{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_distyl","slug":"distyl","name":"Distyl","type":"product","url":"https://distyl.ai","page_url":"https://unfragile.ai/distyl","categories":["automation"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_distyl__cap_0","uri":"capability://automation.workflow.workflow.native.ai.embedding.with.business.system.connectors","name":"workflow-native ai embedding with business system connectors","description":"Distyl embeds AI capabilities directly into existing enterprise workflows by providing pre-built connectors to common business systems (CRM, ERP, HRIS, document management) rather than requiring custom API integration. The platform likely uses a connector abstraction layer that maps workflow triggers and actions to underlying system APIs, allowing non-technical users to define AI-augmented processes without custom development. This approach reduces implementation time by eliminating the need for middleware or custom integration code between AI models and business systems.","intents":["I need to add AI-powered document review to our contract management workflow without building custom integrations","We want to automate customer inquiry routing with AI classification across our existing CRM without hiring engineers","I need to embed AI data extraction into our invoice processing pipeline that already uses our legacy ERP system"],"best_for":["Mid-to-large enterprises with established workflows and multiple integrated systems","Organizations lacking dedicated AI engineering teams but with existing IT infrastructure","Teams seeking faster time-to-value than custom API development would provide"],"limitations":["Limited to pre-built connectors — custom system integrations likely require professional services engagement","Abstraction layer may add latency (estimated 200-500ms per workflow step) compared to direct API calls","Workflow complexity limits unclear — deeply nested conditional logic or multi-step orchestration may require custom development"],"requires":["Active subscription to Distyl enterprise plan","API credentials or OAuth tokens for connected business systems","Existing business system infrastructure (Salesforce, SAP, NetSuite, etc.)","Workflow definition permissions within target business systems"],"input_types":["structured workflow triggers (form submissions, record updates, scheduled events)","unstructured business documents (contracts, invoices, emails)","database records from connected systems"],"output_types":["workflow actions (create/update records, send notifications, trigger downstream processes)","structured classifications and extractions","approval/routing decisions"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_distyl__cap_1","uri":"capability://tool.use.integration.multi.model.ai.orchestration.with.provider.abstraction","name":"multi-model ai orchestration with provider abstraction","description":"Distyl abstracts underlying AI model providers (OpenAI, Anthropic, Google, potentially open-source models) behind a unified interface, allowing enterprises to switch providers, use multiple models for different tasks, or implement cost optimization strategies without changing workflow definitions. The platform likely maintains a model registry with capability profiles (token limits, latency, cost, specialized skills) and routes requests to optimal providers based on task requirements and cost constraints. This abstraction enables vendor lock-in avoidance and cost-aware model selection at runtime.","intents":["We want to use Claude for complex reasoning tasks and GPT-4 for structured extraction without maintaining separate integrations","We need to automatically route requests to cheaper models when accuracy requirements allow to optimize our AI spend","We want to migrate from OpenAI to a self-hosted open-source model without redefining all our workflows"],"best_for":["Enterprises with large-scale AI usage seeking cost optimization","Organizations wanting to avoid vendor lock-in with a single model provider","Teams needing specialized models for different task types (reasoning vs. extraction vs. classification)"],"limitations":["Model abstraction may mask provider-specific capabilities — advanced features (vision, function calling variants) may not be uniformly available","Latency variance across providers (OpenAI ~500ms, Anthropic ~1000ms, local models ~100ms) requires careful SLA management","Cost tracking and optimization logic adds operational complexity — requires monitoring and tuning of routing rules"],"requires":["API keys for at least one supported AI model provider","Model capability profiles configured in Distyl platform","Cost and latency thresholds defined for routing decisions"],"input_types":["prompt templates with variable placeholders","structured task definitions with capability requirements"],"output_types":["model responses (text, structured JSON, classifications)","cost and latency metrics per request"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_distyl__cap_10","uri":"capability://text.generation.language.multi.language.workflow.support.with.localization","name":"multi-language workflow support with localization","description":"Distyl supports defining and executing workflows in multiple languages, with automatic translation of prompts, documents, and outputs to enable global business processes. The platform likely uses translation APIs (Google Translate, Azure Translator) integrated into the workflow pipeline, with language detection for incoming documents and language-specific AI model selection. This enables enterprises to operate workflows across different regions without maintaining separate workflow definitions per language.","intents":["We need to process customer inquiries in English, Spanish, and French with the same workflow without manual translation","We want to automatically detect the language of incoming documents and route them to language-appropriate AI models","We need to translate AI recommendations back to the customer's language before sending them"],"best_for":["Global enterprises operating in multiple language regions","Organizations processing multilingual documents and communications","Teams wanting to avoid maintaining separate workflows per language"],"limitations":["Translation quality varies by language pair — some languages may require human review of translations","Translation adds latency (500ms-2s per document) depending on document size","Specialized terminology may not translate correctly — domain-specific terms may require custom translation dictionaries","Language detection accuracy may be poor for short text or mixed-language documents"],"requires":["Translation service API key (Google Translate, Azure Translator, etc.)","Language configuration for workflows","Optional: custom translation dictionaries for domain-specific terms"],"input_types":["text in multiple languages","documents in multiple languages"],"output_types":["translated text and documents","language detection results"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_distyl__cap_11","uri":"capability://automation.workflow.workflow.performance.monitoring.and.alerting.with.sla.enforcement","name":"workflow performance monitoring and alerting with sla enforcement","description":"Distyl monitors workflow execution performance (latency, error rates, AI model performance) and alerts teams when SLAs are violated, enabling proactive issue detection and response. The platform likely uses time-series metrics collection with configurable thresholds and alert rules, and may automatically trigger remediation actions (fallback to alternative models, workflow pausing) when SLAs are breached. This enables enterprises to maintain service quality and quickly respond to performance degradation.","intents":["I need to be alerted if our contract review workflow takes longer than 5 minutes on average so we can investigate","We want to automatically switch to a faster AI model if our primary model is experiencing latency issues","I need to track error rates across all workflows and understand which ones are degrading"],"best_for":["Enterprises with critical workflows requiring high availability and performance","Organizations wanting to proactively detect and respond to performance issues","Teams needing to demonstrate SLA compliance to customers or regulators"],"limitations":["Metrics collection adds latency (50-100ms per workflow execution) for instrumentation","Alert fatigue may occur if thresholds are set too aggressively — tuning requires operational experience","Automatic remediation actions (model switching) may have unintended consequences — manual review recommended","Historical metrics retention may be limited — long-term trend analysis may require external data warehousing"],"requires":["Metrics collection backend (managed by Distyl or customer-provided)","SLA definitions and alert thresholds configured per workflow","Alert notification channels (email, Slack, PagerDuty, etc.)"],"input_types":["workflow execution metrics (latency, error rates, model performance)"],"output_types":["performance alerts and notifications","SLA compliance reports"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_distyl__cap_2","uri":"capability://memory.knowledge.enterprise.workflow.context.management.and.state.persistence","name":"enterprise workflow context management and state persistence","description":"Distyl maintains conversation and workflow state across multi-step business processes, enabling AI to understand context from previous steps, user interactions, and system data without requiring developers to manually manage state. The platform likely uses a distributed session store (Redis, DynamoDB) with workflow-scoped context windows that persist across multiple AI invocations, allowing long-running business processes to maintain coherent AI reasoning. This enables stateful workflows where AI decisions depend on accumulated context rather than isolated requests.","intents":["I need AI to remember customer context across multiple workflow steps in our support process without manually passing state between steps","We want AI to make decisions based on the full history of a contract negotiation, not just the current document","I need to track what information the AI has already extracted from a document to avoid redundant processing"],"best_for":["Enterprises with multi-step business processes requiring contextual AI reasoning","Organizations with long-running workflows (days/weeks) where state persistence is critical","Teams needing audit trails of AI decisions and the context that informed them"],"limitations":["Context window size limits may require summarization or pruning of old state for very long workflows","State persistence adds latency (100-300ms per lookup) compared to in-memory context","Cross-workflow context sharing likely not supported — each workflow maintains isolated state","No built-in context versioning — reverting to previous workflow state may require manual intervention"],"requires":["Workflow definition with explicit state checkpoints","Persistent storage backend (managed by Distyl or customer-provided)","Workflow execution environment with session management"],"input_types":["workflow execution events","user interactions and form submissions","system data from connected business systems"],"output_types":["contextual AI responses informed by workflow history","state snapshots for audit and debugging"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_distyl__cap_3","uri":"capability://data.processing.analysis.business.data.extraction.with.schema.driven.validation","name":"business data extraction with schema-driven validation","description":"Distyl extracts structured data from unstructured business documents (contracts, invoices, emails) using AI with schema-based validation to ensure output conforms to expected data models. The platform likely uses a schema definition interface where users specify required fields, data types, and validation rules, then routes documents through AI extraction with post-processing validation that flags extraction failures or confidence issues. This approach combines AI flexibility with data quality guarantees needed for downstream business processes.","intents":["I need to extract invoice line items, amounts, and dates from PDFs and validate they match our accounting system schema before importing","We want to extract contract terms and obligations from legal documents with confidence scoring so we can flag uncertain extractions for human review","I need to pull structured customer information from email inquiries and validate it against our CRM data model"],"best_for":["Enterprises processing high volumes of unstructured documents requiring structured data output","Organizations with strict data quality requirements where extraction failures must be caught before downstream processing","Teams needing to map document data to existing database schemas without manual transformation"],"limitations":["Schema validation may reject valid extractions if AI interpretation differs from schema expectations — requires manual review workflow","Complex nested schemas or conditional field requirements may not be fully supported","Extraction accuracy varies by document type and quality — poor-quality scans or non-standard formats may require human intervention","No built-in document classification — all documents assumed to match a single schema"],"requires":["Schema definition in Distyl format (JSON Schema or proprietary format)","Document upload capability (PDF, image, email formats)","Validation rule definitions for extracted fields"],"input_types":["PDF documents","scanned images","email content","unstructured text"],"output_types":["structured JSON matching defined schema","extraction confidence scores","validation error reports"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_distyl__cap_4","uri":"capability://safety.moderation.role.based.workflow.access.control.with.audit.logging","name":"role-based workflow access control with audit logging","description":"Distyl implements enterprise-grade access control where different users/roles can trigger, modify, or view different workflows based on permission policies, with comprehensive audit logging of all AI decisions and workflow executions. The platform likely uses a role-based access control (RBAC) model integrated with enterprise identity providers (LDAP, Azure AD, Okta) and logs all workflow invocations with inputs, outputs, and AI model decisions for compliance and debugging. This enables regulated industries to maintain audit trails required for compliance frameworks.","intents":["We need to ensure only managers can approve AI-recommended contract terms while junior staff can only view recommendations","We want to log all AI decisions for regulatory compliance and be able to trace why the AI made a specific recommendation","We need to restrict certain workflows to specific departments and track who triggered each workflow execution"],"best_for":["Regulated enterprises (financial services, healthcare, legal) requiring comprehensive audit trails","Organizations with complex permission hierarchies and multi-team workflows","Teams needing to demonstrate AI decision transparency to regulators or auditors"],"limitations":["Audit logging adds storage overhead — long-term retention of detailed logs may require external data warehousing","RBAC integration limited to supported identity providers — custom authentication systems may require custom development","Audit log queries may be slow for high-volume workflows — real-time compliance reporting may require external analytics","No built-in data masking — sensitive information in audit logs may require additional redaction"],"requires":["Enterprise identity provider (Azure AD, Okta, LDAP) or Distyl-managed user directory","Role definitions and permission policies configured in Distyl","Audit log storage backend (managed by Distyl or customer-provided)"],"input_types":["user authentication credentials","workflow execution requests"],"output_types":["access control decisions (allow/deny)","audit log entries with full execution context"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_distyl__cap_5","uri":"capability://planning.reasoning.custom.business.logic.integration.via.workflow.rules.engine","name":"custom business logic integration via workflow rules engine","description":"Distyl provides a rules engine allowing enterprises to define custom business logic that executes alongside AI, enabling conditional workflows, business rule enforcement, and integration with legacy business logic without custom code. The platform likely uses a declarative rules language (similar to Drools or JESS) where users define conditions and actions that execute before/after AI steps, allowing business rules (approval thresholds, escalation policies, data validation) to coexist with AI-driven decisions. This bridges the gap between AI flexibility and deterministic business rule requirements.","intents":["We need to automatically escalate AI recommendations above a certain dollar threshold to management approval, not just route them to AI","We want to enforce business rules like 'never approve contracts with non-standard payment terms' even if AI recommends approval","I need to apply complex pricing logic before AI makes a recommendation so it considers our current margin constraints"],"best_for":["Enterprises with complex business rules that must coexist with AI decision-making","Organizations with legacy business logic that can't be easily replaced by AI","Teams needing to enforce compliance rules alongside AI recommendations"],"limitations":["Rules engine complexity limits unclear — deeply nested conditional logic may require custom development","Rules engine performance may degrade with large numbers of rules — optimization may be required for high-volume workflows","No built-in rules versioning — managing rule changes across production workflows may be complex","Rules language learning curve may require training for non-technical users"],"requires":["Rules engine access in Distyl platform","Business logic defined in rules language","Integration points defined between rules and AI steps"],"input_types":["workflow context and state","business data from connected systems"],"output_types":["rule evaluation results (pass/fail)","conditional workflow routing decisions"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_distyl__cap_6","uri":"capability://data.processing.analysis.enterprise.document.processing.pipeline.with.ocr.and.format.normalization","name":"enterprise document processing pipeline with ocr and format normalization","description":"Distyl processes various document formats (PDFs, scanned images, emails, web content) through a pipeline that includes OCR for scanned documents, format normalization, and document classification before routing to AI extraction or analysis. The platform likely uses specialized OCR engines (Tesseract, commercial OCR) for scanned documents and format converters to normalize different document types into a common representation. This enables consistent processing of heterogeneous document sources without requiring users to pre-process documents.","intents":["We receive contracts as PDFs, scanned images, and emails — I need a single workflow that handles all formats without manual conversion","We want to automatically classify incoming documents by type and route them to different extraction workflows","I need to extract data from poor-quality scanned documents where standard OCR fails"],"best_for":["Enterprises receiving documents in multiple formats from various sources","Organizations with legacy document archives in non-standard formats","Teams processing high volumes of scanned documents requiring automated OCR"],"limitations":["OCR accuracy varies by document quality — poor scans may require manual review or re-scanning","Format normalization may lose document structure or formatting information important for context","Document classification accuracy depends on training data — custom document types may require manual labeling","Large document processing (100+ page PDFs) may have latency implications"],"requires":["Document upload capability","OCR engine configuration (if using custom OCR)","Document classification rules or training data"],"input_types":["PDF files","scanned images (JPEG, PNG, TIFF)","email messages","web content"],"output_types":["normalized text content","document classification labels","OCR confidence scores"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_distyl__cap_7","uri":"capability://planning.reasoning.ai.powered.workflow.recommendations.and.optimization","name":"ai-powered workflow recommendations and optimization","description":"Distyl analyzes workflow execution patterns and suggests optimizations, such as identifying bottlenecks where AI could reduce manual review steps, recommending alternative AI models for cost savings, or suggesting workflow restructuring based on execution data. The platform likely uses analytics on workflow metrics (execution time, error rates, manual review frequency) to identify optimization opportunities and surface them to workflow owners. This enables continuous improvement of AI-augmented workflows based on real execution data.","intents":["I want to understand which steps in our contract review workflow are taking the longest and where AI could help most","We're spending too much on AI — can you identify which workflows could use cheaper models without accuracy loss?","I need to know which documents most frequently require manual review so we can improve our AI extraction"],"best_for":["Enterprises running mature AI workflows seeking continuous optimization","Organizations with large-scale AI usage wanting to reduce costs","Teams wanting data-driven insights into workflow performance"],"limitations":["Recommendations require sufficient execution history — new workflows may not have enough data for meaningful suggestions","Optimization suggestions are advisory only — implementing changes requires manual workflow modification","Cost optimization recommendations may trade off accuracy or latency — tradeoffs not always clearly presented","No built-in A/B testing framework — validating optimization recommendations requires manual testing"],"requires":["Workflow execution history (minimum weeks of data for meaningful analysis)","Cost and performance metrics configured for workflows"],"input_types":["workflow execution metrics","cost and latency data"],"output_types":["optimization recommendations","performance analytics dashboards"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_distyl__cap_8","uri":"capability://tool.use.integration.enterprise.api.gateway.with.rate.limiting.and.usage.monitoring","name":"enterprise api gateway with rate limiting and usage monitoring","description":"Distyl provides an API gateway that sits between workflows and external systems, implementing rate limiting, request throttling, and detailed usage monitoring to prevent API quota exhaustion and track costs. The platform likely uses token bucket or sliding window rate limiting algorithms with per-workflow and per-system quotas, and logs all API calls for cost attribution and compliance. This enables enterprises to safely integrate with external APIs without risk of unexpected bills or service disruptions.","intents":["We need to prevent our workflows from accidentally overwhelming our CRM API with too many requests","I want to track exactly how much each workflow is costing us in terms of API calls to external services","We need to implement fair-use limits so one workflow doesn't monopolize our API quota"],"best_for":["Enterprises integrating with multiple external APIs with strict rate limits","Organizations needing detailed cost tracking and attribution per workflow","Teams wanting to prevent runaway costs from uncontrolled API usage"],"limitations":["Rate limiting may cause workflow delays if quotas are exceeded — no built-in queue management for delayed requests","Usage monitoring adds latency (50-100ms per API call) for logging and enforcement","Rate limit configuration requires understanding of external API quotas — misconfiguration may still cause service disruptions","No built-in cost estimation — actual costs depend on external API pricing which may change"],"requires":["API credentials for external systems","Rate limit configuration per API and workflow","Usage monitoring backend (managed by Distyl or customer-provided)"],"input_types":["API requests from workflows"],"output_types":["rate-limited API responses","usage metrics and cost reports"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_distyl__cap_9","uri":"capability://automation.workflow.workflow.versioning.and.rollback.with.change.management","name":"workflow versioning and rollback with change management","description":"Distyl maintains version history of workflow definitions and AI model configurations, enabling teams to roll back to previous versions if new changes cause issues, and provides change management workflows for approving modifications before deployment. The platform likely uses Git-like versioning for workflow definitions with diff visualization, and may require approval workflows before deploying changes to production. This enables safe workflow evolution without risk of breaking production processes.","intents":["We deployed a new version of our contract review workflow and it's making worse recommendations — I need to quickly roll back to the previous version","We want to require manager approval before any changes to our critical workflows go to production","I need to see what changed between workflow versions to understand why behavior changed"],"best_for":["Enterprises with critical workflows requiring change control and approval processes","Organizations wanting to safely experiment with workflow changes without risking production","Teams needing to understand workflow evolution and debug behavior changes"],"limitations":["Rollback may not restore previous AI model behavior if model versions are not pinned","Change approval workflows add deployment latency — urgent fixes may be delayed by approval process","Version history storage may grow large for frequently-modified workflows","No built-in testing framework — validating changes before deployment requires manual testing"],"requires":["Workflow versioning backend (managed by Distyl)","Change approval workflow definitions (if using approval gates)"],"input_types":["workflow definition changes"],"output_types":["version history with diffs","rollback operations"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Active subscription to Distyl enterprise plan","API credentials or OAuth tokens for connected business systems","Existing business system infrastructure (Salesforce, SAP, NetSuite, etc.)","Workflow definition permissions within target business systems","API keys for at least one supported AI model provider","Model capability profiles configured in Distyl platform","Cost and latency thresholds defined for routing decisions","Translation service API key (Google Translate, Azure Translator, etc.)","Language configuration for workflows","Optional: custom translation dictionaries for domain-specific terms"],"failure_modes":["Limited to pre-built connectors — custom system integrations likely require professional services engagement","Abstraction layer may add latency (estimated 200-500ms per workflow step) compared to direct API calls","Workflow complexity limits unclear — deeply nested conditional logic or multi-step orchestration may require custom development","Model abstraction may mask provider-specific capabilities — advanced features (vision, function calling variants) may not be uniformly available","Latency variance across providers (OpenAI ~500ms, Anthropic ~1000ms, local models ~100ms) requires careful SLA management","Cost tracking and optimization logic adds operational complexity — requires monitoring and tuning of routing rules","Translation quality varies by language pair — some languages may require human review of translations","Translation adds latency (500ms-2s per document) depending on document size","Specialized terminology may not translate correctly — domain-specific terms may require custom translation dictionaries","Language detection accuracy may be poor for short text or mixed-language documents","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.25,"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":"2026-05-24T12:16:30.283Z","last_scraped_at":"2026-04-05T13:23:42.561Z","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=distyl","compare_url":"https://unfragile.ai/compare?artifact=distyl"}},"signature":"/Gyows2ha7d0KLMuUn1sMVFxZpZxWGt4zo1Ei5V/69NZspvm+B/TrS0V9yVnCTx8AT0Zbi9ZLHMtzTXXKph9DA==","signedAt":"2026-06-22T14:33:40.860Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/distyl","artifact":"https://unfragile.ai/distyl","verify":"https://unfragile.ai/api/v1/verify?slug=distyl","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"}}