{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_conva-ai","slug":"conva-ai","name":"Conva.ai","type":"product","url":"https://www.slanglabs.in","page_url":"https://unfragile.ai/conva-ai","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_conva-ai__cap_0","uri":"capability://text.generation.language.multilingual.nlu.with.indian.language.support","name":"multilingual nlu with indian language support","description":"Native natural language understanding engine with dedicated support for Indian languages (Hindi, Tamil, Telugu, Kannada, Marathi, Bengali) alongside English, using language-specific tokenization, morphological analysis, and intent classification models trained on regional linguistic patterns. Unlike generic multilingual models that treat all languages equally, Conva.ai implements language-specific NLU pipelines that handle script variations, grammatical structures, and colloquialisms native to each language.","intents":["Build conversational AI for Indian users without translating to English first","Handle code-mixed conversations where users switch between Hindi and English mid-sentence","Deploy regional language support without maintaining separate NLU models per language","Capture intent and entities from languages with complex morphology like Tamil and Telugu"],"best_for":["Indian startups and enterprises targeting regional language users","Mobile app developers in India needing voice-first conversational experiences","E-commerce and fintech platforms serving non-English speaking populations in South Asia"],"limitations":["Limited to 6-8 Indian languages; does not cover all regional languages in India","Performance on heavily code-mixed text (Hinglish) may degrade compared to pure Hindi or English","Training data for regional languages is smaller than English, potentially affecting accuracy on domain-specific terminology"],"requires":["API key from Conva.ai platform","Language code specification in request (e.g., 'hi' for Hindi, 'ta' for Tamil)","HTTPS endpoint connectivity"],"input_types":["text (UTF-8 encoded)","audio (WAV, MP3 formats for voice input)"],"output_types":["structured JSON with intent, entities, confidence scores","language detection metadata"],"categories":["text-generation-language","nlp-localization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_conva-ai__cap_1","uri":"capability://text.generation.language.voice.to.intent.conversion.with.speech.recognition","name":"voice-to-intent conversion with speech recognition","description":"End-to-end speech recognition and NLU pipeline that converts audio input directly to structured intents and entities, combining automatic speech recognition (ASR) with intent classification in a single flow. The architecture streams audio frames to the ASR engine, buffers recognized text, and pipes it through the NLU layer to extract actionable intents without requiring intermediate manual transcription steps.","intents":["Enable voice-first mobile app interactions where users speak commands instead of typing","Build hands-free conversational experiences for in-car, IoT, or accessibility scenarios","Reduce latency between user speech and action execution by combining ASR and NLU in a single pipeline","Support real-time voice input with streaming audio processing"],"best_for":["Mobile app developers building voice-first UX for Indian markets","IoT and smart device manufacturers needing voice control","Accessibility-focused applications requiring hands-free interaction"],"limitations":["Requires microphone permissions and audio input capability on client device","Background noise and poor audio quality degrade ASR accuracy; no built-in noise cancellation","Latency depends on network connectivity; offline voice processing not supported","ASR accuracy varies by language and accent; regional accents may have lower recognition rates"],"requires":["Client-side audio capture capability (mobile OS or web browser with Web Audio API)","Network connectivity to Conva.ai servers","Microphone permissions granted by user","Supported audio format (WAV, MP3, or raw PCM)"],"input_types":["audio stream (real-time or file-based)","raw PCM frames"],"output_types":["recognized text transcription","structured intent JSON with confidence scores","entity extraction results"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_conva-ai__cap_10","uri":"capability://automation.workflow.fallback.handling.and.escalation.to.human.agents","name":"fallback handling and escalation to human agents","description":"Automatic fallback mechanism that detects when the bot cannot confidently handle a user request (low intent confidence, unrecognized intent, or repeated failures) and seamlessly escalates to human agents. The system can transfer conversation context, conversation history, and extracted information to the human agent, enabling warm handoffs without requiring users to repeat information.","intents":["Gracefully handle out-of-scope requests by escalating to human support","Detect bot failures and escalate before user frustration increases","Transfer conversation context and history to human agents for continuity","Implement confidence-based routing to reduce unnecessary escalations"],"best_for":["Customer service platforms requiring human escalation capability","Support teams managing bot + human hybrid workflows","Enterprises prioritizing customer satisfaction over full automation"],"limitations":["Requires integration with external helpdesk or live chat system; no built-in agent management","Escalation logic is rule-based; may not optimize for agent availability or skill matching","Conversation context transfer depends on integration quality; some information may be lost","Warm handoff requires agent system to support conversation history import"],"requires":["Integration with external helpdesk, live chat, or CRM system","API or webhook endpoint for escalation trigger","Agent system capable of receiving conversation context"],"input_types":["conversation state and history","escalation trigger (low confidence, unrecognized intent, user request)"],"output_types":["escalation event with conversation context","human agent assignment confirmation"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_conva-ai__cap_2","uri":"capability://memory.knowledge.multi.turn.conversation.state.management","name":"multi-turn conversation state management","description":"Stateful conversation engine that maintains context across multiple user-assistant exchanges, tracking conversation history, user intents, extracted entities, and dialogue state within a session. The system implements a context window that persists user information and previous turns, enabling the assistant to resolve pronouns, handle follow-up questions, and maintain coherent multi-step conversations without requiring the client to manage state externally.","intents":["Build conversational flows where user context carries across multiple turns (e.g., 'I want to book a flight' followed by 'Make it business class')","Handle clarification requests and follow-up questions that reference previous conversation context","Maintain user preferences and extracted information throughout a session without re-asking for details","Implement dialogue state machines that branch based on conversation history"],"best_for":["Customer service chatbots handling multi-step support requests","E-commerce assistants guiding users through product selection and checkout","Banking and financial services bots managing account inquiries and transactions"],"limitations":["Context window is session-scoped; does not persist across separate conversations without explicit session management","Large conversation histories (100+ turns) may increase latency due to context processing overhead","No built-in long-term memory or user profile persistence; requires external database integration for cross-session learning","Context resolution relies on NLU accuracy; ambiguous pronouns or references may be misinterpreted"],"requires":["Session ID or user identifier to track conversation state","Stateful API client or SDK that maintains session context","Backend storage for conversation history if persistence is required"],"input_types":["user message (text or transcribed speech)","session metadata"],"output_types":["assistant response","updated conversation state","extracted entities and context"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_conva-ai__cap_3","uri":"capability://memory.knowledge.pre.built.domain.models.for.common.use.cases","name":"pre-built domain models for common use cases","description":"Library of pre-trained intent and entity models for vertical-specific domains (e-commerce, banking, customer service, travel, food delivery) that can be deployed immediately without custom training. These models include domain-specific intents (e.g., 'book_flight', 'check_account_balance', 'track_order'), entities (e.g., 'destination', 'account_type', 'order_id'), and dialogue flows optimized for each vertical, reducing time-to-deployment from weeks to days.","intents":["Launch a conversational AI assistant in days without building custom NLU models from scratch","Reuse proven intent and entity schemas from industry best practices","Reduce training data collection and annotation effort by starting with pre-trained models","Deploy domain-specific dialogue flows without custom development"],"best_for":["Startups and SMBs with limited ML expertise needing rapid MVP deployment","Enterprises building vertical-specific assistants (e-commerce, banking, telecom)","Teams without dedicated data science resources for model training"],"limitations":["Pre-built models may not cover niche or highly specialized use cases; customization required for domain-specific terminology","Models are generic across the vertical; may require fine-tuning for specific business logic or workflows","Limited to supported verticals; custom domains require manual model development","Pre-built models may have lower accuracy on edge cases specific to your business"],"requires":["Selection of applicable domain from available pre-built models","API key and access to Conva.ai platform","Optional: training data for fine-tuning if customization is needed"],"input_types":["domain selection parameter","optional custom training data for fine-tuning"],"output_types":["deployed NLU model with domain-specific intents and entities","dialogue flow templates"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_conva-ai__cap_4","uri":"capability://data.processing.analysis.intent.and.entity.extraction.with.confidence.scoring","name":"intent and entity extraction with confidence scoring","description":"NLU module that parses user input to identify the user's intent (what they want to do) and extracts relevant entities (parameters needed to fulfill the intent), returning structured JSON with confidence scores for each extraction. The system uses neural sequence labeling for entity extraction and intent classification, providing confidence thresholds that allow applications to handle low-confidence predictions by requesting clarification or escalating to human agents.","intents":["Extract structured data from unstructured user messages for downstream processing","Determine user intent to route conversations to appropriate handlers or workflows","Identify and extract key parameters (dates, amounts, product names) from user input","Implement confidence-based fallback logic to handle uncertain predictions gracefully"],"best_for":["Developers building intent-driven conversation flows","Teams implementing confidence-based routing to human agents","Applications requiring structured data extraction from conversational input"],"limitations":["Accuracy depends on training data quality; low-resource domains may have lower extraction accuracy","Confidence scores are calibrated for the training distribution; may not be reliable for out-of-distribution inputs","Multi-entity extraction in complex sentences may have lower accuracy than single-entity extraction","Requires clear intent definitions and entity schemas; ambiguous or overlapping intents reduce accuracy"],"requires":["Intent and entity schema definition (list of possible intents and entity types)","Training data or use of pre-built domain models","API endpoint for intent/entity extraction"],"input_types":["user message (text)","optional context or previous turns"],"output_types":["structured JSON with intent, confidence score, and extracted entities","entity type and value pairs with position information"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_conva-ai__cap_5","uri":"capability://automation.workflow.dialogue.flow.builder.with.visual.workflow.design","name":"dialogue flow builder with visual workflow design","description":"Low-code interface for designing multi-turn conversation flows using a visual node-and-edge graph editor, where nodes represent dialogue states (user input, bot response, decision branches) and edges represent transitions. Developers can define branching logic, slot-filling sequences, and fallback paths without writing code, with the builder generating executable dialogue specifications that the runtime engine interprets.","intents":["Design complex multi-turn conversations without writing dialogue management code","Implement branching logic and conditional flows based on user input or extracted entities","Create slot-filling dialogues that guide users through required information collection","Visualize and test conversation flows before deployment"],"best_for":["Non-technical dialogue designers and business analysts","Teams building conversation flows rapidly without backend development","Enterprises managing multiple dialogue variants for A/B testing"],"limitations":["Visual builder may not support complex programmatic logic; advanced use cases require custom code","No version control or collaborative editing; difficult for teams to work on flows simultaneously","Limited debugging and testing tools; testing flows requires manual interaction","Generated dialogue specifications may not be human-readable or easily version-controlled"],"requires":["Access to Conva.ai web console or builder interface","Basic understanding of dialogue flow concepts (intents, entities, states)","Browser with JavaScript support"],"input_types":["visual node definitions","transition rules and conditions"],"output_types":["executable dialogue flow specification","deployable conversation model"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_conva-ai__cap_6","uri":"capability://tool.use.integration.api.based.integration.with.mobile.and.web.applications","name":"api-based integration with mobile and web applications","description":"RESTful and SDK-based integration layer that allows developers to embed Conva.ai NLU and dialogue capabilities into native iOS/Android apps and web applications. The platform provides language-specific SDKs (iOS, Android, JavaScript) that handle audio capture, API communication, and response rendering, with built-in error handling, retry logic, and offline fallbacks.","intents":["Integrate conversational AI into existing mobile apps without rebuilding from scratch","Add voice and text chat capabilities to web applications with minimal code","Handle audio streaming and API communication transparently through SDK abstractions","Manage authentication, rate limiting, and error handling automatically"],"best_for":["Mobile app developers adding conversational features to existing apps","Web developers integrating chat interfaces into web applications","Teams with limited backend infrastructure needing managed API endpoints"],"limitations":["Requires network connectivity; no offline conversation capability","SDK abstractions add ~100-200ms latency per API call compared to direct HTTP requests","Limited customization of UI/UX; SDKs provide default chat interfaces that may not match app design","API rate limits and quota management required; high-volume applications may incur additional costs"],"requires":["API key from Conva.ai platform","Supported platform: iOS 12+, Android 6+, or modern web browser (Chrome, Safari, Firefox)","Network connectivity to Conva.ai servers","SDK installation via CocoaPods (iOS), Gradle (Android), or npm (JavaScript)"],"input_types":["user message (text or audio)","session metadata"],"output_types":["assistant response (text or audio)","structured intent and entity data"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_conva-ai__cap_7","uri":"capability://text.generation.language.sentiment.analysis.and.user.emotion.detection","name":"sentiment analysis and user emotion detection","description":"NLU module that analyzes user messages to detect emotional tone and sentiment (positive, negative, neutral, frustrated, satisfied), enabling applications to respond contextually or escalate to human agents when users are dissatisfied. The system uses language-specific sentiment models trained on conversational data, with support for sarcasm and context-dependent sentiment in Indian languages.","intents":["Detect user frustration and automatically escalate conversations to human support agents","Adjust bot responses based on user sentiment (e.g., more empathetic tone for frustrated users)","Monitor conversation quality and identify dissatisfied customers for follow-up","Implement sentiment-based routing to specialized support teams"],"best_for":["Customer service chatbots needing to detect and respond to user frustration","Support platforms requiring automatic escalation triggers","Enterprises monitoring customer satisfaction through conversational AI"],"limitations":["Sentiment detection is less accurate for sarcasm and context-dependent emotions","Language-specific models may have lower accuracy on code-mixed text (Hinglish)","Sentiment is inferred from text alone; tone of voice in speech is not analyzed","False positives may trigger unnecessary escalations if confidence thresholds are too low"],"requires":["User message input (text or transcribed speech)","Sentiment analysis enabled in API request"],"input_types":["user message (text)"],"output_types":["sentiment label (positive, negative, neutral, frustrated, satisfied)","confidence score for sentiment prediction"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_conva-ai__cap_8","uri":"capability://data.processing.analysis.custom.entity.recognition.and.slot.filling","name":"custom entity recognition and slot filling","description":"Framework for defining custom entity types and implementing slot-filling dialogues that guide users through providing required information. Developers define entity schemas (e.g., 'flight_destination', 'departure_date', 'passenger_count') and the system automatically extracts these from user input, prompts for missing slots, and validates extracted values against business rules.","intents":["Implement guided conversations that collect required information step-by-step","Extract domain-specific entities (flight routes, account numbers, product SKUs) from user input","Validate extracted entities against business rules (e.g., departure date must be in future)","Handle partial information and ask clarifying questions for missing or ambiguous slots"],"best_for":["E-commerce and booking platforms needing structured information collection","Banking and financial services requiring accurate data extraction","Customer service systems collecting support tickets with required fields"],"limitations":["Requires upfront definition of entity schemas; adding new entity types requires retraining or manual updates","Slot filling may feel rigid or unnatural if not carefully designed; users may resist guided information collection","Validation rules are static; complex business logic requires custom code","Extraction accuracy depends on entity definition clarity; ambiguous entity types reduce accuracy"],"requires":["Entity schema definition (entity types, examples, validation rules)","Training data or use of pre-built domain models with predefined entities","Optional: custom validation logic for business rules"],"input_types":["user message (text)","entity schema definition"],"output_types":["extracted entity values","slot-filling status (complete/incomplete)","validation results"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_conva-ai__cap_9","uri":"capability://data.processing.analysis.conversation.analytics.and.performance.monitoring","name":"conversation analytics and performance monitoring","description":"Dashboard and analytics engine that tracks conversation metrics (total conversations, average turn count, intent distribution, user satisfaction, escalation rate) and provides insights into bot performance. The system logs all conversations (with privacy controls), identifies common failure patterns, and surfaces recommendations for dialogue improvement based on conversation data.","intents":["Monitor bot performance and identify conversations that failed or required escalation","Analyze intent distribution to understand user needs and prioritize feature development","Track user satisfaction metrics and identify conversations with dissatisfied users","Identify common dialogue failures and get recommendations for improvement"],"best_for":["Product teams iterating on conversational AI based on usage data","Support teams monitoring bot effectiveness and escalation patterns","Enterprises tracking KPIs for conversational AI ROI"],"limitations":["Analytics are aggregated; individual conversation debugging requires separate conversation logs","Recommendations are heuristic-based; may not capture complex dialogue patterns","Privacy controls required for conversation logging; GDPR/data residency compliance needed","Real-time analytics may have latency; historical analysis is more reliable"],"requires":["Conversations logged to Conva.ai platform","Access to analytics dashboard (web console)","Privacy policy and data retention settings configured"],"input_types":["conversation logs","user feedback (optional)"],"output_types":["analytics dashboard with metrics and charts","conversation transcripts and logs","improvement recommendations"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["API key from Conva.ai platform","Language code specification in request (e.g., 'hi' for Hindi, 'ta' for Tamil)","HTTPS endpoint connectivity","Client-side audio capture capability (mobile OS or web browser with Web Audio API)","Network connectivity to Conva.ai servers","Microphone permissions granted by user","Supported audio format (WAV, MP3, or raw PCM)","Integration with external helpdesk, live chat, or CRM system","API or webhook endpoint for escalation trigger","Agent system capable of receiving conversation context"],"failure_modes":["Limited to 6-8 Indian languages; does not cover all regional languages in India","Performance on heavily code-mixed text (Hinglish) may degrade compared to pure Hindi or English","Training data for regional languages is smaller than English, potentially affecting accuracy on domain-specific terminology","Requires microphone permissions and audio input capability on client device","Background noise and poor audio quality degrade ASR accuracy; no built-in noise cancellation","Latency depends on network connectivity; offline voice processing not supported","ASR accuracy varies by language and accent; regional accents may have lower recognition rates","Requires integration with external helpdesk or live chat system; no built-in agent management","Escalation logic is rule-based; may not optimize for agent availability or skill matching","Conversation context transfer depends on integration quality; some information may be lost","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.78,"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":"2026-05-24T12:16:30.281Z","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=conva-ai","compare_url":"https://unfragile.ai/compare?artifact=conva-ai"}},"signature":"ilnAVxH5lF+HcpNwX9jJgOQVCeHYBf5GehNOeloYqsXEfFT0AXHhtLrWjWSIFnC73oHaB8ZrpxuyMtpBhXMnCQ==","signedAt":"2026-06-22T23:43:53.540Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/conva-ai","artifact":"https://unfragile.ai/conva-ai","verify":"https://unfragile.ai/api/v1/verify?slug=conva-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"}}