Documentation Index
Fetch the complete documentation index at: https://docs.gildea.ai/llms.txt
Use this file to discover all available pages before exploring further.
Gildea classifies every signal into a two-axis theme system. Signals can have multiple themes per axis.
Value chain (value_chain)
Where in the AI value chain does this signal sit?
| Theme | Description |
|---|
| Infrastructure | The compute layer — GPUs, TPUs, custom chips, and cloud infrastructure that make large-scale model training and inference possible. |
| Foundation Models | The core models that define capability — trained systems like GPT, Claude, Gemini, and Llama that serve as the base for downstream products. |
| Orchestration | The software layer that makes models usable — frameworks, agents, vector stores, and developer tools for building and managing model-powered systems. |
| Data & Labeling | The data layer — how models are trained, evaluated, and refined, from raw datasets to synthetic data and human feedback loops. |
| Applications | The product layer — AI features and experiences built for real users, from enterprise copilots to consumer agents. |
| Distribution | The delivery layer — how AI capabilities reach users, through APIs, platforms, partnerships, and ecosystems. |
Market force (market_force)
What external pressure or catalyst is shaping the value chain?
| Theme | Description |
|---|
| Capital & Investment | Tracks the allocation of financial resources — who’s funding whom, how capital moves across stages, and how valuations shape incentives. |
| Regulatory & Legal | Tracks how rules, policies, and court decisions set the boundaries for innovation and competition. |
| Competitive Dynamics | Examines how firms position themselves — via product strategy, data moats, or market timing — to gain or defend share. |
| Talent & Labor | Follows the human factor — hiring trends, executive shifts, skill shortages, and how automation reshapes work. |
| Geopolitical Strategy | Analyzes the interplay between national interests, technology policy, and access to compute and talent. |
| Trust & Societal Impact | Captures public sentiment, safety debates, and the ethics shaping adoption and regulation. |
Interpretation fields
Each theme includes interpretation fields derived from the raw trend stats. These are discrete labels that agents can act on without statistical expertise.
| Field | Values | What it tells you |
|---|
direction | Rising, Stable, Declining | Which way is this theme trending |
confidence | Significant, Insignificant | Is the trend statistically reliable |
stability | Volatile, Steady | How consistent is coverage week to week |
notability | High, Medium, Low, Negligible | How much this theme warrants attention right now — foreground vs. background |
notability_reasoning | Free text | Human-readable explanation of the notability assignment |
scale field — all 12 themes are always active.
All trend statistics (theil_sen_slope, chi2_p_value, coefficient_of_variation) are computed over a rolling 12-week window — the current week plus 11 prior weeks.
{
"axis": "value_chain",
"label": "Foundation Models",
"direction": "Rising",
"confidence": "Significant",
"stability": "Steady",
"notability": "High",
"notability_reasoning": "Theme with confirmed upward trend and consistent coverage; notable upward shift reliably gaining traction.",
"trend": { "..." : "..." }
}
Theme intelligence
Each theme has a rich profile including:
- Signal count — total signals tagged with this theme
- Trend stats — analytics across four dimensions (see below)
- Related themes — themes that frequently co-occur across both axes
Trend analytics
Every theme includes a trend object with analytics across four dimensions:
| Field | Dimension | Description |
|---|
share_of_voice | Scale | Theme’s share of total corpus over the trailing 4 weeks |
theil_sen_slope | Directionality | Robust trend slope (resistant to outliers) |
streak | Directionality | Consecutive weeks of growth |
chi2_trend | Significance | Chi-square test statistic for linear trend |
chi2_p_value | Significance | Statistical significance of trend (< 0.1 = significant) |
effect_size | Significance | Cramer’s V — practical significance (0 = no effect, 1 = maximal) |
coefficient_of_variation | Volatility | How variable is coverage (stddev / mean) |
Using themes as filters
Themes work as filters across the API. Use the exact theme labels above:
# Signals about Infrastructure
curl -H "X-API-Key: gld_your_key" \
"https://api.gildea.ai/v1/signals?theme=Infrastructure"
# Entities in the Foundation Models space
curl -H "X-API-Key: gld_your_key" \
"https://api.gildea.ai/v1/entities?theme=Foundation%20Models"
# Search within a theme
curl -H "X-API-Key: gld_your_key" \
"https://api.gildea.ai/v1/search?q=chip+shortage&theme=Competitive%20Dynamics"
Theme detail
The theme detail endpoint returns co-occurring themes from both axes:
{
"axis": "value_chain",
"label": "Foundation Models",
"signal_count": 45,
"trend": { "share_of_voice": 0.22, "theil_sen_slope": 0.12, "chi2_trend": 15.3, "..." : "..." },
"related_themes": [
{
"axis": "market_force",
"label": "Capital & Investment",
"co_occurrence_count": 32
}
]
}
