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Technical Documentation

The Cognir Ontology™

A dual-engine, multi-stage research intelligence system that transforms unstructured human cognition into ranked, evidence-grounded research directions and curated literature pathways. Built for precision. Designed for the serious researcher.

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Limited to 200 researchers

Overview

Phase 1

Phase 2

Reference

01 / Philosophy

The Problem We Solve

Research begins in chaos. A researcher does not wake up with a perfectly formed hypothesis — they wake up with a hunch, a contradiction, a pattern noticed in passing, or a frustration with existing literature. The gap between that raw cognitive state and a rigorous, defensible research question is where most projects die.

Traditional tools treat research as a search problem. You type a query, you get papers. But the researcher does not yet know what to query. They do not yet have the vocabulary. They have not yet articulated the boundary between what they know and what they need to know.

The COGNIR ONTOLOGY™ treats research as a transformation problem . It accepts unstructured, half-formed, emotionally charged human thought as input. It outputs ranked, evidence-grounded research questions and a curated literature pathway. The messy stage of research — the stage where most people quit — is compressed from weeks to hours.

Pipeline Stages

Per phase, fully autonomous

Enrichment APIs

Semantic Scholar, CrossRef, arXiv

Query Variations

Synonym expansion + snowballing

02 / Capabilities

What the System Does

Intent Extraction

Parses unstructured, stream-of-consciousness researcher notes into structured semantic components: core problem, knowledge gap, key concepts, research domains, and notable themes.

Question Generation

Generates 10+ candidate research questions derived solely from extracted intent. No hallucination. No external injection. Every question is traceable to the user's original input.

Evidence Collection

Executes multi-query Serper searches, crawls priority academic domains (arXiv, Nature, PubMed, IEEE), and extracts structured metadata including abstracts, publication dates, and citation counts.

Viability Scoring

Multi-dimensional scoring across five axes: Research Activity, Academic Coverage, Specificity, Novelty, and Practicality. Weighted composite produces a final 0-100 viability score.

Literature Curation

Organizes discovered papers into six taxonomic categories: Foundations, Core Evidence, Frontiers, Methodology, Reviews & Meta-Analyses, and Controversies. Each paper is tagged with relevance score and reading priority.

Citation Snowballing

Recursively searches citations and references of top-scored papers to discover seminal works and recent developments that initial queries may have missed.

Phase 1

From Unstructured Ideas to Researchable Questions

The first engine accepts raw researcher cognition — notes, ramblings, half-formed hypotheses — and transforms it into a ranked set of 3 validated research questions. This is not keyword extraction. It is semantic archaeology: digging beneath the surface text to find what the researcher actually means.

01

Raw Text Understanding

The system performs foundational semantic extraction on the user's raw input. It does not generate questions yet. It does not infer beyond the text. It extracts exactly what is present: the core problem, the apparent goal, key concepts, research domains, and notable themes.

"main_problem": "string describing the core problem",<br>"main_goal": "string describing what the user wants to understand",<br>"key_concepts": ["concept1", "concept2", "concept3"],<br>"research_domains": ["domain1", "domain2"],<br>"notable_themes": ["theme1", "theme2"]

Temperature: 0.2<br>Max Tokens: 2048<br>Model: Laguna M.1

02

Intent Compression

This is the critical interpretive layer. The system identifies what the user is actually trying to understand — not what they said, but what they meant. It detects the knowledge gap they are circling around and articulates the high-level research objective that would genuinely serve them.

Input

Extraction from Stage 1 (concepts, domains, themes)

Output

Research intent, knowledge gap, objective, primary domain

Constraint: All inference is grounded in the provided extraction. No hallucinated domains. No invented concepts. The system is explicitly prohibited from adding external knowledge.

03

Candidate Question Generation

Generates exactly 10 candidate research questions derived solely from the compressed intent and extracted concepts. Questions are varied in approach — some empirical, some theoretical, some applied — but all are researchable, specific, and traceable to the user's original...