Fodda

Translate cultural trends, brand challenges, and consumer insights into a 2026 Strategy-to-Activation Comms & Retail Media Plan. Generates an agency-grade media deliverable with T1-T5 tasks, 3-layer shopper audience cards (with mandatory exclusions), Amazon/Walmart digital shelf intelligence, tactical placement tables with [LIVE] provenance tags, and a machine-readable activation manifest.

generate_comms_plan

Create a presentation-ready data visualization from research findings. Available chart types: "cultural_shifts" (From→To transitions), "competitive_compass" (brands on 2 axes), "trend_constellation" (network of related trends), "implication_ladder" (Signal→Trend→So What→Do What), "innovation_pathway" (Now→Near-Term→Future), "opportunity_map" (2×2 white space analysis). Returns a branded SVG that renders directly in the chat. (Data payload transformation + SVG chart rendering.)

generate_visual

List unique taxonomy entity values (brands, locations, technologies, audiences) or retrieve the complete table of contents for small, discrete report graphs (<40 trends, e.g. DHL, PwC, or Mintel radars) where search may return partial results. Returns up to 100 unique values ordered alphabetically. Sibling routing: DO NOT use this tool for thematic or topic research (e.g. 'find AI trends' or 'what trends are in tech?') on large domain graphs (tech, retail, cpg), because it caps at 100 items alphabetically without evidence or signals; use search_graph instead for semantic trend retrieval. (Graph taxonomy label value lookup.)

get_label_values

The canonical per-ticker earnings source for 517 covered companies. - view='snapshot' (default): Full quarterly record including headline, financial metrics, analyst sentiment, strategic activity, CEO quotes, and top Q&A entries. - view='qa': Itemized analyst Q&A session with verbatim analyst questions, analyst names, analyst firms, executive responses, and response directness. USE THIS VIEW whenever the user asks who asked a question, for analyst names/firms, or for verbatim call quotes. - view='history': Narrative quarterly timeline. - view='compare': Side-by-side comparison of 2-5 tickers.

get_company_earnings

Returns Fodda's capabilities, semantic research workflows, Human Agents (Human Agents). Call this for any question about what Fodda can do, how experts work, available research recipes,. Sibling routing: Use get_capabilities to discover platform features and workflow recipes; use list_graphs to list specific data graphs; use list_analysts to discover expert personas. Returns platform overview, active specialist counts, workflow descriptions.

get_capabilities

Federated empirical data layer: queries 110+ live institutional and government feeds (FRED, BLS, US Census, World Bank, OECD, WTO, IMF PortWatch, etc.) for real-time macroeconomic indicators, demographic shifts, global trade flows, container shipping & port throughput, maritime chokepoints (Suez, Panama), and retail spending. When citing US retail sales, cite official Headline Retail & Food Services (NAICS 44X72 / FRED RSAFS, ~\$738B) as the primary retail figure; clearly distinguish from Retail Trade Excl. Food Services (NAICS 44000 / FRED RSXFS, ~\$646B). When citing consumer spending, report live Monthly Personal Consumption Expenditures (BEA Table T20805 / FRED PCE, ~\$22.3T through August) with MoM and YoY growth rates rather than quarterly aggregates. Use when verifying macroeconomic benchmarks, checking consumer spending metrics, grounding supply chain or trend hypotheses with empirical statistics, or when knowledge graph coverage is thin. Sibling routing: Use search_graph for curated consumer trends and brand case studies; use get_supplemental_context when you need authoritative government statistics, port/logistics telemetry, or regional/country economic context. Returns a structured 5-bucket evidence bundle (demand_signals, economic_context, market_data, financial_reporting, demographic_context) with full source provenance.

get_supplemental_context

Get the full profile of a specific trend — detailed description, lifecycle stage (emerging/building/mature), signal strength, geographic scope, and all properties. Use when you need deeper detail on a single trend after search_graph returned a summary. Requires node_id from a prior search_graph result. (Direct Neo4j node metadata lookup by ID.)

get_node

Evidence citation lookup: retrieves primary evidence articles, publisher case studies, data points, and source URLs that validate a specific trend node. Prerequisite: Requires a valid for_node_id and graphId obtained from a prior search_graph or get_neighbors call. Use when backing a specific trend with original reporting, verifying publisher citations, or extracting concrete brand examples. Sibling routing: Use search_graph first to discover trends and retrieve their node_id; use get_evidence to inspect the underlying source citations for that node; use get_node for high-level metadata; use search_statistics for standalone numbers. Returns an array of evidence items with title, publication, publication_date, snippet, and source_url.

get_evidence

Discovers consumer trends that have been empirically corroborated by corporate earnings reports in Fodda's cross-validation pipeline. Connects high-level cultural trend signals with corroborating executive statements and analyst Q&A from quarterly 10-Q/10-K earnings calls. Use when you need proof that an emerging consumer trend is impacting corporate P&L and executive strategy (e.g., 'Show consumer trends validated in recent beauty earnings calls'). Sibling routing: Use search_graph for pure cultural/consumer trend discovery; use get_company_earnings for full single-ticker filings; use get_validated_trends when you specifically need the bridge between consumer trends and corporate earnings validation. Returns validated trend records with corroborating company tickers, validation types, analyst themes, and executive quote excerpts.

get_validated_trends

Catalog and discover all expert personas available on Fodda across 4 categories: living Human Agents (practitioners with Human Agent models), C-Suite Agents (corporate executive lenses grounded in earnings), Classic Agents (historical thinkers), and Synthetic Analysts (domain specialists). Use when exploring available experts, finding who to consult for a specific topic. Sibling routing: This tool lists and filters agents; to initiate an interactive conversation with a chosen specialist, call consult_human_agent (for living experts) or consult_analyst (for synthetic/classic/C-suite personas); to search experts by query match, use find_expert. Returns an array of agent profiles with analyst_id, canonical_slug, name, role, agent_class, topics, consult_tool.

list_analysts

Catalog and discovery layer for Fodda knowledge graphs: lists all accessible knowledge graph IDs, topic vertical tags, author credentials, node counts, and signal metrics. Use first when discovering available domain libraries or when you need a valid graphId parameter for graph-scoped queries (e.g. "What knowledge graphs do you have on retail or sustainability?"). Sibling routing: Use list_graphs to inspect graph metadata and available sources; use search_graph to query trend content across graphs; use list_analysts to discover specialist Human Agents. Returns an array of graph profiles with graphId, name, topics, signal_count, and author.

list_graphs

Cross-company thematic earnings intelligence from the knowledge graph and web sources. Use for multi-company comparisons ("what are hotel companies saying about labor costs?"), industry-level queries, sector filters, or strategic corporate activity categories (activity: "sustainability" | "marketing" | "retail" | "technology"). For corporate ESG, circular economy, and climate initiatives (e.g. "What sustainability commitments did apparel companies make this quarter?"), pass activity="sustainability" or rely on server-side intent routing to avoid conflation with analyst margin sustainability questions. Multi-company and sector queries return active initiatives along with confirmed_absent brands (checked tickers with confirmed silence / no initiatives discussed). For single-brand earnings, brand_tracker includes earnings automatically. For per-ticker structured analysis (analyst concerns, activity breakdown, validated consumer trends), use get_company_earnings instead — it reads the canonical truth layer. Results may include "knowledge_graph" or "web_supplemental" provenance. OUTPUT REQUIREMENT: For multi-company thematic findings, present results in a structured evidence table citing: | Company (Ticker) | Reporting Quarter | Speaker (Analyst & Firm / Exec) | Verbatim Quote / Finding |. Clearly distinguish Fodda's synthesized theme labels (e.g. "Pilot Trap") from what analysts or executives actually said on the call.

get_earnings_intelligence

Extract clean text content from any URL. Use this when a user shares a link (competitor site, news article, client brief, trend report) and wants to cross-reference it against Fodda knowledge graphs. Returns structured text ready for analysis.

read_url

Narrative and qualitative evidence layer only: returns expert quotes, editorial analysis, and strategic perspectives from named strategists and industry leaders across all graphs, with source attribution and parent trend context. Sibling routing: Use search_insights when you need authoritative quotes and qualitative commentary; use search_statistics for numerical data points and market sizing; use consult_human_agent to speak interactively with an expert. Does NOT return raw statistics or market sizing.

search_insights

Use when searching for market trends, category dynamics, and consumer shifts across 100+ curated knowledge graphs. Returns trends with cited evidence, lifecycle stage (emerging/building/mature), and structured next_moves. Sibling routing: Use search_graph as the default starting tool for broad trend discovery; use search_statistics if you specifically need numerical data points and growth rates; use search_insights if you only need qualitative quotes and expert commentary; use brand_tracker if querying a specific company or brand name.

search_graph

Quantitative statistics and hard numbers retrieval layer: queries curated knowledge graphs for empirical metrics, survey percentages, market sizes, adoption rates, and compound annual growth rates (CAGR) linked to parent trends. Use when looking specifically for data points, percentages, and numerical proof to substantiate arguments (e.g., 'What percentage of Gen Z consumers prefer clean beauty?', 'Find market size data for sports tourism'). Sibling routing: Use search_statistics for numerical data points; use search_insights for quotes and narrative editorial commentary; use get_supplemental_context for live government/macro statistics. Does NOT return general narrative prose.

search_statistics

Use when verifying a factual claim, hypothesis, or strategic assertion against a named practitioner's curated knowledge graph via their AI-powered Human Agent (Digital Twin) model. Evaluates whether the practitioner's documented research and domain evidence supports or refutes the claim. If analyst_id is omitted, automatically routes to the most relevant active Human Agent covering the domain. Sibling routing: Use verify_claim to evaluate a claim against a specific practitioner's knowledge graph and receive a structured verdict; use verify_market_claim for multi-graph algorithmic 5-gate verification across 100+ graphs and earnings. Returns a structured verification object with verdict ("confirms", "contradicts", "partial", or "no_coverage"), confidence ("full", "partial", or "thin"), one_line summary, rationale, cited sources, and expert attribution.

verify_claim

Use when verifying a factual market claim against 100+ knowledge graphs, primary research citations, and corporate earnings divergence through 5 sequential kill-gates (grounding, contradiction, freshness, corroboration, divergence). Sibling routing: Use verify_market_claim for objective, multi-source empirical data verification and contradiction scoring; use verify_claim to evaluate a claim against a specific practitioner's curated knowledge graph via their AI-powered Human Agent model. Returns structured verdict ([VERIFIED], [REFUTED_BY_EVIDENCE], [INSUFFICIENT_EVIDENCE], [UNGROUNDED_SPECULATION]), supporting evidence citations, and contradiction scores.

verify_market_claim