Spatial Limen: Structural Inference Engine
An evidence-grounded structural inference system designed to surface hidden overlaps across papers and patents while preserving provenance, contradiction, and reviewer control.
The Problem with Standard Search
Standard search and basic RAG systems are good at retrieval, but weaker at discovery. They work best when you already know what to ask, or when the relevant documents already live near each other through language or citation patterns. In deep research, the more valuable signal often sits somewhere else: when different fields are solving similar structural problems without using the same terminology, without citing each other, or while actively contradicting each other. That is where standard search and summary-first AI products tend to flatten nuance instead of surfacing it.
What I Built
Spatial Limen was designed to help users explore those hidden overlaps in a more structured way. Core capabilities include:
- Evidence-grounded discovery: Surface cross-domain overlaps without reducing everything to a generic summary.
- Provenance-preserving structure: Keep source, extracted evidence, and claims traceable rather than collapsing them into one layer.
- Contradiction-aware reasoning: Preserve support, challenge, and disagreement as first-class parts of the system.
- Explorable visual interface: Use a 3D graph interface for navigation and pattern recognition when chat is not the right tool.
Architecture and Trust Model
The strongest part of Spatial Limen is not just the interface. It is the trust model behind it. The system is built around a structured evidence layer that separates:
- Source from extracted evidence.
- Derivation from independence.
- Contradiction from deletion.
That matters because research systems become much less trustworthy when repeated claims look like independent support, or when conflicting evidence gets flattened away. Spatial Limen is designed to preserve lineage and disagreement instead of hiding them behind a smooth answer. On the product side, that leads to a very different design: ingestion and collision pipelines instead of chat-only retrieval; trust labels and reviewer logic instead of silent confidence assumptions; visual exploration plus source traceability instead of black-box summarization.
Why This Matters
Spatial Limen proves a different product muscle than Reztrix or BurgReport. It shows:
- Ability to design systems for ambiguity, not just clean workflows.
- Strong judgment around provenance, evidence integrity, and trust.
- Willingness to choose a different interface when chat is not the right UX.
- Capacity to structure messy, cross-domain information into something explorable and reviewable.