What is?
Predictable Research is a robust, enterprise-ready platform that combines large language models with rigorous verification workflows. It empowers users to interrogate LLMs like OpenAI, Anthropic Claude, or Perplexity with traceability, structure, and control—transforming AI outputs into reliable, auditable insights.
With Predictable Research, you get more than answers: you get verified knowledge. Each conclusion undergoes automated fact-checking, logical consistency tests, and hypothesis-driven experiments, all anchored to source data and tracked through detailed provenance.
Predictable Research is a robust, enterprise-ready platform that combines large language models with rigorous verification workflows. It empowers users to interrogate LLMs like OpenAI, Anthropic Claude, or Perplexity with traceability, structure, and control—transforming AI outputs into reliable, auditable insights.
With Predictable Research, you get more than answers: you get verified knowledge. Each conclusion undergoes automated fact-checking, logical consistency tests, and hypothesis-driven experiments, all anchored to source data and tracked through detailed provenance.
Key Features
Multi-Model Support: Seamlessly switch between LLMs (OpenAI, Anthropic Claude, Perplexity) and compare results.
Test Layer Control: Use predefined or custom test suites to challenge AI outputs at varying depths.
Dataset Anchoring: Attach reference corpora or live data feeds to ground model responses in authoritative sources.
Verification Framework: Link every output to formal checks, logic rules, and traceable reasoning paths.
Collaborative Environment: Share, comment, and review research threads in real-time with teammates.
API-First Design: Integrate Predictable Research into your systems via REST or gRPC with minimal overhead.
Multi-Model Support: Seamlessly switch between LLMs (OpenAI, Anthropic Claude, Perplexity) and compare results.
Test Layer Control: Use predefined or custom test suites to challenge AI outputs at varying depths.
Dataset Anchoring: Attach reference corpora or live data feeds to ground model responses in authoritative sources.
Verification Framework: Link every output to formal checks, logic rules, and traceable reasoning paths.
Collaborative Environment: Share, comment, and review research threads in real-time with teammates.
API-First Design: Integrate Predictable Research into your systems via REST or gRPC with minimal overhead.
Technologies and integrations
Predictable Research is built for precision and extensibility with:
TypeScript for front-end and orchestration logic.
Kotlin for backend services, offering concise syntax and strong type safety.
Rust for performance-critical validation tasks.
Formal Methods to define and enforce reasoning patterns.
Functional Programming paradigms for deterministic, composable pipelines.
Flexible LLM Integrations to support multiple model providers.
Dataset Connectors for ingesting structured data, documents, or knowledge graphs.
Test Level Selector to configure the strictness of verification checks.
Predictable Research is built for precision and extensibility with:
TypeScript for front-end and orchestration logic.
Kotlin for backend services, offering concise syntax and strong type safety.
Rust for performance-critical validation tasks.
Formal Methods to define and enforce reasoning patterns.
Functional Programming paradigms for deterministic, composable pipelines.
Flexible LLM Integrations to support multiple model providers.
Dataset Connectors for ingesting structured data, documents, or knowledge graphs.
Test Level Selector to configure the strictness of verification checks.
Use cases
Real-Time Fact Checking for newsrooms and compliance teams.
Complex Document Analysis in legal, technical, or scientific domains.
Collaborative Research Workflows for distributed teams.
Custom Dataset Grounding in specialized fields like healthcare, finance, or law.
Automated Hypothesis Testing to accelerate data-driven decision making.
Real-Time Fact Checking for newsrooms and compliance teams.
Complex Document Analysis in legal, technical, or scientific domains.
Collaborative Research Workflows for distributed teams.
Custom Dataset Grounding in specialized fields like healthcare, finance, or law.
Automated Hypothesis Testing to accelerate data-driven decision making.
