Zappr AI

Enables non-technical users to build multi-turn conversational agents by dragging and connecting pre-built functional blocks (150+ available) on a visual canvas without writing code. The platform orchestrates block execution sequentially or conditionally, routing user inputs through connected blocks (LLM agents, data lookups, integrations) and aggregating outputs into natural language responses. Block composition appears to follow a directed acyclic graph (DAG) pattern where each block declares input/output contracts and the engine validates connectivity before deployment.

Provides a library of pre-configured agent templates (inbound sales, support responder, appointment booking, lead qualification) that users can instantiate and customize without building from scratch. Templates encapsulate common block sequences, response patterns, and integration configurations (e.g., CRM field mappings) as reusable starting points. Users can clone a template, modify block parameters and data connections, and deploy within hours rather than designing workflows from first principles.

Offers a freemium pricing model where users can build and deploy agents for free up to certain limits (number of agents, conversation volume, features—specifics unknown), with paid tiers for higher usage or advanced features. Additionally, Zappr offers a revenue-share model where users (particularly agencies and white-label partners) can resell agents and share revenue with Zappr rather than paying fixed subscription fees. Pricing structure and tier details are not publicly disclosed; users must book a demo to see pricing.

Allows users to customize agent behavior by configuring parameters of individual blocks (e.g., LLM temperature, response tone, data field mappings, integration credentials) without modifying block logic or writing code. Each block exposes a set of configurable parameters in the UI (text fields, dropdowns, toggles); users adjust these parameters to tune agent behavior. Parameter changes take effect immediately or after redeployment; the underlying block implementation remains unchanged.

Provides a testing/preview mode where users can interact with agents in a sandbox environment before deploying to production channels. Users can send test messages, verify agent responses, and check integration behavior (CRM lookups, payment processing, etc.) without affecting real customers or data. Preview mode simulates the agent's behavior on different channels (web, SMS, WhatsApp, voice) and allows users to iterate on workflows before going live.

Deploys a single agent definition across multiple communication channels (website chat widget, SMS, WhatsApp, voice calls) without requiring separate agent implementations per channel. The platform abstracts channel-specific protocols (HTTP webhooks for web, Twilio-like APIs for SMS/WhatsApp, voice codec handling) behind a unified agent interface, translating user inputs to a canonical message format and routing agent outputs to the appropriate channel. Channel selection and configuration happen in the deployment UI; the underlying Routine Engine handles protocol translation.

Connects agents to external CRM systems, databases, and APIs through pre-built integration blocks that handle authentication, data querying, and record updates without requiring custom code. Integration blocks abstract away API complexity—users select a data source (e.g., Salesforce, HubSpot, custom database), authenticate via UI (OAuth or API key), and then use subsequent blocks to query or update records. The platform manages connection pooling, credential storage, and error handling for integrations; block outputs are structured data (JSON objects) that downstream blocks can consume.

Includes an OCR (Optical Character Recognition) block that agents can use to extract text from images or scanned documents, converting unstructured visual data into structured text that downstream blocks can process. The OCR block accepts image inputs (format unspecified), performs text extraction, and outputs recognized text as a string or structured data (if layout-aware OCR is used). This enables agents to handle document-based workflows (invoice processing, form extraction, ID verification) without manual transcription.

Includes a payment integration block that agents can use to collect payments directly within conversations, supporting payment processors (Stripe, PayPal, Square—specifics unknown). The block handles payment UI rendering (checkout form or payment link), transaction processing, and confirmation messaging without requiring users to leave the chat. Payment block outputs include transaction status, receipt data, and error messages that downstream blocks can use for order fulfillment or confirmation workflows.

Enables agents to accept voice input (phone calls or voice messages) and generate voice output (text-to-speech or pre-recorded audio) as part of multi-modal conversations. Voice input is converted to text via speech-to-text (STT) engine, processed through the agent workflow, and voice output is generated via text-to-speech (TTS) or played from pre-recorded audio files. Voice channel integration allows agents to be deployed as IVR (Interactive Voice Response) systems or voice assistants accessible via phone calls.

Offers three deployment models: (1) Cloud-hosted: Zappr manages infrastructure and agents run on Zappr servers; (2) Self-hosted: Users deploy agents on their own servers with full control over infrastructure and data; (3) White-label SaaS: Users resell Zappr as a branded platform under their own domain and branding. Each deployment model has different trade-offs around control, scalability, data residency, and operational overhead. Users select deployment model during agent configuration; the underlying Routine Engine and block definitions remain the same across deployment options.

Provides dashboards and reporting on agent performance metrics (conversation volume, resolution rate, average response time, user satisfaction) and conversation history (transcripts, user intents, agent responses). Analytics are aggregated by channel, time period, and agent type; users can drill down into individual conversations to debug agent behavior or identify improvement opportunities. Monitoring capabilities include real-time alerts for agent failures, high error rates, or unusual conversation patterns.

Maintains conversation context across multiple user turns, allowing agents to reference previous messages, user information, and conversation history when generating responses. The platform stores conversation state (user inputs, agent outputs, extracted data) in a session store and passes relevant context to the LLM agent block on each turn. Context window size and retention policy are undocumented, but the system appears to support multi-turn conversations with coherent context rather than treating each message as independent.