> ## 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. # Themes > Two-axis theme system with trend analytics for value chain segments and market forces Gildea classifies every signal into a two-axis theme system. Signals can have multiple themes per axis. ```mermaid theme={null} graph LR S[Every signal] --> VC[Value Chain] S --> MF[Market Force] VC --> I[Infrastructure] VC --> FM[Foundation Models] VC --> O[Orchestration] VC --> DL[Data & Labeling] VC --> A[Applications] VC --> D[Distribution] MF --> CI[Capital & Investment] MF --> RL[Regulatory & Legal] MF --> CD[Competitive Dynamics] MF --> TL[Talent & Labor] MF --> GS[Geopolitical Strategy] MF --> TS[Trust & Societal Impact] style S fill:#000,color:#fff,stroke:#333 style VC fill:#1a1a1a,color:#fff,stroke:#444 style MF fill:#1a1a1a,color:#fff,stroke:#444 style I fill:#2a2a2a,color:#fff,stroke:#555 style FM fill:#2a2a2a,color:#fff,stroke:#555 style O fill:#2a2a2a,color:#fff,stroke:#555 style DL fill:#2a2a2a,color:#fff,stroke:#555 style A fill:#2a2a2a,color:#fff,stroke:#555 style D fill:#2a2a2a,color:#fff,stroke:#555 style CI fill:#2a2a2a,color:#fff,stroke:#555 style RL fill:#2a2a2a,color:#fff,stroke:#555 style CD fill:#2a2a2a,color:#fff,stroke:#555 style TL fill:#2a2a2a,color:#fff,stroke:#555 style GS fill:#2a2a2a,color:#fff,stroke:#555 style TS fill:#2a2a2a,color:#fff,stroke:#555 ``` ## 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 | Themes do not have a `scale` field; all 12 themes are always active. The interpretation labels are computed over a rolling 12-week window: the current week plus 11 prior weeks. ```json theme={null} { "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: | Field | Description | | ---------------- | ------------------------------------------------------- | | `share_of_voice` | Theme's share of total corpus over the trailing 4 weeks | | `streak` | Consecutive weeks of growth | | `current_week` | Signal count this week | | `prior_week` | Signal count last week | The discrete interpretation labels above (`direction`, `confidence`, `stability`, `notability`) are derived from the underlying statistics so you can act on them without doing the statistics yourself. ## Using themes as filters Themes work as filters across the API. Use the exact theme labels above: ```bash theme={null} # 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: ```json theme={null} { "axis": "value_chain", "label": "Foundation Models", "signal_count": 45, "trend": { "share_of_voice": 0.22, "streak": 3, "current_week": 28, "prior_week": 24 }, "related_themes": [ { "axis": "market_force", "label": "Capital & Investment", "co_occurrence_count": 32 } ] } ```