{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_cleargpt","slug":"cleargpt","name":"ClearGPT","type":"product","url":"https://cleargpt.ai","page_url":"https://unfragile.ai/cleargpt","categories":["app-builders"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_cleargpt__cap_0","uri":"capability://safety.moderation.data.residency.compliant.generative.ai.inference","name":"data-residency-compliant generative ai inference","description":"Executes LLM inference with guaranteed data residency constraints, routing requests to geographically isolated compute clusters based on regulatory jurisdiction requirements. Implements request-level data governance policies that prevent model weights, training data, or inference logs from crossing specified geographic boundaries, with audit logging at the network layer to verify compliance.","intents":["I need to run AI models on sensitive healthcare data without it leaving the EU for GDPR compliance","Our finance team requires proof that customer data never touches shared infrastructure during inference","We need to demonstrate to auditors that our AI system respects data residency mandates in regulated markets"],"best_for":["Healthcare organizations subject to HIPAA with multi-region patient data","Financial services firms operating under regional data sovereignty laws","Legal tech companies handling attorney-client privileged information across jurisdictions"],"limitations":["Geographic isolation adds 50-200ms latency for cross-region failover scenarios","Requires pre-negotiated data residency agreements with ClearGPT — cannot be dynamically configured per request","Limited to regions where ClearGPT maintains dedicated infrastructure (specific geographies not publicly documented)"],"requires":["Enterprise contract with ClearGPT specifying data residency zones","Network connectivity to designated regional endpoints","Compliance framework documentation (HIPAA, GDPR, or equivalent) for audit purposes"],"input_types":["text (unstructured documents, queries)","structured data (JSON, CSV with PII fields)"],"output_types":["text (model completions with residency metadata)","audit logs (compliance verification records)"],"categories":["safety-moderation","compliance-governance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleargpt__cap_1","uri":"capability://code.generation.editing.domain.specific.model.fine.tuning.with.regulatory.aware.tokenization","name":"domain-specific model fine-tuning with regulatory-aware tokenization","description":"Accepts domain-specific training datasets (legal contracts, medical records, financial documents) and performs supervised fine-tuning on base models with custom tokenizers that preserve regulatory-sensitive entities (medical codes, legal citations, ticker symbols). Uses domain-aware vocabulary expansion and entity masking during training to prevent model overfitting on sensitive identifiers while maintaining domain-specific reasoning capabilities.","intents":["I want to fine-tune a model on our proprietary legal case law without exposing client names or case numbers in the model weights","Our healthcare team needs a model that understands ICD-10 codes and medical terminology without memorizing patient records","We need to adapt an LLM to our financial domain while ensuring it doesn't leak ticker symbols or proprietary trading strategies"],"best_for":["Legal firms with large case databases seeking domain-specific reasoning without data leakage","Healthcare systems wanting to fine-tune on EHR data while maintaining HIPAA compliance","Financial institutions building compliance-aware trading or risk analysis models"],"limitations":["Fine-tuning requires minimum dataset size (not publicly specified) to achieve meaningful domain adaptation","Custom tokenizer training adds 2-4 week turnaround time before model deployment","No incremental fine-tuning support — each new dataset version requires full retraining cycle","Regulatory-aware masking may reduce model accuracy on edge cases where sensitive entities are semantically important"],"requires":["Minimum 10,000-100,000 domain-specific training examples (exact threshold undocumented)","Data preprocessing pipeline to anonymize PII before upload","Domain taxonomy or entity classification schema (provided by customer or ClearGPT)","2-4 week lead time for fine-tuning and validation"],"input_types":["text documents (legal contracts, medical notes, financial reports)","structured metadata (entity labels, domain classifications)","domain taxonomy files (JSON or CSV)"],"output_types":["fine-tuned model weights (proprietary format)","domain-specific tokenizer","performance benchmarks on held-out test set"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleargpt__cap_2","uri":"capability://automation.workflow.on.premise.and.private.cloud.deployment.orchestration","name":"on-premise and private-cloud deployment orchestration","description":"Manages containerized model deployment to customer-controlled infrastructure (on-premise data centers, private cloud VPCs) with automated provisioning, scaling, and lifecycle management. Handles model weight distribution, inference server configuration, and monitoring across heterogeneous hardware (GPUs, TPUs, CPUs) with no data transmission to ClearGPT's public infrastructure. Includes air-gapped deployment mode for fully isolated networks with manual model updates.","intents":["We need to run ClearGPT models entirely within our corporate data center with zero external API calls","Our compliance team requires air-gapped deployment where model updates are manually staged and verified before deployment","We want to scale inference across our existing Kubernetes cluster without vendor lock-in to cloud providers"],"best_for":["Large enterprises with existing on-premise infrastructure and strict data sovereignty requirements","Government agencies and defense contractors requiring air-gapped AI systems","Organizations with heterogeneous hardware environments (mixed GPU/CPU clusters) seeking unified deployment"],"limitations":["On-premise deployment requires dedicated DevOps resources for infrastructure management and monitoring","Air-gapped mode requires manual model update procedures — no automatic security patches or model improvements","Scaling beyond initial hardware capacity requires customer-side infrastructure expansion (no auto-scaling to cloud)","Support response times for on-premise issues typically 24-48 hours vs real-time for cloud deployments"],"requires":["Kubernetes 1.20+ or Docker Swarm for container orchestration","Minimum 8 GPU cores (NVIDIA A100/H100 or equivalent) for production inference","Network isolation capability (air-gapped deployments require manual model staging)","Dedicated on-premise storage (minimum 500GB for model weights and inference logs)","Enterprise support contract with ClearGPT for deployment assistance"],"input_types":["model configuration files (YAML)","hardware specifications (GPU/CPU inventory)","network topology diagrams"],"output_types":["containerized inference servers (Docker images)","Kubernetes manifests or Docker Compose files","deployment runbooks and monitoring dashboards"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleargpt__cap_3","uri":"capability://safety.moderation.compliance.audit.trail.and.inference.logging.with.immutable.records","name":"compliance audit trail and inference logging with immutable records","description":"Captures and stores immutable audit logs for every inference request, including input prompts, model outputs, latency metrics, and data residency verification. Implements append-only logging architecture (similar to blockchain-style ledgers) where logs cannot be retroactively modified, with cryptographic hashing to detect tampering. Provides query interfaces for compliance teams to retrieve logs by date range, user, data classification level, or regulatory requirement (HIPAA, SOC 2, etc.).","intents":["Our auditors need to verify that all AI inferences on patient data were logged and that no logs were deleted or modified","We need to demonstrate to regulators that our AI system maintains a complete chain of custody for sensitive financial decisions","Our compliance team requires monthly reports showing which users accessed which models and what data was processed"],"best_for":["Healthcare organizations undergoing HIPAA audits requiring complete inference audit trails","Financial services firms subject to SEC or FINRA regulations on algorithmic decision-making","Legal tech companies needing to prove attorney-client privilege was maintained during AI-assisted document review"],"limitations":["Immutable logging adds 10-50ms latency per inference request due to cryptographic hashing and append-only writes","Audit log storage grows rapidly (estimated 1-10MB per 1000 inferences depending on prompt/output size) — requires dedicated storage infrastructure","Log retention policies are fixed per contract (typically 3-7 years) — cannot be dynamically adjusted without contract amendment","Query performance degrades with log volume — queries across 1M+ inferences may take 30+ seconds"],"requires":["Dedicated audit log storage (minimum 1TB for typical enterprise usage)","Compliance team trained on log query interface and interpretation","Regular log backup and archival procedures (customer responsibility)","Integration with SIEM or compliance management platform (optional but recommended)"],"input_types":["inference requests (prompts, model parameters)","user identity and authorization context","data classification labels"],"output_types":["immutable audit logs (JSON with cryptographic signatures)","compliance reports (CSV, PDF)","log query results (filtered by date, user, data type)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleargpt__cap_4","uri":"capability://safety.moderation.custom.content.filtering.and.guardrails.with.domain.specific.policies","name":"custom content filtering and guardrails with domain-specific policies","description":"Allows enterprises to define custom content policies (e.g., 'block outputs containing medical diagnoses without physician review', 'redact financial ticker symbols from responses') and enforces them at the output layer before returning results to users. Policies are defined as rule sets combining pattern matching (regex), semantic similarity (embeddings), and domain classifiers, with per-user or per-role policy overrides. Includes dry-run mode to test policies without blocking outputs.","intents":["We need to prevent our AI system from generating medical diagnoses without a physician in the loop, even if the model is capable","Our legal team wants to block outputs containing specific client names or case numbers to prevent accidental disclosure","We need different content policies for different user roles — executives see unfiltered outputs, junior staff see redacted versions"],"best_for":["Healthcare organizations requiring physician-in-the-loop for diagnostic outputs","Legal firms needing to prevent accidental disclosure of client information in AI outputs","Financial services firms with role-based output filtering (traders vs compliance officers)"],"limitations":["Custom policy definition requires domain expertise — no low-code policy builder, requires JSON/YAML configuration","Pattern-matching policies (regex) are brittle and require frequent updates as adversarial users find workarounds","Semantic similarity-based policies add 100-500ms latency per inference due to embedding computation","False positive rate for content filtering typically 5-15% depending on policy complexity — requires manual review of blocked outputs","No built-in A/B testing framework for policy validation before production deployment"],"requires":["Domain expertise to define meaningful content policies","Access to ClearGPT policy configuration interface (API or dashboard)","Optional: embedding model for semantic similarity policies (can use ClearGPT's or bring your own)","Testing environment to validate policies before production rollout"],"input_types":["policy definitions (JSON/YAML with pattern rules, semantic classifiers)","model outputs (text)","user role/context metadata"],"output_types":["filtered outputs (text with redactions or blocks)","policy violation logs (what was blocked and why)","policy effectiveness metrics (false positive rate, coverage)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleargpt__cap_5","uri":"capability://planning.reasoning.multi.model.orchestration.with.automatic.model.selection.based.on.task.classification","name":"multi-model orchestration with automatic model selection based on task classification","description":"Routes inference requests to different fine-tuned models based on automatic task classification (e.g., 'legal document review' → legal-specialized model, 'medical coding' → healthcare-specialized model). Uses a classifier layer that analyzes input prompts and metadata to determine optimal model, with fallback to general-purpose model if task is ambiguous. Supports A/B testing across models and gradual traffic shifting for model updates.","intents":["We have multiple fine-tuned models for different departments — we need automatic routing so users don't have to specify which model to use","We want to gradually roll out a new legal model to 10% of traffic while keeping 90% on the stable version","We need to A/B test two versions of our healthcare model to measure which one produces better clinical outcomes"],"best_for":["Large enterprises with multiple domain-specific models (legal, healthcare, finance) seeking unified inference interface","Organizations running continuous model improvement cycles with staged rollouts","Teams needing to measure model performance differences across user cohorts"],"limitations":["Task classification adds 50-200ms latency per request due to classifier inference overhead","Misclassification of tasks (e.g., legal query routed to healthcare model) can degrade output quality — requires careful classifier tuning","A/B testing requires statistical significance testing infrastructure — no built-in power analysis or sample size calculation","Model selection logic is opaque to end users — difficult to debug why a particular model was selected for a request"],"requires":["Multiple fine-tuned models deployed and ready for inference","Task classifier model (provided by ClearGPT or customer-trained)","Monitoring infrastructure to track model selection distribution and performance metrics","Statistical analysis capability for A/B test results"],"input_types":["inference requests (prompts, metadata tags)","model routing policies (JSON configuration)","A/B test definitions (traffic split percentages, duration)"],"output_types":["routed inference results (with model selection metadata)","A/B test reports (performance metrics by model variant)","model selection logs (which model was chosen and why)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleargpt__cap_6","uri":"capability://safety.moderation.pii.detection.and.redaction.with.domain.specific.entity.recognition","name":"pii detection and redaction with domain-specific entity recognition","description":"Detects personally identifiable information (PII) in both input prompts and model outputs using domain-specific entity recognition models (medical record numbers, social security numbers, credit card numbers, legal case identifiers). Redacts detected PII before sending to model (for inputs) or before returning to user (for outputs), with configurable redaction strategies (masking, hashing, removal). Maintains a redaction map to enable downstream systems to re-identify data if needed.","intents":["We need to automatically redact patient names and medical record numbers from prompts before they reach the model","Our compliance team requires proof that no credit card numbers or SSNs appear in model outputs, even if they were in the training data","We want to redact PII but still allow downstream systems to re-identify records for audit purposes"],"best_for":["Healthcare organizations processing EHR data through AI systems","Financial services firms handling customer PII in AI-assisted customer service","Legal tech companies processing documents with attorney names, client information, and case numbers"],"limitations":["Entity recognition accuracy varies by PII type — medical record numbers detected at 95%+ accuracy, but context-dependent PII (names in legal documents) at 70-85%","Redaction can degrade model output quality if PII is semantically important (e.g., redacting patient name makes clinical notes less coherent)","Redaction map storage and management adds operational complexity — requires secure key management for re-identification","False positives (incorrectly flagging non-PII as PII) can block legitimate outputs — requires manual review process"],"requires":["Domain-specific entity recognition models (provided by ClearGPT for healthcare, finance, legal)","Secure storage for redaction maps (customer-managed or ClearGPT-managed)","Configuration of redaction strategies per PII type","Manual review process for false positives"],"input_types":["text documents (prompts, model outputs)","PII type definitions (regex patterns, entity classifiers)","redaction strategy configuration (JSON)"],"output_types":["redacted text (with PII removed or masked)","redaction logs (what was redacted and where)","redaction maps (for re-identification if needed)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleargpt__cap_7","uri":"capability://safety.moderation.role.based.access.control.with.granular.permission.management","name":"role-based access control with granular permission management","description":"Enforces fine-grained access control at the model, dataset, and inference level based on user roles and attributes. Supports role hierarchies (admin > manager > user), attribute-based access control (ABAC) with custom attributes (department, clearance level, project), and time-based access restrictions. Integrates with enterprise identity providers (LDAP, SAML, OAuth 2.0) for centralized user management. Logs all access attempts (successful and failed) for audit purposes.","intents":["We need to restrict access to our healthcare model to only physicians and nurses, not administrative staff","Our finance team needs different access levels — traders can use the trading model, but compliance officers can only use the risk model","We need to grant temporary access to a consultant for a specific project, then automatically revoke access after the project ends"],"best_for":["Large enterprises with complex organizational hierarchies and strict access control requirements","Healthcare organizations with role-based access control mandated by HIPAA","Financial services firms with segregation of duties requirements (traders vs compliance)"],"limitations":["RBAC configuration is complex and error-prone — requires careful planning of role hierarchies and attribute mappings","Integration with enterprise identity providers (LDAP, SAML) requires IT infrastructure support","Time-based access restrictions require synchronized clocks across systems — clock skew can cause access denial","No built-in workflow for access request/approval — requires integration with external ticketing systems"],"requires":["Enterprise identity provider (LDAP, SAML, OAuth 2.0 compatible)","Role and attribute definitions (custom schema)","Access control policy configuration (JSON or YAML)","Integration with ClearGPT's RBAC API or dashboard"],"input_types":["user identity and attributes (from identity provider)","role definitions (JSON/YAML)","access control policies (JSON/YAML)","time-based access restrictions (ISO 8601 date ranges)"],"output_types":["access decision (allow/deny with reason)","access logs (successful and failed attempts)","audit reports (who accessed what and when)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleargpt__cap_8","uri":"capability://automation.workflow.inference.cost.tracking.and.budget.enforcement.with.per.user.quotas","name":"inference cost tracking and budget enforcement with per-user quotas","description":"Tracks inference costs in real-time based on model type, input/output token count, and compute resources used. Enforces per-user, per-department, and per-project budget quotas with configurable enforcement strategies (hard limit blocks requests, soft limit triggers alerts). Provides cost dashboards and detailed billing reports broken down by model, user, department, and time period. Supports cost allocation across cost centers for chargeback accounting.","intents":["We need to prevent individual users from running expensive inference jobs that exceed their monthly budget","Our finance team needs to allocate AI costs across departments and charge them back to project budgets","We want to identify which teams are using the most expensive models and optimize their usage"],"best_for":["Large enterprises with multiple departments and cost center accounting requirements","Organizations running cost-sensitive inference workloads (high-volume customer service, batch processing)","Teams needing to optimize AI spending and identify cost-saving opportunities"],"limitations":["Cost tracking adds minimal latency (<10ms) but requires real-time billing infrastructure","Hard budget limits can disrupt critical workflows — requires careful quota planning and monitoring","Cost allocation across cost centers requires manual configuration and maintenance","Billing accuracy depends on accurate token counting — edge cases (special tokens, multi-modal inputs) may be miscounted"],"requires":["Cost model definition (price per token, per model, per compute resource)","Budget quota configuration (per user, per department, per project)","Integration with enterprise accounting/billing system (optional but recommended)","Monitoring and alerting infrastructure for quota violations"],"input_types":["inference requests (token counts, model type, compute resources)","budget quota definitions (JSON/YAML)","cost allocation rules (cost center mappings)"],"output_types":["cost tracking logs (per-request cost breakdown)","billing reports (CSV, PDF, JSON)","cost dashboards (real-time cost visualization)","quota violation alerts (email, webhook)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":37,"verified":false,"data_access_risk":"high","permissions":["Enterprise contract with ClearGPT specifying data residency zones","Network connectivity to designated regional endpoints","Compliance framework documentation (HIPAA, GDPR, or equivalent) for audit purposes","Minimum 10,000-100,000 domain-specific training examples (exact threshold undocumented)","Data preprocessing pipeline to anonymize PII before upload","Domain taxonomy or entity classification schema (provided by customer or ClearGPT)","2-4 week lead time for fine-tuning and validation","Kubernetes 1.20+ or Docker Swarm for container orchestration","Minimum 8 GPU cores (NVIDIA A100/H100 or equivalent) for production inference","Network isolation capability (air-gapped deployments require manual model staging)"],"failure_modes":["Geographic isolation adds 50-200ms latency for cross-region failover scenarios","Requires pre-negotiated data residency agreements with ClearGPT — cannot be dynamically configured per request","Limited to regions where ClearGPT maintains dedicated infrastructure (specific geographies not publicly documented)","Fine-tuning requires minimum dataset size (not publicly specified) to achieve meaningful domain adaptation","Custom tokenizer training adds 2-4 week turnaround time before model deployment","No incremental fine-tuning support — each new dataset version requires full retraining cycle","Regulatory-aware masking may reduce model accuracy on edge cases where sensitive entities are semantically important","On-premise deployment requires dedicated DevOps resources for infrastructure management and monitoring","Air-gapped mode requires manual model update procedures — no automatic security patches or model improvements","Scaling beyond initial hardware capacity requires customer-side infrastructure expansion (no auto-scaling to cloud)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2833333333333333,"quality":0.63,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:29.716Z","last_scraped_at":"2026-04-05T13:23:42.562Z","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=cleargpt","compare_url":"https://unfragile.ai/compare?artifact=cleargpt"}},"signature":"Nb9vsvEuAeGSBj0HFRSblsIKxAdQn7+zxxGVMXNKXlXSGHquLcVaqA4Mv1wBFHAdfqrvkMDCdbv6bhVFN/67Cw==","signedAt":"2026-06-22T00:09:44.223Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cleargpt","artifact":"https://unfragile.ai/cleargpt","verify":"https://unfragile.ai/api/v1/verify?slug=cleargpt","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"}}