Technical Principles
Generic AI fails in markets.
Generic LLMs
To oversimplify, generic LLMs are like a cook who doesn't understand flavor and simply throws ingredients together, while many of them are rotten.
Large models can generate text, but without financial structure they produce wrong outputs, hallucinated numbers, and shallow reasoning.
LLM Wrappers
On the other hand, LLM wrappers are like chefs who know a few fixed recipes. They can prepare a dish that looks correct on paper, but it lacks balance and taste.
Wrappers built on static prompts or narrow APIs can handle a few use cases, but they cannot scale across thousands of scenarios. Because markets are dynamic, cross-asset, and narrative-driven.
Edgen is built on a different foundation.
It fuses state-of-the-art compute with proven investment frameworks, structured datasets, and reinforcement learning. The result is not another prediction engine, but a decision layer built specifically for markets.
At the center is EDGM (Efficient Decision Guidance Model), a commander that directs specialist agents. Each agent is designed for a precise task, such as parsing filings, scanning derivatives, tracing on-chain activity, or linking news to assets. Together they produce coherent, auditable outputs.
To oversimplify, EDGM is essentially a chef who understands ingredients, proportions, and technique: able to follow thousands of recipes, invent new ones, and consistently serve meals that are actually good and harmonious.
This architecture is built to be:
Compounding: sharper over time as feedback and reinforcement learning accumulate.
Sustainable: accessible to all investors at the base layer, with advanced modules and research available through subscription and contribution.
Composable: open to new agents, tools, and domains as markets evolve.
These principles ensure Edgen is an intelligence system that grows stronger, broader, and more aligned with its users the more it is used.
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