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| Pricing | Paid | Free |
| Capabilities | 11 decomposed | 14 decomposed |
| Times Matched | 0 | 0 |
Converts natural language questions into executable smart contract queries using LLM-based semantic parsing and contract ABI schema mapping. The system analyzes user intent, maps it to contract function signatures, and generates optimized query parameters without requiring developers to write low-level blockchain code. This reduces friction for Web3 developers unfamiliar with contract ABIs and RPC call semantics.
Unique: Uses contract ABI schema-aware LLM prompting with parameter validation against function signatures, ensuring generated queries are syntactically valid before execution — unlike generic LLM-to-SQL approaches that require post-hoc validation
vs alternatives: Faster developer onboarding than The Graph's GraphQL schema learning curve, and more flexible than hardcoded query templates since it adapts to arbitrary contract ABIs
Maintains a distributed cache of frequently-accessed blockchain state (balances, allowances, contract storage) with automatic invalidation on new block finality. Uses event-driven architecture to subscribe to contract logs and update cached state incrementally rather than re-scanning the entire chain. Implements multi-level caching (in-memory, Redis, persistent) with configurable TTLs to balance freshness vs query latency.
Unique: Event-driven incremental indexing with multi-level cache hierarchy (in-memory → Redis → persistent) and automatic reorg detection, rather than full-chain rescans like traditional RPC-based approaches or static snapshot indexing like The Graph
vs alternatives: Significantly faster query response times than direct RPC calls (10-100x improvement), and more cost-effective than running dedicated indexing nodes while maintaining real-time freshness guarantees
Maintains immutable audit logs of all blockchain data queries and modifications, tracking who accessed what data, when, and for what purpose. Links query results back to source transactions and blocks, enabling data lineage tracing. Integrates with compliance frameworks (SOX, HIPAA) to generate audit reports for regulatory purposes.
Unique: Immutable audit logs with data lineage tracing back to source transactions and compliance report generation, rather than basic query logging or manual audit trail maintenance
vs alternatives: Provides regulatory-grade audit trails that raw blockchain data access lacks, and automates compliance reporting that would otherwise require manual effort
Validates zero-knowledge proofs embedded in blockchain transactions to verify sensitive data (private balances, confidential transactions) without exposing the underlying plaintext. Implements proof verification circuits compatible with major ZK frameworks (Circom, Cairo, Noir) and validates proofs against on-chain commitment roots. Enables querying encrypted blockchain state while maintaining cryptographic privacy guarantees.
Unique: Integrates multiple ZK proof verification backends (Groth16, PLONK, custom circuits) with on-chain commitment validation, enabling privacy-preserving queries across heterogeneous ZK protocols rather than single-protocol support
vs alternatives: Enables privacy-preserving analytics on encrypted blockchain data that traditional indexers like The Graph cannot access, while maintaining cryptographic guarantees stronger than application-level encryption
Applies declarative validation rules to blockchain data before returning query results, ensuring type correctness, value bounds, and business logic invariants. Uses a schema definition language to specify expected data types, ranges, and relationships across contract state. Validates decoded contract storage and function outputs against these schemas, catching data corruption or contract bugs before they propagate to applications.
Unique: Declarative schema-based validation with automatic type binding generation for multiple languages, combined with on-chain state verification — unlike generic JSON schema validators that lack blockchain-specific invariant checking
vs alternatives: Catches contract state anomalies that raw RPC queries would miss, and provides stronger guarantees than application-level validation by validating at the data ingestion layer
Abstracts away chain-specific differences (RPC endpoints, block times, finality rules) and provides a unified query interface across Ethereum, Polygon, Arbitrum, Optimism, and other EVM chains. Handles chain-specific quirks (different block confirmation times, reorg depths) transparently and returns results with consistent schemas. Supports cross-chain state queries by coordinating requests across multiple chains and merging results.
Unique: Unified query abstraction with automatic chain-specific RPC routing and result schema normalization, handling finality and reorg semantics per-chain rather than exposing raw RPC differences to applications
vs alternatives: Eliminates boilerplate for multi-chain applications compared to managing separate RPC connections, and provides more consistent semantics than chain-specific indexers like The Graph (which requires separate subgraphs per chain)
Analyzes incoming queries and recommends optimizations (batching, caching, index selection) to minimize RPC calls and associated costs. Estimates gas costs and RPC provider fees before query execution and suggests alternative query patterns with lower costs. Uses historical query patterns and chain state analysis to predict optimal execution strategies.
Unique: Combines query analysis with RPC provider pricing models and historical execution patterns to generate cost-aware optimization recommendations, rather than generic query optimization that ignores blockchain-specific economics
vs alternatives: Provides cost visibility and optimization that raw RPC calls lack, and more accurate estimates than generic database query planners since it understands blockchain-specific cost drivers (block finality, reorg handling)
Stores sensitive blockchain metadata (private keys, transaction signing data, user identifiers) in encrypted vaults with encryption-at-rest and encryption-in-transit. Uses envelope encryption with key derivation from user credentials, ensuring PublicAI cannot access plaintext data. Integrates with hardware security modules (HSMs) for key management in enterprise deployments.
Unique: Envelope encryption with user-controlled key derivation and optional HSM integration, ensuring PublicAI cannot access plaintext even with database compromise — unlike application-level encryption that requires key management by users
vs alternatives: Provides stronger security guarantees than unencrypted storage, and more operational simplicity than client-side encryption since encryption/decryption is handled transparently by PublicAI
+3 more capabilities
Converts natural language application descriptions into executable database schemas by parsing user intent through an LLM pipeline, inferring entity relationships, cardinality, and data types without manual schema definition. The system likely uses prompt engineering to constrain schema generation to Bubble's supported data model, then validates and materializes schemas in Bubble's backend database layer.
Unique: Integrates LLM-driven schema inference directly into Bubble's visual database builder, allowing non-technical users to generate normalized schemas through conversational prompts rather than manual table/field creation or SQL DDL statements
vs alternatives: Faster than traditional database design tools (Lucidchart, dbdiagram.io) for non-technical users because it eliminates the need to learn ER diagram syntax or database normalization rules
Translates natural language descriptions of application workflows (user actions, conditional logic, data transformations, multi-step processes) into executable Bubble workflows without requiring visual workflow builder expertise. The system maps user intent to Bubble's workflow primitives (actions, conditions, loops, API calls) through LLM-guided code generation, then validates and deploys workflows to Bubble's serverless execution layer.
Unique: Generates complete workflow definitions including conditional branching, loops, and API calls from natural language, mapping user intent to Bubble's visual workflow primitives without requiring users to interact with the workflow builder UI
vs alternatives: More accessible than Zapier or Make for complex multi-step workflows because it generates logic from natural language rather than requiring users to manually chain actions and configure conditions through a visual interface
Bubble AI scores higher at 71/100 vs PublicAI at 44/100. Bubble AI also has a free tier, making it more accessible.
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Automatically generates data entry forms with built-in validation rules, error messages, and user feedback mechanisms inferred from the database schema and workflow requirements. The system maps schema field types and constraints to appropriate form inputs (text fields, dropdowns, date pickers, etc.), generates validation rules, and creates error handling workflows that provide users with clear feedback on submission failures.
Unique: Automatically generates form components with validation rules and error handling inferred from database schema constraints and workflow requirements, eliminating manual form configuration and validation logic implementation
vs alternatives: Simpler than manual form development in traditional frameworks because it automatically generates validation rules from schema constraints, whereas traditional development requires explicit validation configuration in form code
Automatically generates user authentication systems (signup, login, password reset) and role-based access control (RBAC) workflows based on natural language descriptions of user types and permissions. The system infers authentication requirements from application descriptions, generates secure authentication flows, and creates authorization rules that restrict access to features and data based on user roles.
Unique: Automatically generates complete authentication and authorization systems including signup, login, password reset, and role-based access control from natural language descriptions, eliminating manual implementation of security-critical authentication logic
vs alternatives: More secure than manual authentication implementation for non-technical users because it uses Bubble's built-in security features, whereas manual implementation is prone to security vulnerabilities (weak password hashing, SQL injection, etc.)
Automatically generates reports and data export functionality that allows users to export application data in standard formats (CSV, PDF, Excel) and view summarized data through generated dashboards and charts. The system infers reporting requirements from the application schema and workflows, generates report templates, and creates export workflows that transform application data into user-friendly formats.
Unique: Automatically generates reports, dashboards, and data export workflows from natural language descriptions, inferring aggregations and visualizations from application schema without requiring manual report design or data transformation logic
vs alternatives: Faster than manual report development in traditional BI tools (Tableau, Power BI) because it automatically generates reports from application data, whereas traditional BI tools require separate data modeling and report configuration
Enables multiple users to collaborate on application development through shared editing of generated applications, with real-time synchronization of changes and conflict resolution. The system maintains a shared application state that updates in real-time as team members make modifications through the visual editor or natural language prompts, allowing teams to build applications collaboratively without version control complexity.
Unique: Provides real-time collaborative editing of generated applications with automatic synchronization across team members, eliminating version control complexity and merge conflict management required in traditional development
vs alternatives: Simpler than traditional collaborative development (Git, GitHub) for non-technical teams because it provides real-time synchronization without version control concepts, whereas traditional development requires understanding branching, merging, and conflict resolution
Automatically generates responsive web UI components (pages, forms, tables, navigation, layouts) from natural language descriptions of application screens and user interactions. The system infers component hierarchy, styling, and responsive breakpoints through LLM analysis, then materializes components in Bubble's visual design system with built-in mobile responsiveness and accessibility features.
Unique: Generates complete responsive UI layouts from natural language by inferring component hierarchy, spacing, and breakpoints, then materializes them in Bubble's visual design system with automatic mobile responsiveness rather than requiring manual component placement and styling
vs alternatives: Faster than traditional UI design tools (Figma, Adobe XD) for non-technical users because it eliminates design tool learning curve and automatically handles responsive breakpoints, whereas design tools require manual layout work for each breakpoint
Orchestrates end-to-end application generation by coordinating database schema creation, workflow generation, and UI component generation from a single natural language application description. The system decomposes user intent into sub-tasks (data modeling, business logic, interface design), executes each through specialized LLM pipelines, then integrates outputs into a cohesive, deployable application with pre-configured data bindings and workflow triggers.
Unique: Coordinates multi-stage LLM-driven generation (schema → workflows → UI) from a single prompt, automatically integrating outputs with data bindings and event triggers, eliminating the need for users to manually connect database to business logic to UI
vs alternatives: Dramatically faster than traditional full-stack development (weeks to months) because it generates database, backend logic, and frontend UI simultaneously from natural language, whereas traditional development requires sequential phases of design, implementation, and integration
+6 more capabilities