A patented multi-agent analysis engine that turns information overload into structured conviction.
The Intellidimension Mesh Platform is a multi-agent analysis engine that takes messy, long-form inputs — transcripts, briefs, filings, internal documents — and produces debated, scored, and simulatable views of a case.
Each run spins up a network of specialized AI agents, lets them argue over the same evidence through a structured social network, and produces two kinds of output: artifacts you can read (reports, FAQs, transcripts, scenario maps) and quantitative objects you can model against (probability distributions, sensitivity rankings, dimension scores, and calibrated signals).
The platform supports any domain where structured disagreement produces better analysis than consensus: earnings forecasts, investment diligence, competitive intelligence, risk assessment, policy analysis, sports prediction.
Each mesh run assembles a panel of synthetic experts with distinct roles — sector analyst, macro strategist, compliance counsel, consumer researcher, risk specialist — plus a Chair who synthesizes, an Oracle who fact-checks, and a Judge who scores.
These agents aren't arranged as a flat committee. They operate within a small-world network:
The result is a deliberation transcript where you can trace exactly how a conclusion was built — which agent introduced it, who challenged it, who extended it, and whether it survived contact with adjacent expertise — and a synthesis analysis document that distills the deliberation into a structured analytical output: the conclusions, key tensions, risk factors, and judgments that emerged from the network, organized for decision-making rather than replay.
The mesh runtime is domain-agnostic. What makes a run specific to earnings analysis, PE diligence, or sports prediction is a plugin — a configuration layer that focuses the engine on a particular analytical domain.
Each plugin defines:
The plugin model means new domains don't require new infrastructure. Partners can build and maintain their own plugins against the runtime, or work with Intellidimension to develop domain-specific configurations for their use case.
The mesh doesn't stop at a single forecast. When a plugin includes Many-Worlds output, the engine produces a scenario decomposition: a set of named worlds, each defined by specific conditions across the dimensions the mesh identified as decision-relevant.
Each world carries a calibrated tilt — an expected outcome magnitude grounded in a continuous margin scale, not a binary call. The simulation engine runs Monte Carlo draws across all dimensions, maps each draw to matching worlds, and adjusts the outcome based on how the specific dimension states within that world resolve. The result is a full probability distribution over outcomes, decomposed by the mechanism that produces each region of the distribution.
This means you don't just get "we think X wins" or "we're 60% confident." You get a histogram showing the probability of every outcome band, colored by which world drives it, with the market's expectation marked for comparison — so you can see exactly where the mesh agrees with the market, where it disagrees, and which analytical mechanism creates the disagreement.
A perturbation layer tests how fragile the answer is. The engine re-runs the simulation across a thousand Dirichlet-sampled prior sets, and the sensitivity analysis tells you which dimensions move the outcome most — so you know what to watch, not just what to expect.
Mesh outputs are calibrated against external anchors — market prices, model estimates, historical base rates — so that the analytical content maps to realistic magnitudes rather than generic language.
After outcomes resolve, the platform runs structured post-mortems: which world materialized, which dimensions the mesh got right, where the correlation structure broke down, and what the scoring framework rewards. These post-mortems feed back into domain manuals — living reference documents that encode the traps, distinctions, heuristics, and calibration lessons specific to each analytical domain. The mesh learns from experience through documentation, not retraining.
The Intellidimension platform is built on a patent family dating to 2015 covering multi-agent orchestration, simulated network methods, and structured deliberation architectures. The core IP covers the network-based agent interaction model — agents with distinct personas communicating through a defined topology to produce emergent analytical outputs — which is foundational to modern agentic AI systems.
We're happy to discuss architecture, implementation, and IP with qualified partners.
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