{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_pragma","slug":"pragma","name":"Pragma","type":"product","url":"https://www.pragma.ai","page_url":"https://unfragile.ai/pragma","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_pragma__cap_0","uri":"capability://memory.knowledge.multi.source.knowledge.base.indexing.and.semantic.search","name":"multi-source knowledge base indexing and semantic search","description":"Pragma ingests documents from multiple enterprise sources (likely including cloud storage, document management systems, and internal wikis) and builds a searchable semantic index using vector embeddings. When users query, it performs hybrid search combining keyword matching with semantic similarity to retrieve the most relevant documents, then grounds responses in actual company knowledge rather than generic LLM training data. This architecture reduces hallucinations by constraining the model to only synthesize information from indexed sources.","intents":["I want employees to find answers to common questions without searching through 5 different systems","I need to reduce repetitive HR and IT support tickets by making policies instantly accessible","I want to ensure answers are always based on our actual company documents, not generic internet knowledge"],"best_for":["Mid-sized enterprises (50-500 employees) with centralized documentation","Organizations with mature knowledge management practices and documented processes","Teams wanting to reduce support ticket volume through self-service knowledge access"],"limitations":["Requires upfront effort to identify, organize, and structure all knowledge sources before indexing begins","Performance degrades with poorly organized or duplicate content in source systems","No built-in deduplication or conflict resolution when same information exists in multiple sources","Indexing latency means recently updated documents may not be immediately searchable (typically 15-60 minute delay)"],"requires":["Access credentials to source systems (cloud storage, document management, wikis)","Minimum 50-100 documents to establish meaningful semantic search baseline","Designated knowledge owner or admin to maintain index quality and freshness"],"input_types":["text documents (PDF, DOCX, TXT)","structured data (likely CSV, JSON from databases)","web content from internal wikis or knowledge bases"],"output_types":["natural language responses grounded in source documents","citations/references to original documents with confidence scores","structured answers extracted from indexed content"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pragma__cap_1","uri":"capability://text.generation.language.context.aware.conversational.query.resolution.with.document.grounding","name":"context-aware conversational query resolution with document grounding","description":"Pragma maintains conversation context across multiple turns, allowing users to ask follow-up questions that reference previous answers without re-stating context. The system retrieves relevant documents for each query, synthesizes answers using an LLM, and explicitly cites source documents to establish trust and traceability. This differs from generic chatbots by constraining generation to company-specific knowledge and maintaining an audit trail of which documents informed each response.","intents":["I want to ask follow-up questions about company policies without repeating my initial question","I need to know which document my answer came from so I can verify it or get more context","I want the assistant to understand company-specific terminology and context from our documents"],"best_for":["Employees asking multi-turn questions about policies, procedures, or company information","Support teams wanting to provide consistent, documented answers to recurring questions","Compliance-focused organizations needing audit trails showing which policies informed decisions"],"limitations":["Context window is limited by the underlying LLM (typically 4K-8K tokens), so very long conversation histories may lose early context","If source documents contain conflicting information, the system may synthesize contradictory answers across turns","No built-in mechanism to flag when documents are outdated or superseded by newer versions","Citation accuracy depends on quality of document chunking and embedding — poor chunking can produce citations that don't actually support the answer"],"requires":["Indexed knowledge base with at least 20-50 documents covering common query topics","Clear document structure and metadata (titles, dates, categories) to enable accurate citations","LLM API access (likely OpenAI, Anthropic, or self-hosted model)"],"input_types":["natural language questions in conversational format","follow-up questions with implicit references to previous context"],"output_types":["natural language answers with inline citations","document references with relevance scores","conversation history with source attribution"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pragma__cap_10","uri":"capability://text.generation.language.role.based.answer.personalization.and.context.injection","name":"role-based answer personalization and context injection","description":"Pragma can personalize answers based on user role or department — for example, an HR question answered for a manager might include information about team management responsibilities, while the same question for an individual contributor might focus on personal benefits. The system injects user context (department, role, location, tenure) into queries to retrieve more relevant documents and tailor responses. This requires maintaining a user directory with role/department information and mapping it to document access and answer customization rules.","intents":["I want answers tailored to my role (e.g., managers see different information than individual contributors)","I want the assistant to understand my department's specific processes and policies","I want location-specific information (e.g., office address, local benefits) without having to specify my location"],"best_for":["Large organizations with different policies or processes for different roles/departments","Companies with multiple locations or regional variations in policies","Teams wanting to reduce information overload by showing only relevant information per role"],"limitations":["Requires maintaining accurate user directory with role and department information","Role-based personalization may create inconsistency if different roles receive conflicting information","No mechanism to handle edge cases where a user has multiple roles or responsibilities","Personalization rules must be manually configured and updated when organizational structure changes","Privacy concerns about using role/department data to filter information"],"requires":["Integration with HR system or directory (LDAP, Active Directory, Okta) to maintain user roles","Mapping of roles to document categories and answer customization rules","Regular updates to user directory and role mappings"],"input_types":["user identity and role information","department and location data","role-specific document categories and customization rules"],"output_types":["personalized answers tailored to user role","role-specific document recommendations","context-aware information (location, department, tenure)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pragma__cap_2","uri":"capability://tool.use.integration.multi.system.integration.and.connector.management","name":"multi-system integration and connector management","description":"Pragma provides pre-built connectors to common enterprise platforms (Slack, Confluence, Google Drive, SharePoint, Jira, etc.) that handle authentication, incremental syncing, and schema normalization. The connector framework abstracts platform-specific APIs behind a unified ingestion interface, allowing knowledge from disparate systems to be indexed into a single semantic space. This eliminates the need for custom ETL pipelines while maintaining data freshness through scheduled or event-driven sync triggers.","intents":["I want to index knowledge from Slack, Confluence, and Google Drive without writing custom integration code","I need documents to stay fresh automatically without manual re-indexing","I want to connect to legacy systems and modern SaaS platforms without building separate connectors for each"],"best_for":["Organizations using multiple knowledge platforms (Slack, Confluence, Google Workspace, Microsoft 365)","Teams without dedicated engineering resources to build custom ETL pipelines","Enterprises wanting to consolidate knowledge without migrating data out of existing systems"],"limitations":["Limited to pre-built connectors — integrating custom or legacy systems requires manual setup or custom development","Connector reliability depends on third-party API stability; platform outages or API changes can break syncs","No built-in conflict resolution when the same document exists in multiple source systems","Incremental sync may miss updates if source system doesn't provide reliable change detection APIs","Data transformation during sync may lose formatting, metadata, or embedded media from source documents"],"requires":["API credentials or OAuth tokens for each source system","Appropriate permissions in source systems to read documents and metadata","Network connectivity and firewall rules allowing Pragma to access source systems"],"input_types":["OAuth/API credentials for enterprise platforms","configuration specifying which folders/spaces/channels to index","metadata mappings (e.g., which fields to treat as document title, date, category)"],"output_types":["normalized document representations in unified schema","sync status and error logs","metadata about source system and last sync timestamp"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pragma__cap_3","uri":"capability://safety.moderation.access.control.and.data.governance.enforcement","name":"access control and data governance enforcement","description":"Pragma enforces document-level access control by mapping user identities to permissions defined in source systems (e.g., Slack channel membership, Google Drive sharing settings, Confluence space permissions). When a user queries the knowledge base, the system filters search results to only include documents they have permission to access, preventing unauthorized disclosure of sensitive information. This architecture maintains security posture by respecting existing permission models rather than creating a separate access control layer.","intents":["I want to ensure employees only see documents they're authorized to access","I need to prevent confidential information (HR docs, financial data, executive communications) from being exposed to unauthorized users","I want access control to automatically update when permissions change in source systems"],"best_for":["Regulated industries (finance, healthcare, legal) with strict data governance requirements","Organizations with sensitive internal documents that shouldn't be universally accessible","Teams using multiple source systems with different permission models that need unified enforcement"],"limitations":["Access control is only as strong as the source system's permission model — if source system has overly permissive sharing, Pragma cannot restrict further","Permission sync latency means recently-revoked access may still be visible for 5-15 minutes until next sync","No fine-grained field-level access control — entire documents are either visible or hidden, not individual fields","Complex permission hierarchies (nested groups, role-based access) may not sync correctly if source system uses non-standard models","No built-in audit logging of who accessed which documents through Pragma (depends on underlying LLM provider's logging)"],"requires":["Source systems must expose user identity and permission information via API","User identity must be consistently mapped across systems (e.g., email addresses match)","Regular permission sync to keep access control in sync with source system changes"],"input_types":["user identity (email, username, SSO identifier)","permission mappings from source systems"],"output_types":["filtered search results respecting user permissions","access denied responses for unauthorized queries","audit logs of access attempts"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pragma__cap_4","uri":"capability://data.processing.analysis.knowledge.base.quality.monitoring.and.staleness.detection","name":"knowledge base quality monitoring and staleness detection","description":"Pragma tracks document metadata (last modified date, source system, sync status) and can flag documents that haven't been updated recently or whose source content has changed. The system may provide dashboards showing indexing coverage, document freshness, and sync errors, helping knowledge managers identify gaps or outdated information. This enables proactive maintenance of the knowledge base rather than relying on users to report incorrect answers.","intents":["I want to know which documents in our knowledge base are outdated or haven't been updated in months","I need to identify gaps in our documentation coverage so I can prioritize what to write next","I want to be alerted when sync errors prevent documents from being indexed"],"best_for":["Knowledge managers and documentation teams responsible for maintaining knowledge base quality","Organizations with compliance requirements to ensure policies are current and accurate","Teams wanting to measure and improve knowledge base coverage over time"],"limitations":["Staleness detection only works if source documents have reliable modification timestamps — some systems may not track this accurately","No automatic remediation — the system can flag problems but requires manual action to update or remove outdated documents","Coverage metrics are only as good as the taxonomy/categorization used — poorly categorized documents may appear as gaps","No built-in mechanism to validate that answers generated from documents are actually correct or complete","Monitoring dashboards may have latency (5-15 minutes) before reflecting recent changes"],"requires":["Source systems that expose document modification timestamps","Defined document categories or taxonomy to measure coverage against","Regular review process to act on staleness and gap alerts"],"input_types":["document metadata from source systems","user feedback on answer quality","knowledge base taxonomy/categories"],"output_types":["staleness reports and dashboards","coverage gap analysis","sync error logs and alerts","recommendations for documentation updates"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pragma__cap_5","uri":"capability://text.generation.language.natural.language.query.understanding.with.company.context","name":"natural language query understanding with company context","description":"Pragma uses the indexed knowledge base as context to improve query understanding — it can recognize company-specific terminology, acronyms, and concepts that wouldn't be understood by a generic LLM. For example, if your company uses 'PTO' to mean 'Paid Time Off' and this is defined in your HR policies, Pragma understands this context when interpreting queries. The system likely uses semantic similarity to map user queries to relevant document categories before retrieving specific documents, improving retrieval precision.","intents":["I want to ask questions using company jargon and acronyms without having to spell them out","I want the assistant to understand context-specific meanings (e.g., 'sprint' means something different in our company than in generic software development)","I want queries to be routed to the right documents even if I use different terminology than the documents use"],"best_for":["Organizations with specialized terminology or industry jargon","Companies with multiple departments using the same terms differently","Teams wanting to reduce friction for employees unfamiliar with company-specific language"],"limitations":["Query understanding quality depends on how well company terminology is documented — if acronyms aren't defined in indexed documents, the system won't understand them","Ambiguous terminology may be misinterpreted if multiple documents define the same term differently","No built-in mechanism to learn new terminology from user interactions — requires manual updates to knowledge base","Query understanding may fail for very new or rapidly evolving terminology not yet documented","Homonyms and context-dependent terms may be misinterpreted without explicit disambiguation in documents"],"requires":["Comprehensive documentation of company terminology, acronyms, and definitions","Consistent use of terminology across indexed documents","Regular updates to terminology documentation as company evolves"],"input_types":["natural language queries using company terminology","company glossaries or terminology documentation"],"output_types":["interpreted queries with recognized company concepts","answers using company-specific terminology","clarification requests when terminology is ambiguous"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pragma__cap_6","uri":"capability://automation.workflow.conversational.onboarding.and.employee.self.service.support","name":"conversational onboarding and employee self-service support","description":"Pragma can be deployed as a conversational interface (likely via Slack, web chat, or mobile app) that employees use to ask questions about policies, procedures, benefits, and company information. The system provides instant answers without requiring employees to search through wikis or contact HR/IT, reducing support ticket volume and accelerating onboarding. This capability combines knowledge retrieval with conversational UX to create a self-service support channel.","intents":["I want new employees to get answers to common onboarding questions instantly without contacting HR","I want to reduce the volume of repetitive support tickets about policies and procedures","I want employees to be able to ask questions in natural language without learning how to search our knowledge systems"],"best_for":["Organizations with high employee turnover or frequent onboarding","Companies with high support ticket volume from repetitive questions","Teams wanting to improve employee experience by reducing friction in accessing information"],"limitations":["Effectiveness depends on knowledge base completeness — if common questions aren't documented, the system will provide poor answers","No built-in escalation to human support — if the system can't answer a question, users must manually contact support","Conversational interface may be less discoverable than a searchable knowledge base — employees may not know to ask the assistant","Answers are limited to documented knowledge — the system cannot provide personalized advice or handle edge cases not covered in documentation","No built-in feedback loop to identify which questions the system struggles with and prioritize documentation updates"],"requires":["Comprehensive documentation of policies, procedures, benefits, and common questions","Integration with employee communication platforms (Slack, Teams, email) or deployment of web/mobile chat interface","Clear escalation path for questions the system cannot answer"],"input_types":["natural language questions from employees","employee identity for personalization (optional)"],"output_types":["natural language answers to common questions","links to detailed documentation","escalation to human support when needed"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pragma__cap_7","uri":"capability://data.processing.analysis.analytics.and.usage.insights.on.knowledge.base.effectiveness","name":"analytics and usage insights on knowledge base effectiveness","description":"Pragma collects analytics on which questions are asked most frequently, which documents are accessed most often, and which queries result in low-confidence answers. This data is aggregated into dashboards showing knowledge base effectiveness, identifying popular topics and gaps in coverage. The system can recommend which documents to create or update based on unanswered questions and can track metrics like average answer quality, user satisfaction, and support ticket reduction.","intents":["I want to understand which topics employees ask about most so I can prioritize documentation","I want to measure whether the knowledge assistant is actually reducing support ticket volume","I want to identify questions the system struggles to answer so I can improve documentation"],"best_for":["Knowledge managers and documentation teams wanting to measure and improve knowledge base ROI","Organizations with data-driven culture wanting to optimize support operations","Teams wanting to justify investment in knowledge management with concrete metrics"],"limitations":["Analytics are only as good as the data collected — if the system doesn't log queries or user feedback, insights will be incomplete","Privacy concerns may limit what data can be collected (e.g., cannot log full query text if it contains sensitive information)","Correlation between knowledge base usage and support ticket reduction is not causal — other factors may reduce tickets","No built-in predictive analytics — the system can show historical trends but not forecast future needs","Analytics dashboards may have latency (hours or days) before reflecting recent changes"],"requires":["User consent to collect and analyze query data","Integration with support ticketing system to correlate knowledge base usage with ticket volume","Regular review of analytics to act on insights"],"input_types":["query logs and user interactions","user feedback on answer quality","support ticket data","document metadata"],"output_types":["dashboards showing query volume by topic","document popularity and usage metrics","answer quality and confidence scores","recommendations for documentation updates","support ticket reduction metrics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pragma__cap_8","uri":"capability://safety.moderation.document.aware.answer.validation.and.confidence.scoring","name":"document-aware answer validation and confidence scoring","description":"Pragma assigns confidence scores to answers based on how well the retrieved documents support the response. If an answer is directly stated in a document, confidence is high; if the answer requires inference or synthesis across multiple documents, confidence is lower. The system can flag low-confidence answers and may refuse to answer if no relevant documents are found, preventing hallucinations. This architecture provides transparency about answer reliability and helps users decide whether to trust the answer or escalate to human support.","intents":["I want to know how confident the assistant is in its answer before I act on it","I want the assistant to refuse to answer if it doesn't have reliable information rather than making something up","I want to identify when the assistant is synthesizing information across multiple documents vs. stating a single fact"],"best_for":["Risk-averse organizations where incorrect answers have significant consequences","Regulated industries requiring documented justification for decisions","Teams wanting to build user trust by being transparent about answer reliability"],"limitations":["Confidence scoring is heuristic-based and may not accurately reflect true answer reliability","High-confidence answers may still be incorrect if source documents contain errors or outdated information","No mechanism to validate that synthesized answers are logically consistent across retrieved documents","Confidence thresholds are arbitrary — there's no objective standard for what constitutes 'high confidence'","Users may ignore confidence scores and act on low-confidence answers anyway"],"requires":["Well-structured documents with clear statements of facts and policies","Reliable document metadata (dates, authors, approval status) to assess credibility","User education on how to interpret confidence scores"],"input_types":["retrieved documents and relevance scores","answer synthesis logic and inference chains"],"output_types":["answers with confidence scores (e.g., 0.0-1.0)","source document citations with relevance scores","refusals to answer when confidence is too low","explanations of how answer was derived from documents"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pragma__cap_9","uri":"capability://automation.workflow.feedback.loop.and.continuous.knowledge.base.improvement","name":"feedback loop and continuous knowledge base improvement","description":"Pragma collects user feedback on answer quality (thumbs up/down, explicit corrections, follow-up questions) and uses this to identify which documents need updating or which topics need better coverage. The system may flag answers that receive negative feedback for review by knowledge managers and can track which documents are frequently corrected or questioned. This creates a feedback loop where user interactions directly inform knowledge base maintenance priorities.","intents":["I want to know which answers users found unhelpful so I can improve documentation","I want to identify documents that contain errors or outdated information based on user corrections","I want to prioritize documentation updates based on which topics users struggle with most"],"best_for":["Organizations committed to continuous improvement of knowledge base quality","Teams with dedicated knowledge managers who can act on feedback","Companies wanting to close the loop between user interactions and documentation updates"],"limitations":["Feedback is only useful if users actually provide it — many users may not take time to rate answers","Negative feedback may reflect user confusion rather than incorrect documentation","No automatic remediation — feedback must be manually reviewed and acted upon","Feedback data may be biased toward power users who are more likely to provide ratings","Privacy concerns may limit what feedback can be collected (e.g., cannot store full queries if they contain sensitive information)"],"requires":["User interface for providing feedback (ratings, corrections, comments)","Process for knowledge managers to review and act on feedback","Mechanism to track which documents have been updated in response to feedback"],"input_types":["user ratings and feedback on answers","user corrections and clarifications","follow-up questions indicating incomplete answers"],"output_types":["feedback dashboards showing answer quality by topic","lists of documents flagged for review","recommendations for documentation updates","tracking of feedback resolution"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Access credentials to source systems (cloud storage, document management, wikis)","Minimum 50-100 documents to establish meaningful semantic search baseline","Designated knowledge owner or admin to maintain index quality and freshness","Indexed knowledge base with at least 20-50 documents covering common query topics","Clear document structure and metadata (titles, dates, categories) to enable accurate citations","LLM API access (likely OpenAI, Anthropic, or self-hosted model)","Integration with HR system or directory (LDAP, Active Directory, Okta) to maintain user roles","Mapping of roles to document categories and answer customization rules","Regular updates to user directory and role mappings","API credentials or OAuth tokens for each source system"],"failure_modes":["Requires upfront effort to identify, organize, and structure all knowledge sources before indexing begins","Performance degrades with poorly organized or duplicate content in source systems","No built-in deduplication or conflict resolution when same information exists in multiple sources","Indexing latency means recently updated documents may not be immediately searchable (typically 15-60 minute delay)","Context window is limited by the underlying LLM (typically 4K-8K tokens), so very long conversation histories may lose early context","If source documents contain conflicting information, the system may synthesize contradictory answers across turns","No built-in mechanism to flag when documents are outdated or superseded by newer versions","Citation accuracy depends on quality of document chunking and embedding — poor chunking can produce citations that don't actually support the answer","Requires maintaining accurate user directory with role and department information","Role-based personalization may create inconsistency if different roles receive conflicting information","builder identity is not verified yet","no observed match outcomes 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