Chatbuddy

Delivers real-time AI-powered conversational responses directly within WhatsApp's messaging interface using webhook-based message routing and LLM backend integration. Messages are intercepted via WhatsApp Business API webhooks, routed to an LLM inference engine (likely OpenAI, Anthropic, or similar), and responses are sent back through WhatsApp's message delivery system, eliminating context-switching between apps.

Classifies incoming WhatsApp messages into discrete task categories (summarization, content generation, Q&A, translation, etc.) and routes them to specialized prompt templates or backend handlers. Uses intent classification (likely via prompt engineering or fine-tuned classifier) to determine which capability to invoke, then executes the appropriate processing pipeline with task-specific parameters.

Allows users to define custom prompts or task templates that modify AI behavior for specific use cases, enabling power users to optimize responses without code. Likely stores user-defined prompts server-side and applies them as system instructions or context injection when matching requests are detected.

Accepts long-form text, articles, or message threads via WhatsApp and generates concise summaries while preserving key information and context. Likely uses extractive or abstractive summarization techniques (prompt-based or fine-tuned model) to condense content to a specified length while maintaining semantic coherence and actionable insights.

Generates original text content (emails, social media posts, creative writing, product descriptions, etc.) based on user prompts or brief specifications provided via WhatsApp. Uses prompt engineering or fine-tuned generation models to produce contextually appropriate, stylistically consistent output that can be directly copied and used from the chat interface.

Translates text between multiple languages (likely 50+ language pairs) using neural machine translation models, with results delivered as WhatsApp messages. Detects source language automatically or accepts explicit language specification, then routes to appropriate translation model (OpenAI, Google Translate API, or proprietary NMT backend) and returns translated text.

Maintains conversation history within a WhatsApp chat thread, allowing the AI to reference previous messages and provide contextually aware responses across multiple turns. Likely stores recent message history (last 10-50 messages) in session state or backend database, indexed by WhatsApp chat ID, and includes this context in each LLM prompt to enable coherent multi-turn dialogue.

Parses natural language input or documents to extract structured information (names, dates, amounts, entities, relationships) and returns it in organized format (JSON, tables, or formatted text). Uses prompt-based extraction or fine-tuned NER/relation extraction models to identify and structure relevant data from messy or free-form input.

Answers factual questions and provides information based on the LLM's training data or integrated knowledge sources. Routes questions to appropriate backend (general knowledge LLM, web search integration, or proprietary knowledge base) and returns accurate, cited answers within WhatsApp's message format.

Adjusts AI response style, formality, and tone based on user preferences or implicit context (professional vs casual, technical vs simplified, verbose vs concise). Likely implemented through system prompts or fine-tuning that encodes style preferences, allowing users to request responses in specific tones without explicit configuration.

Processes multiple messages or content items in sequence, applying the same operation (summarization, translation, generation, extraction) to each item and returning results in organized format. Likely implemented through message queue or batch processing backend that groups related requests and executes them sequentially or in parallel.