
TechRxiv
A preprint server for health sciences.

A visual AI-powered research studio for deep-dive literature reviews and conceptual mapping.

Heuristi.ca is a sophisticated AI research engine and visual thought-canvas designed to disrupt traditional linear chat interfaces. By leveraging a node-based spatial architecture, it allows researchers and strategists to map out complex information landscapes using a 'visual-first' Retrieval-Augmented Generation (RAG) approach. In the 2026 market landscape, Heuristi.ca distinguishes itself through its multi-model orchestration, allowing users to toggle between leading LLMs like GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro within the same workspace to cross-verify insights. Its core technical innovation lies in its contextual memory management; unlike standard chat threads that lose context over long sessions, Heuristi’s canvas maintains global awareness of all connected nodes. This makes it an essential tool for high-stakes academic research, market analysis, and patent landscaping, where the relationship between concepts is as important as the concepts themselves. The platform's ability to ingest massive PDF datasets and URLs, then recursively branch into sub-topics via semantic discovery, positions it as a premier 'thinking partner' rather than a simple assistant.
Heuristi.
Explore all tools that specialize in conceptual branching. This domain focus ensures Heuristi.ca delivers optimized results for this specific requirement.
Parallel processing of queries across different LLM architectures to ensure accuracy and minimize bias.
Uses vector embeddings to suggest hidden links between disparate research papers or notes.
Automated sub-node generation based on semantic density of the previous output.
Allows users to 'pin' specific source data to nodes to force RAG adherence.
Serializes the visual graph into formats compatible with Obsidian Canvas and Logseq.
Bi-directional sync with Zotero libraries for real-time citation tracking.
Integration with Perplexity-style web search to ground node generation in current events.
Sign up via Google or Email to initialize a secure research workspace.
Create a new 'Research Canvas' for a specific project or domain.
Upload source materials including local PDFs or paste URLs for live web scraping.
Select the preferred AI Engine (e.g., GPT-4o for logic, Claude for synthesis).
Generate a 'Root Node' by asking a primary research question.
Use the 'Expand' feature on specific keywords to generate child nodes.
Reorganize nodes manually to establish semantic relationships and groupings.
Use the 'Synthesize' tool to create a summary node from multiple selected sources.
Review auto-generated citations and link them to the reference manager.
Export the completed knowledge graph to Markdown for integration with PKM tools.
All Set
Ready to go
Verified feedback from other users.
"Users praise the spatial workflow and the ability to see the 'big picture' of their research, though some find the credit system restrictive on complex projects."
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A preprint server for health sciences.

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