What is?
Predictable Machines’ Model Context Protocol (MCP) is an extension of the open MCP standard, designed to expose our research and verification services as discoverable, invocable tools for any AI assistant or LLM that supports the protocol.
Predictable Machines’ Model Context Protocol (MCP) is an extension of the open MCP standard, designed to expose our research and verification services as discoverable, invocable tools for any AI assistant or LLM that supports the protocol.
Key Features
Through MCP, we deliver bespoke AI tool integrations across any industry:
Containerized MCP Servers: Deploy behind corporate auth (OAuth2, SAML, API keys) for secure access.
Custom Connectors: Build adapters with our SDKs to integrate tool calls into proprietary workflows (e.g., legal review, financial analytics).
Agent Augmentation: Wire MCP clients into chatbots or RPA bots to surface verified outputs inline.
Pipeline Orchestration: Embed MCP calls in Airflow, Kubeflow, or Prefect for batch and streaming verification.
Domain Extensions: Extend MCP schemas with industry-specific parameters (e.g., compliance checks, spec validation).
Observability & Audit: Leverage Prometheus metrics and centralized logs to monitor usage and maintain governance.
Features this MVP Interface Could Offer:
Source traceability ("Where does this info come from?")
Confidence scores or reliability metrics
Side-by-side comparison of original vs. generated/processed info
Verification workflows (approve, flag, reject)
Audit trail of changes/edits
Data masking/unmasking for privacy compliance
Through MCP, we deliver bespoke AI tool integrations across any industry:
Containerized MCP Servers: Deploy behind corporate auth (OAuth2, SAML, API keys) for secure access.
Custom Connectors: Build adapters with our SDKs to integrate tool calls into proprietary workflows (e.g., legal review, financial analytics).
Agent Augmentation: Wire MCP clients into chatbots or RPA bots to surface verified outputs inline.
Pipeline Orchestration: Embed MCP calls in Airflow, Kubeflow, or Prefect for batch and streaming verification.
Domain Extensions: Extend MCP schemas with industry-specific parameters (e.g., compliance checks, spec validation).
Observability & Audit: Leverage Prometheus metrics and centralized logs to monitor usage and maintain governance.
Features this MVP Interface Could Offer:
Source traceability ("Where does this info come from?")
Confidence scores or reliability metrics
Side-by-side comparison of original vs. generated/processed info
Verification workflows (approve, flag, reject)
Audit trail of changes/edits
Data masking/unmasking for privacy compliance
Technologies and integrations
Tool Catalog** (`/tools/list`): Lists available services (fact-checking, logical assertions, dataset anchoring, hypothesis testing) with metadata and schemas.
Invocation API** (`/tools/call`): Accepts JSON-RPC or gRPC calls, executes research pipelines, and returns results with execution logs and provenance.
Resource Gateway** (`/resources/get`): Provides grounding data (documents, corpora, knowledge graphs) on demand.
Context Injection**: Middleware captures tool outputs and injects verified results back into the LLM context.
Protocol Adapters**: SDKs in Kotlin, Java, and Python, plus a CLI, to simplify integration.
Tool Catalog** (`/tools/list`): Lists available services (fact-checking, logical assertions, dataset anchoring, hypothesis testing) with metadata and schemas.
Invocation API** (`/tools/call`): Accepts JSON-RPC or gRPC calls, executes research pipelines, and returns results with execution logs and provenance.
Resource Gateway** (`/resources/get`): Provides grounding data (documents, corpora, knowledge graphs) on demand.
Context Injection**: Middleware captures tool outputs and injects verified results back into the LLM context.
Protocol Adapters**: SDKs in Kotlin, Java, and Python, plus a CLI, to simplify integration.
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.
