Introduction

Documentation Index

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Traditional market intelligence was built for human reasoning. Gildea is built for agentic reasoning. Legacy reports are static, narrative-heavy, and cost thousands of dollars to access. Gildea provides a programmatic context layer purpose-built for tracking the AI frontier for a fraction of the price. Instead of serving raw text for your models to parse, Gildea provides inference-ready intelligence. We do this by handling the unglamorous work of sourcing, verifying, and structuring the insights so your agents don’t have to. By skipping the ‘filtering’ phase, your agents can spend their entire attention budget on synthesis and analysis. The result is a more reliable agent, higher-quality reasoning, and a 60% reduction in token overhead. Here’s how Gildea does it:

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Why Gildea

Frontier models can now “see” millions of tokens, but they still struggle to think across them. The “million-token window” is a marketing stat; the effective context window is the reality. Beyond a few thousand tokens, models succumb to context rot. When you fill a context window with raw web-search data, you aren’t empowering your agent. You’re drowning it in noise. This leads to:

• Positional neglect: Critical evidence gets ignored simply because of where it sits in the prompt.
• Synthesis collapse: Models fail to connect scattered data points into a coherent answer, even when the data is “present.”

The fix isn’t a bigger context window. It’s higher density, structured input. Gildea closes the gap between retrieval and reasoning by delivering dense, structured input instead of raw text, ensuring your agent’s finite attention budget is spent on reasoning, not the pre-work of filtering noise.

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Inside the intelligence layer

Six capabilities your agents can plug into, all built on the same verified corpus:

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How analyst-grade work changes

These capabilities target a specific kind of work: strategic research that today requires professional analyst time (evaluating a thesis against evidence, mapping expert positions on a contested topic, verifying claims against multiple sources, or monitoring an entity’s trajectory across the news cycle). This work has a market because it’s hard to produce well. Five things shift when it’s produced on Gildea:

• Compression. Producing an evidence-backed evaluation of a thesis takes a consultant or analyst 8–40 hours; a sector brief spans days; a competitive landscape analysis takes weeks to assemble. With Gildea, retrieval returns in seconds and full analyst-grade outputs become producible in minutes.
• Personalization. A market primer covers a fixed set of dimensions for a publishing audience; an investment memo follows a standard template. Outputs built on Gildea adapt to the specific question the user asks.
• Transparency. An investment memo or strategy doc reflects judgment calls: which sources to weight, which contrary evidence to bury. Every Gildea-served unit is verified and cited; outputs can surface evidence weighting and contrary positions explicitly.
• Compounding. Investment memos and strategy docs are fixed once they’re written. A context layer built on Gildea compounds: the corpus updates daily as new verified claims land; outputs auto-refresh with new evidence. Your intelligence asset gets richer over time without re-querying.
• Coverage breadth. A human reading-time bottleneck caps practical source depth at 5–20 per output. Gildea pre-verifies atomic claims across the full 500+ expert source corpus, so retrieval pool size stops being the binding constraint.

This is what differentiates Gildea from both LLM + web search (which lacks rigor) and analyst work (which lacks the other four shifts).

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How does this compare?

Teams currently source market intelligence on AI from two extremes. Neither was built for agents.

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Common use cases

Deep research and web+LLM tools handle one-off questions fine. Gildea is built to unlock the longitudinal use cases AI-forward teams need to track competitive trajectories, validate strategic bets against new evidence, and build intelligence assets that compound over time. The substrate stays stable as the corpus grows: a fixed taxonomy of 12 themes, thousands of disambiguated entities across 8 entity types, and a consistent extraction schema. We enable agents to keep building on a foundation that doesn’t drift.

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