Building network-driven intelligence systems to support high stakes decision-makers.
Intellidimension builds mesh-driven intelligence systems—where diverse AI agents reason over the same evidence, debate, and produce insight you can test, trust, and audit.
We work with investors, research platforms, and enterprises who need more than summaries. They need defensible analysis, traceable reasoning, and signal they can act on.
The Intellidimension Mesh Platform orchestrates networks of AI agents—each with distinct expertise and perspective—to analyze complex questions. The agents research independently, then debate: challenging each other's reasoning, surfacing blind spots, and pressure-testing conclusions. What emerges isn't consensus for its own sake. It's clarity about what holds up and why.
The platform scores and compares results across catalogs of cases—companies, policies, incidents, targets—so you get structured, comparable analysis, not just a pile of narratives.
Intellidimension's roots are in the early semantic web and graph database era—technology that modeled complex domains as graphs, queried them with standards like RDF and SPARQL, and built applications on linked, contextual data rather than isolated records.
That foundation made the progression to big data, machine learning, and now generative AI a natural one. Structure, context, and relationships still matter as much as raw scale.
Large language models can read almost everything, but most tools still behave like a single, overconfident expert.
We're building something different: networks of agents that argue, reconcile, and preserve disagreement—with a traceable record of how conclusions form. The output isn't an answer to believe. It's a position to evaluate.
Intellidimension holds a family of patents on simulated networks of agents, with priority dating to 2015. The portfolio covers constructing populations of simulated users, connecting them in similarity-based networks, simulating how information propagates through those networks, and using interaction history as signal for downstream outputs.
These patents predate the current wave of LLM-based agent systems by nearly a decade.
For partnership or licensing inquiries, please contact us.
We're working with a select group of design partners to refine the Mesh Platform. If you share our vision for the future of research infrastructure, we'd love to hear from you.
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