ai account with data governance features","IBM Data Platform integration (or compatible metadata store)","Data classification policies defined by organization"],"input_types":["training datasets","fine-tuning data","inference inputs","data metadata (source, owner, sensitivity level)"],"output_types":["data lineage graphs","dataset-to-model mappings","data governance reports","access control decisions"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibm-watsonx-ai__cap_7","uri":"capability://tool.use.integration.bring.your.own.model.deployment.and.serving","name":"bring-your-own-model-deployment-and-serving","description":"Supports deployment of custom models trained outside watsonx.ai (PyTorch, TensorFlow, ONNX, scikit-learn — specific frameworks not confirmed) as inference endpoints. Abstracts model format conversion and containerization behind a managed service. Supports model artifacts in standard formats (ONNX, SavedModel, pickle — formats not explicitly documented). Enables versioning and A/B testing of custom models alongside foundation models. Integrates with CI/CD pipelines for automated model deployment.","intents":["I want to deploy my existing PyTorch or TensorFlow models on watsonx.ai without rewriting them","I need to serve custom models alongside foundation models in a unified platform","I want to perform A/B testing between my custom model and a foundation model","I need to automate model deployment from my CI/CD pipeline to watsonx.ai"],"best_for":["Teams with existing ML models wanting to consolidate on a single serving platform","Organizations comparing custom models against foundation models for specific tasks","ML teams with established CI/CD workflows needing to integrate model deployment"],"limitations":["Supported model frameworks and formats not enumerated — unclear if PyTorch, TensorFlow, ONNX, scikit-learn are all supported","Model artifact size limits and containerization requirements not specified","Custom dependencies and Python package management not documented","Model format conversion and optimization (quantization, pruning) capabilities not mentioned","Integration with CI/CD platforms (GitHub Actions, GitLab CI, Jenkins) not detailed","Model health monitoring and automatic rollback on inference failures not described","Support for GPU-accelerated inference for custom models not confirmed"],"requires":["IBM watsonx.ai account with custom model deployment enabled","Model artifact in supported format (frameworks and formats unknown)","Container registry access or model artifact upload capability","CI/CD pipeline integration (optional, for automated deployment)"],"input_types":["model artifacts (PyTorch, TensorFlow, ONNX, scikit-learn — unconfirmed)","model metadata (name, version, input/output schema)","inference requests"],"output_types":["deployed model endpoints","inference results","model performance metrics"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibm-watsonx-ai__cap_8","uri":"capability://data.processing.analysis.batch.inference.and.asynchronous.processing","name":"batch-inference-and-asynchronous-processing","description":"Supports asynchronous batch inference for processing large datasets without requiring real-time API calls. Accepts batch job submissions with input datasets (CSV, JSON, Parquet — formats unspecified) and returns results asynchronously. Abstracts distributed batch processing across multiple workers. Integrates with object storage (IBM Cloud Object Storage, S3 — unconfirmed) for input/output data. Provides job status tracking and result retrieval via API or dashboard.","intents":["I need to process millions of records through a model without making individual API calls","I want to run inference on historical data for analysis or model evaluation","I need to reduce costs by batching inference requests instead of real-time processing","I want to integrate model inference into data pipelines for ETL workflows"],"best_for":["Data science teams processing large datasets for analysis or model evaluation","Organizations optimizing inference costs by batching requests","Data engineering teams integrating model inference into ETL pipelines"],"limitations":["Supported input/output formats (CSV, JSON, Parquet, etc.) not enumerated","Maximum batch size and dataset size limits not specified","Batch processing latency and throughput not documented","Integration with object storage services (S3, Azure Blob, GCS) not detailed","Distributed batch processing architecture and parallelization strategy not described","Error handling and partial failure recovery not documented","Cost model for batch processing not disclosed — unclear if cheaper than real-time inference"],"requires":["IBM watsonx.ai account with batch inference enabled","Input dataset in supported format (formats unknown)","Object storage access for input/output data (service unknown)"],"input_types":["batch datasets (CSV, JSON, Parquet — unconfirmed)","batch job configuration (model selection, parameters)"],"output_types":["batch results (format matching input — unspecified)","job status and metadata","error logs for failed records"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibm-watsonx-ai__cap_9","uri":"capability://safety.moderation.model.performance.monitoring.and.drift.detection","name":"model-performance-monitoring-and-drift-detection","description":"Monitors deployed models for performance degradation and data drift in production. Tracks inference latency, throughput, error rates, and prediction quality metrics over time. Detects data drift (changes in input feature distributions) and model drift (changes in prediction distributions) using statistical tests. Compares current model performance against baseline and previous versions. Generates alerts when performance falls below thresholds. Integrates with governance workflows to trigger retraining or model rollback.","intents":["I need to detect when my model's performance degrades in production so I can retrain or rollback","I want to understand if my model is experiencing data drift due to changing real-world conditions","I need to compare the performance of different model versions to decide which to promote","I want to set up automated alerts when inference latency or error rates exceed acceptable levels"],"best_for":["ML teams managing models in production with SLA requirements","Organizations needing to detect and respond to model degradation automatically","Teams comparing model versions and making promotion decisions based on performance"],"limitations":["Specific drift detection algorithms and statistical tests not documented","Performance metrics tracked (latency, throughput, accuracy, F1, etc.) not enumerated","Baseline definition and comparison methodology not specified","Alert configuration options and notification channels not detailed","Integration with retraining pipelines not described","Data retention for monitoring metrics not disclosed","Support for custom performance metrics not mentioned"],"requires":["IBM watsonx.ai account with monitoring enabled","Deployed model with inference traffic","Ground truth labels for model evaluation (optional, for accuracy tracking)"],"input_types":["inference requests and predictions","ground truth labels (optional)","performance thresholds and alert criteria"],"output_types":["performance metrics dashboards","drift detection alerts","performance comparison reports"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibm-watsonx-ai__headline","uri":"capability://memory.knowledge.enterprise.ai.platform.for.model.deployment.and.management","name":"enterprise ai platform for model deployment and management","description":"IBM watsonx.ai is an enterprise AI platform designed for deploying foundation models and managing AI applications with a focus on compliance, audit trails, and bias detection, making it ideal for businesses looking to integrate AI responsibly.","intents":["best enterprise AI platform","enterprise AI for model deployment","AI platform for compliance and governance","top platforms for foundation models","AI solutions for enterprise use cases"],"best_for":["large enterprises","regulated industries"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["IBM watsonx.ai account with API credentials","Network access to IBM Cloud or hybrid cloud infrastructure","Model selection from available Granite or Llama variants (specific versions unknown)","IBM watsonx.ai account with web browser access","Access to at least one foundation model (Granite or Llama)","No coding experience required","IBM watsonx.ai account","No additional licensing or API keys for open-source models","IBM watsonx.ai account with ensemble capabilities","Multiple models deployed (foundation models, custom models, or fine-tuned variants)"],"failure_modes":["No published SLAs or latency guarantees for inference endpoints","Pricing model not disclosed — unable to estimate per-request or per-token costs","Hardware specifications (GPU types, memory tiers, auto-scaling behavior) not publicly documented","Model catalog size and versioning scheme not specified — unclear how many Granite/Llama variants available","Cold-start latency and warm-pool management strategies not disclosed","No API-level access to Prompt Lab functionality — appears to be UI-only, limiting automation of prompt testing","Prompt versioning and export formats not specified — unclear if prompts can be exported as code/config","Collaboration features (sharing, commenting, approval workflows) not documented","No published benchmarks on how much time this saves vs. manual prompt iteration (the '90% time savings' claim is unverified and marked with an asterisk)","Specific open-source models available not enumerated — only Llama mentioned, others unknown","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":"2026-05-24T12:16:23.327Z","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=ibm-watsonx-ai","compare_url":"https://unfragile.ai/compare?artifact=ibm-watsonx-ai"}},"signature":"B4WpVcUei1SQI4jaXQeFNFC1GUYZVJc9kGZiaWqwmT4SEzZwNXs2joEh8hDJprtXxtN3W4eU82t3AHInf4NXAA==","signedAt":"2026-06-21T21:29:45.062Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ibm-watsonx-ai","artifact":"https://unfragile.ai/ibm-watsonx-ai","verify":"https://unfragile.ai/api/v1/verify?slug=ibm-watsonx-ai","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"}}