TIRO-Scientific
Enterprise autonomous research platform for deep literature review, verified citations, and high-precision scientific synthesis
Overview
Platform Vision
TIRO-Scientific v2.0 is designed not as a generic chatbot, but as an enterprise autonomous research assistant for academia and R&D. The platform accelerates literature review and document analysis across hundreds of sources simultaneously, turning scientific synthesis into a strategic high-precision workflow.
Its value proposition is built on three pillars: speed, connection of complex ideas, and documentary accuracy ensured by multi-level verification and verified citations.
Agentic Research Pipeline
The workflow follows a transparent sequence: iterative planning, user approval, and autonomous drafting. This keeps the researcher in strategic control while allowing the system to execute broad and structured deep research tasks autonomously.
Technical Specs
Reasoning Architecture
TIRO-Scientific mitigates the context-window and long-form coherence limitations of standard LLMs through a hierarchical Reasoning Engine (RLM). A root system acts as an architectural anchor, preserving global structure while specialized sub-tasks generate individual sections.
Scientific Integrity Stack
- Lie Detector (RAGLens): monitors unsupported inferences and helps prevent hallucinations
- Five-level critic: reviews each section for quality and coherence before finalization
- Targeted retrieval: automatically searches for missing evidence and rewrites weak sections
Knowledge Intelligence
- Citation graphs: map relationships across publications and identify key papers
- Semantic clustering: groups large corpora into meaningful thematic domains
- Deduplication: metadata, structural and semantic checks reduce redundancy in research libraries
- Citation export: support for APA, IEEE and BibTeX
Privacy and Control
Through the Discover Dashboard, researchers can query sources such as arXiv, Semantic Scholar and OpenAlex, choose the most suitable model for each task, and monitor token consumption. Local Docker deployment ensures that proprietary research data remains inside the organization’s security perimeter.
Project Partners
No partners listed.
Timeline
Iterative planning and user-governed research framing
Autonomous drafting through the Agentic Research Pipeline
Verification, targeted retrieval and citation-grounded refinement
Discover Dashboard, graph intelligence and local enterprise deployment
Gallery
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