What is?
Predictable Machines implements Model Context Protocol (MCP) servers expose our research and verification tools as discoverable, invocable services for any AI assistant or LLM that supports the MCP standard.
Predictable Machines implements Model Context Protocol (MCP) servers expose our research and verification tools as discoverable, invocable services for any AI assistant or LLM that supports the MCP standard.
Key Features
Through MCP integration, we provide verification capabilities directly within AI assistant workflows:
Dockerized Verification Tools: 17+ containerized tools (Arxiv, Firecrawl, Logic Solvers, Brave Search) deployable as MCP servers with automated discovery and health monitoring.
Real-time Research Workflows: Hierarchical task breakdown with live streaming progress, allowing AI assistants to conduct multi-step research with full transparency and intermediate results.
TypeScript Integration Libraries: Production-ready clients enabling seamless integration of verification workflows into web applications with full type safety and event streaming.
Streaming Verification Pipelines: Server-Sent Events architecture providing real-time fact-checking, logical validation, and mathematical verification with complete audit trails.
Interactive Research Steppers: Advanced UI components showing live research progress, tool invocations, and hierarchical task relationships with expandable detail views.
Enterprise Authentication: OAuth2 and session management supporting multi-tenancy with persistent conversation history and user-specific tool configurations.
Core Platform Features:
Complete Source Provenance: Every verification result includes full source attribution, reasoning chains, and evidence links with clickable references.
Real-time Progress Visualization: Interactive steppers showing research task hierarchies, tool call status, and content accumulation as it happens.
Multi-layered Verification: A Combination of factual verification (web search), logical validation (SMT solvers), and mathematical checking (computational tools), and many more.
Persistent Audit Trails: Complete conversation history with verification decisions, tool usage, and reasoning transparency stored with full traceability.
Evidence-based Confidence Scoring: Verification results include confidence levels based on source authority, logical consistency, and cross-validation across multiple tools.
Through MCP integration, we provide verification capabilities directly within AI assistant workflows:
Dockerized Verification Tools: 17+ containerized tools (Arxiv, Firecrawl, Logic Solvers, Brave Search) deployable as MCP servers with automated discovery and health monitoring.
Real-time Research Workflows: Hierarchical task breakdown with live streaming progress, allowing AI assistants to conduct multi-step research with full transparency and intermediate results.
TypeScript Integration Libraries: Production-ready clients enabling seamless integration of verification workflows into web applications with full type safety and event streaming.
Streaming Verification Pipelines: Server-Sent Events architecture providing real-time fact-checking, logical validation, and mathematical verification with complete audit trails.
Interactive Research Steppers: Advanced UI components showing live research progress, tool invocations, and hierarchical task relationships with expandable detail views.
Enterprise Authentication: OAuth2 and session management supporting multi-tenancy with persistent conversation history and user-specific tool configurations.
Core Platform Features:
Complete Source Provenance: Every verification result includes full source attribution, reasoning chains, and evidence links with clickable references.
Real-time Progress Visualization: Interactive steppers showing research task hierarchies, tool call status, and content accumulation as it happens.
Multi-layered Verification: A Combination of factual verification (web search), logical validation (SMT solvers), and mathematical checking (computational tools), and many more.
Persistent Audit Trails: Complete conversation history with verification decisions, tool usage, and reasoning transparency stored with full traceability.
Evidence-based Confidence Scoring: Verification results include confidence levels based on source authority, logical consistency, and cross-validation across multiple tools.
Technologies and integrations
MCP Server Implementation: Dockerized MCP servers exposing 17+ verification tools (Arxiv, Firecrawl, Logic Solvers, Code Interpreter) with standardized tool discovery and invocation.
Server-Sent Events API: Real-time streaming endpoints that execute verification workflows and return live progress updates with complete event hierarchies and provenance tracking.
Docker Tool Registry: Containerized verification tools with automated deployment, health monitoring, and dynamic tool selection based on verification requirements.
TypeScript Client Libraries: Production-ready streaming clients with full type safety, automatic reconnection, event processing, and sophisticated error handling for frontend integration.
Kotlin Backend Services: Type-safe verification orchestration with functional programming patterns, workflow management, and comprehensive logging for enterprise reliability.
MCP Server Implementation: Dockerized MCP servers exposing 17+ verification tools (Arxiv, Firecrawl, Logic Solvers, Code Interpreter) with standardized tool discovery and invocation.
Server-Sent Events API: Real-time streaming endpoints that execute verification workflows and return live progress updates with complete event hierarchies and provenance tracking.
Docker Tool Registry: Containerized verification tools with automated deployment, health monitoring, and dynamic tool selection based on verification requirements.
TypeScript Client Libraries: Production-ready streaming clients with full type safety, automatic reconnection, event processing, and sophisticated error handling for frontend integration.
Kotlin Backend Services: Type-safe verification orchestration with functional programming patterns, workflow management, and comprehensive logging for enterprise reliability.
Use cases
Through the verification technology (Predictable Research), we are working to offer different modules to integrate in your AI automatization, in order to offer a layer of verification of the information in different fields
1. Legal & Compliance
Due diligence checks (e.g., M&A, investment vetting): Verify the origin and consistency of documents.
Regulatory compliance: Ensure policies and claims align with legal frameworks (e.g., GDPR, HIPAA).
Sensitive document validation: Verify authenticity and changes in NDAs, contracts, or regulatory filings.
2. Healthcare & Life Sciences
Medical research verification: Confirm the sources and consistency of studies or trial data.
Drug information validation: Ensure that any AI-generated summaries of drug effects or interactions are aligned with approved medical literature.
Patient data handling: Verify that patient info shared or generated by AI complies with privacy standards.
3. Finance & Insurance
KYC & AML verification: Double-check customer-submitted documents or information against known databases.
Sensitive investment reports: Make sure financial insights generated or quoted from models are grounded in real, trusted sources.
Claims processing: Validate the data that feeds into automated decision-making in underwriting or claims.
4. Journalism & Media
Fact-checking interface: Allow journalists to test the validity of claims or documents from sources.
Source credibility scoring: Use the interface to trace and evaluate original sources of sensitive news stories.
AI-assisted investigation: Cross-check leaked or sensitive info against verified corpuses (e.g., court documents, financial records).
5. Education & Research
Academic paper vetting: Validate references and citation quality in sensitive or controversial research topics.
Plagiarism detection + source verification: Combine semantic similarity with source authenticity.
Historical/archival truth-checking: Cross-verify timelines, quotes, and sources, especially in political or war-related research.
Through the verification technology (Predictable Research), we are working to offer different modules to integrate in your AI automatization, in order to offer a layer of verification of the information in different fields
1. Legal & Compliance
Due diligence checks (e.g., M&A, investment vetting): Verify the origin and consistency of documents.
Regulatory compliance: Ensure policies and claims align with legal frameworks (e.g., GDPR, HIPAA).
Sensitive document validation: Verify authenticity and changes in NDAs, contracts, or regulatory filings.
2. Healthcare & Life Sciences
Medical research verification: Confirm the sources and consistency of studies or trial data.
Drug information validation: Ensure that any AI-generated summaries of drug effects or interactions are aligned with approved medical literature.
Patient data handling: Verify that patient info shared or generated by AI complies with privacy standards.
3. Finance & Insurance
KYC & AML verification: Double-check customer-submitted documents or information against known databases.
Sensitive investment reports: Make sure financial insights generated or quoted from models are grounded in real, trusted sources.
Claims processing: Validate the data that feeds into automated decision-making in underwriting or claims.
4. Journalism & Media
Fact-checking interface: Allow journalists to test the validity of claims or documents from sources.
Source credibility scoring: Use the interface to trace and evaluate original sources of sensitive news stories.
AI-assisted investigation: Cross-check leaked or sensitive info against verified corpuses (e.g., court documents, financial records).
5. Education & Research
Academic paper vetting: Validate references and citation quality in sensitive or controversial research topics.
Plagiarism detection + source verification: Combine semantic similarity with source authenticity.
Historical/archival truth-checking: Cross-verify timelines, quotes, and sources, especially in political or war-related research.
