{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_frankly-ai","slug":"frankly-ai","name":"Frankly.ai","type":"product","url":"https://frankly.ai","page_url":"https://unfragile.ai/frankly-ai","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_frankly-ai__cap_0","uri":"capability://text.generation.language.teams.native.conversational.ai.assistance.with.thread.context.awareness","name":"teams-native conversational ai assistance with thread context awareness","description":"Frankly.ai embeds a conversational AI agent directly within Microsoft Teams' native UI, leveraging Teams' conversation threading and message history APIs to maintain contextual awareness across multi-turn discussions. The system ingests Teams message objects (including metadata like sender, timestamp, thread depth) and uses this context to generate responses that reference prior messages and team dynamics without requiring users to manually copy-paste conversation history. Integration occurs via Teams Bot Framework and Graph API for message retrieval.","intents":["I want my support team to get AI-assisted responses without leaving Teams or copying conversation context","I need the AI to understand the full thread history when responding to a customer inquiry","I want to reduce context-switching friction for teams handling high-volume support tickets in Teams"],"best_for":["Mid-to-large enterprises using Microsoft Teams as primary communication platform","Support teams handling high-volume inquiries who need to stay in Teams","Organizations where context-switching to external tools creates operational friction"],"limitations":["Requires Teams as the communication platform — no Slack, Discord, or other platform support","Thread context limited to Teams message history accessible via Graph API — external documents or knowledge bases require separate integration","Latency depends on Teams Graph API response time for message retrieval, typically 200-500ms per context fetch"],"requires":["Microsoft Teams (desktop, web, or mobile client)","Teams workspace with bot installation permissions","Azure AD tenant for authentication and authorization"],"input_types":["text messages","Teams thread metadata","conversation history"],"output_types":["text responses","formatted Teams messages with mentions and links"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_frankly-ai__cap_1","uri":"capability://safety.moderation.enterprise.grade.data.residency.and.compliance.aware.response.filtering","name":"enterprise-grade data residency and compliance-aware response filtering","description":"Frankly.ai implements data residency controls and compliance-aware filtering that prevents sensitive information (PII, regulated data) from being processed by external LLM providers or stored in non-compliant regions. The system uses pattern-matching and entity recognition to identify regulated data types (SSN, credit card, health records) and either redacts them before processing, routes requests to compliant regional endpoints, or blocks processing entirely based on organizational policy. This is implemented via pre-processing pipelines that run before LLM inference.","intents":["I need to ensure customer data never leaves our region or compliant infrastructure","I want to prevent PII from being sent to external AI providers","I need audit trails showing what data was processed and where it was stored"],"best_for":["Healthcare organizations subject to HIPAA or similar regulations","Financial services firms handling regulated customer data","Enterprises in EU/GDPR jurisdictions requiring data residency guarantees","Organizations with strict data governance policies"],"limitations":["Data residency enforcement requires deployment in specific Azure regions, limiting geographic flexibility","PII detection relies on pattern matching and NER models — may miss context-dependent sensitive data or false-positive on legitimate data","Compliance filtering adds 100-300ms latency per request due to pre-processing pipeline","Audit logging and compliance reporting require separate configuration and monitoring setup"],"requires":["Azure subscription with regional deployment options","Compliance framework definition (HIPAA, GDPR, SOC 2, etc.)","Data governance policies configured in Frankly.ai admin console"],"input_types":["text messages containing potentially sensitive data","compliance policy definitions"],"output_types":["redacted/filtered text responses","compliance audit logs","data processing reports"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_frankly-ai__cap_2","uri":"capability://safety.moderation.teams.channel.and.conversation.scoped.ai.response.generation.with.role.based.access.control","name":"teams channel and conversation-scoped ai response generation with role-based access control","description":"Frankly.ai implements scope-aware response generation where the AI understands which Teams channel, conversation, or team it's operating within and applies role-based access control (RBAC) to determine what information it can surface and what actions it can perform. The system uses Teams' native permission model (channel membership, team ownership, guest status) to enforce access boundaries, preventing the AI from surfacing confidential information to users without appropriate permissions. This is implemented via Teams Graph API permission checks before response generation.","intents":["I want the AI to respect Teams channel permissions and not leak information across team boundaries","I need different AI capabilities for different user roles (support agents vs managers vs executives)","I want to ensure guest users or external collaborators don't access sensitive team information through the AI"],"best_for":["Large enterprises with complex team hierarchies and permission models","Organizations with external collaborators or guest users in Teams","Support organizations with tiered access (agents, supervisors, managers)"],"limitations":["RBAC enforcement depends on Teams Graph API permission checks, which add 100-200ms latency per request","Scope boundaries are limited to Teams' native permission model — cannot enforce custom business logic beyond Teams RBAC","Guest user handling may be inconsistent if Teams permissions are misconfigured at the organizational level","No fine-grained field-level access control — all-or-nothing access to information within a scope"],"requires":["Microsoft Teams with properly configured team and channel permissions","Azure AD with role definitions aligned to Teams structure","Frankly.ai configured with Teams Graph API permissions"],"input_types":["user identity and Teams membership","channel and team context","role definitions"],"output_types":["role-filtered text responses","access control audit logs"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_frankly-ai__cap_3","uri":"capability://planning.reasoning.customer.support.workflow.automation.with.ai.assisted.ticket.triage.and.response.suggestions","name":"customer support workflow automation with ai-assisted ticket triage and response suggestions","description":"Frankly.ai provides AI-assisted support workflow automation that analyzes incoming customer inquiries (via Teams messages or integrated ticketing systems) to automatically categorize tickets, suggest response templates, and identify escalation needs. The system uses text classification and intent recognition to route tickets to appropriate support tiers, generate draft responses based on historical resolution patterns, and flag urgent or complex issues for human review. This is implemented via NLP classification pipelines and retrieval-augmented generation (RAG) over historical support tickets.","intents":["I want the AI to automatically categorize incoming support tickets by issue type","I need draft response suggestions based on similar past tickets to speed up agent response time","I want the AI to flag tickets that need escalation to senior support staff or specialists"],"best_for":["Support teams handling high-volume, repetitive inquiries","Organizations with well-documented historical ticket data","Mid-to-large enterprises where support efficiency directly impacts cost"],"limitations":["Triage accuracy depends on quality and volume of historical ticket data — poor historical data leads to poor classifications","Response suggestions are template-based and may require significant agent customization for complex or novel issues","Escalation detection relies on heuristics and may miss edge cases or context-dependent urgency signals","RAG over historical tickets requires regular retraining as support processes evolve"],"requires":["Historical support ticket data (minimum 500-1000 tickets for effective training)","Defined ticket categories and escalation criteria","Integration with ticketing system or Teams channel for ticket ingestion"],"input_types":["customer inquiry text","ticket metadata (category, priority, customer info)","historical ticket database"],"output_types":["ticket classification and category","suggested response templates","escalation flags and recommendations","priority/urgency scores"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_frankly-ai__cap_4","uri":"capability://memory.knowledge.knowledge.base.integration.and.retrieval.augmented.generation.for.support.responses","name":"knowledge base integration and retrieval-augmented generation for support responses","description":"Frankly.ai integrates with organizational knowledge bases (SharePoint, wikis, documentation) and uses retrieval-augmented generation (RAG) to ground AI responses in authoritative company information. The system embeds and indexes knowledge base documents, retrieves relevant passages based on customer inquiries, and generates responses that cite sources and maintain consistency with documented policies. This is implemented via vector embeddings (likely OpenAI or similar), semantic search over indexed documents, and prompt engineering to enforce citation and consistency.","intents":["I want the AI to answer support questions using our official documentation and knowledge base","I need the AI to cite sources so customers know the response is based on official company information","I want to reduce inconsistency in support responses by grounding them in a single source of truth"],"best_for":["Support organizations with well-maintained knowledge bases or documentation","Enterprises with complex product documentation or policy manuals","Organizations where response consistency and accuracy are critical"],"limitations":["RAG quality depends on knowledge base quality and coverage — gaps in documentation lead to hallucinations or generic responses","Embedding and retrieval adds 200-500ms latency per request","Knowledge base indexing requires manual setup and ongoing maintenance as documentation evolves","Semantic search may retrieve irrelevant documents if knowledge base is poorly organized or uses inconsistent terminology","Source citation is only as good as the underlying knowledge base — outdated or incorrect documentation will be cited as authoritative"],"requires":["Accessible knowledge base (SharePoint, Confluence, custom wiki, or document repository)","Knowledge base content in text-searchable format (PDFs, markdown, HTML, etc.)","Vector embedding service (OpenAI, Azure Cognitive Search, or similar)"],"input_types":["customer inquiry text","knowledge base documents","document metadata (title, category, last updated)"],"output_types":["grounded response text","source citations with document links","confidence scores for retrieved passages"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_frankly-ai__cap_5","uri":"capability://memory.knowledge.multi.turn.conversation.state.management.with.teams.message.history.persistence","name":"multi-turn conversation state management with teams message history persistence","description":"Frankly.ai maintains conversation state across multiple turns within Teams threads, tracking context, user intent, and conversation history without requiring explicit state management by the developer. The system uses Teams' native message threading to persist conversation state, retrieves prior messages via Graph API on each turn, and maintains a working context window that includes relevant prior exchanges. This is implemented via Teams message history retrieval and in-memory context management with optional persistence to Azure storage.","intents":["I want the AI to remember context from earlier in the conversation without me repeating information","I need the AI to handle multi-turn support conversations where context builds across multiple exchanges","I want conversation history to persist in Teams so team members can review the full exchange later"],"best_for":["Support teams handling complex, multi-step customer issues","Organizations where conversation history is important for compliance or training","Teams with high context-switching needs where persistent conversation history reduces rework"],"limitations":["Context window is limited by Teams message history API rate limits and practical token limits (typically 4K-8K tokens)","Retrieving full conversation history on each turn adds 200-500ms latency","State management is tied to Teams threads — conversations cannot be easily exported or migrated to other systems","Long conversations may exceed token limits, requiring summarization or context pruning"],"requires":["Microsoft Teams with message history accessible via Graph API","Azure AD authentication for Graph API access"],"input_types":["current user message","Teams thread ID","prior message history"],"output_types":["contextually aware response","updated conversation state","Teams message with thread context"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_frankly-ai__cap_6","uri":"capability://tool.use.integration.secure.api.integration.and.function.calling.with.microsoft.ecosystem.connectors","name":"secure api integration and function calling with microsoft ecosystem connectors","description":"Frankly.ai supports secure function calling and API integration with Microsoft ecosystem services (Dynamics 365, Power Automate, SharePoint, Azure services) via OAuth 2.0 and managed connectors. The system allows the AI to invoke business logic, retrieve data, or trigger workflows without exposing API keys or credentials, using Teams' identity context to authenticate API calls. This is implemented via Power Automate connectors, Azure Managed Identity, and secure credential storage in Azure Key Vault.","intents":["I want the AI to look up customer data from Dynamics 365 without exposing API credentials","I need the AI to trigger Power Automate workflows to automate support tasks","I want the AI to retrieve data from SharePoint or other Microsoft services securely"],"best_for":["Enterprises deeply integrated with Microsoft ecosystem (Dynamics, Power Platform, Azure)","Organizations with strict credential management and security policies","Support teams that need to automate tasks across multiple Microsoft services"],"limitations":["Function calling is limited to Microsoft ecosystem connectors — third-party APIs require custom integration","OAuth 2.0 authentication adds latency (typically 100-300ms per API call) due to token validation","Managed connectors may have rate limits or throttling that impact response time","Requires Azure subscription and proper configuration of managed identities and Key Vault"],"requires":["Microsoft 365 subscription with appropriate service licenses (Dynamics, Power Automate, etc.)","Azure subscription with Azure Key Vault for credential storage","Azure AD with managed identity configuration","Power Automate connectors configured for target services"],"input_types":["function/action requests from AI","user identity context from Teams","API parameters and payloads"],"output_types":["API response data","workflow execution status","error messages and logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_frankly-ai__cap_7","uri":"capability://safety.moderation.audit.logging.and.compliance.reporting.for.ai.assisted.support.interactions","name":"audit logging and compliance reporting for ai-assisted support interactions","description":"Frankly.ai provides comprehensive audit logging of all AI-assisted interactions, including what data was processed, what responses were generated, who reviewed/approved them, and what actions were taken. The system logs interactions to Azure storage with immutable audit trails, generates compliance reports for regulatory audits, and provides dashboards for monitoring AI usage patterns. This is implemented via structured logging to Azure Monitor/Application Insights and compliance report generation templates.","intents":["I need audit trails showing what data the AI processed and where it was stored","I want to generate compliance reports for regulatory audits (HIPAA, GDPR, SOC 2)","I need to monitor AI usage patterns to detect anomalies or misuse"],"best_for":["Regulated industries (healthcare, finance) subject to compliance audits","Organizations with strict data governance and audit requirements","Enterprises needing to demonstrate AI system transparency and accountability"],"limitations":["Audit logging adds overhead and storage costs — large-scale deployments may require log retention policies","Compliance report generation requires manual configuration of report templates for each regulatory framework","Audit logs may contain sensitive data (customer inquiries, responses) requiring careful access control","Real-time monitoring dashboards may have latency (5-15 minutes) due to log aggregation"],"requires":["Azure subscription with Azure Monitor or Application Insights","Compliance framework definitions (HIPAA, GDPR, SOC 2, etc.)","Log retention and archival policies configured"],"input_types":["AI interaction events (request, response, action)","user identity and context","compliance policy definitions"],"output_types":["audit logs in structured format","compliance reports (PDF, CSV)","monitoring dashboards","anomaly alerts"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_frankly-ai__cap_8","uri":"capability://data.processing.analysis.sentiment.analysis.and.escalation.detection.for.support.conversations","name":"sentiment analysis and escalation detection for support conversations","description":"Frankly.ai analyzes customer sentiment in support conversations using NLP-based sentiment classification and detects escalation signals (frustration, urgency, threats) that indicate a ticket needs human intervention. The system scores sentiment on each message, tracks sentiment trends across the conversation, and flags conversations that show negative sentiment progression or explicit escalation indicators. This is implemented via pre-trained sentiment models and rule-based escalation heuristics.","intents":["I want the AI to detect when a customer is frustrated and flag for escalation","I need to identify high-priority or urgent support tickets automatically","I want to track customer satisfaction trends across support conversations"],"best_for":["Support teams handling high-volume inquiries where manual triage is inefficient","Organizations where customer satisfaction is critical","Teams needing to identify escalation needs early before customer dissatisfaction increases"],"limitations":["Sentiment analysis accuracy depends on language and context — may misclassify sarcasm, technical jargon, or non-English languages","Escalation detection relies on heuristics and may miss subtle signals or context-dependent urgency","Sentiment scoring is relative and may not align with human judgment in edge cases","No multi-language support beyond English (likely limitation based on typical NLP models)"],"requires":["Support conversation data in text format","Sentiment model training data (optional, for custom models)"],"input_types":["customer message text","conversation history"],"output_types":["sentiment score (-1 to 1 or similar scale)","escalation flags and confidence scores","sentiment trend analysis","recommended actions"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Microsoft Teams (desktop, web, or mobile client)","Teams workspace with bot installation permissions","Azure AD tenant for authentication and authorization","Azure subscription with regional deployment options","Compliance framework definition (HIPAA, GDPR, SOC 2, etc.)","Data governance policies configured in Frankly.ai admin console","Microsoft Teams with properly configured team and channel permissions","Azure AD with role definitions aligned to Teams structure","Frankly.ai configured with Teams Graph API permissions","Historical support ticket data (minimum 500-1000 tickets for effective training)"],"failure_modes":["Requires Teams as the communication platform — no Slack, Discord, or other platform support","Thread context limited to Teams message history accessible via Graph API — external documents or knowledge bases require separate integration","Latency depends on Teams Graph API response time for message retrieval, typically 200-500ms per context fetch","Data residency enforcement requires deployment in specific Azure regions, limiting geographic flexibility","PII detection relies on pattern matching and NER models — may miss context-dependent sensitive data or false-positive on legitimate data","Compliance filtering adds 100-300ms latency per request due to pre-processing pipeline","Audit logging and compliance reporting require separate configuration and monitoring setup","RBAC enforcement depends on Teams Graph API permission checks, which add 100-200ms latency per request","Scope boundaries are limited to Teams' native permission model — cannot enforce custom business logic beyond Teams RBAC","Guest user handling may be inconsistent if Teams permissions are misconfigured at the organizational level","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"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":"2026-05-24T12:16:30.892Z","last_scraped_at":"2026-04-05T13:23:42.552Z","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=frankly-ai","compare_url":"https://unfragile.ai/compare?artifact=frankly-ai"}},"signature":"kE2aRTcSl7ox1/FovlcySnvF6Rz1tAJVxc69k287aW/GnXv+RL+OIsFdLntHznckjmgi2xx4DG6uIoLbGuiACA==","signedAt":"2026-06-22T07:56:58.969Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/frankly-ai","artifact":"https://unfragile.ai/frankly-ai","verify":"https://unfragile.ai/api/v1/verify?slug=frankly-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"}}