Fodda

Brand, market & earnings intel

Category: Data & Analytics
Primary Subcategory: Enterprise Knowledge Search & AI Context Layer

Integration details

Description

Fodda connects ChatGPT to expert-curated knowledge graphs across retail, consumer goods, technology, sustainability, culture, and finance. Use it to research emerging market and consumer trends, track a brand's footprint and competitive landscape, and read what executives are actually saying in quarterly earnings calls. You can also consult named industry experts and their AI counterparts for a domain perspective on a specific question. Every answer is grounded in graphs maintained by real analysts, and each finding comes with its source evidence and attribution, so you can see where an insight came from and follow it back to the original quote or report.

Integration type: Plugin
Verification status: Not applicable
Platform: ChatGPT
Category: Data & Analytics
Primary Subcategory: Enterprise Knowledge Search & AI Context Layer
Secondary Subcategories: None listed
Brand: Fodda
Access: Account required
First tracked: 2026-09-26
Tool count: 24
Geography: US

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Primary Subcategory

Competing in ChatGPT Enterprise Knowledge Search & AI Context Layer

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Tools

24 tools agents can invoke

Build a complete Brand Intelligence Profile by searching ALL knowledge graphs for a specific brand. Returns trend footprint (which trends the brand appears in), competitive landscape (co-occurring brands ranked by overlap), cross-graph presence, evidence timeline, lifecycle distribution, and bundled supplemental signals (Google Trends, Wikipedia, Amazon, earnings). Use when the query is about a specific company or brand — "What is Nike doing?", "Patagonia's innovation strategy", "How is Apple positioned?". This aggregates cited, cross-graph intelligence into a single brand profile.
brand_tracker
Check the current user's account status: API call balance, plan, enabled/disabled graphs, and profile info. Use when the user asks "how many API calls do I have?", "what plan am I on?", "what graphs can I access?", or similar account questions. Returns live data — not cached from session start. (User account status & credit balance lookup.)
get_my_account
Check if deep research is complete and retrieve the final report. Call this after deep_research_topic — poll every 10 seconds until status is COMPLETE or FAILED. (Async research job status poll.)
check_research_status
Check if market data gathering is complete and retrieve the results. Call this after get_supplemental_context — poll every 5-10 seconds until status is COMPLETE or FAILED. (Async supplemental query status poll.)
check_supplemental_status
Consult an authorized Human Agent (Digital Twin) expert created directly with the named expert's consent, participation, and curated knowledge graph. The expert answers in their voice — one-off questions or multi-turn engagements (pass session_id back to continue). Each human agent has a unique methodology, domain expertise, and analytical lens with a curated evidence base. Call list_analysts first to find the right expert ID. Responses may include a coverage status (in/adjacent/out), source attribution, and referrals to other expert graphs. Any referrals to other expert graphs are returned in third-person platform voice with an offer to query the referred graph. Response may include `book_a_call` (URL + a pre-written booking sentence shown verbatim) for booking time with the real person — surface it when the user wants to hire or speak to the expert.
consult_human_agent
Consult a named Synthetic Analyst expert who answers in their expert voice using their curated knowledge graph — one-off questions or multi-turn engagements (pass session_id back to continue). Synthetic analyst experts have a unique methodology, domain expertise, and analytical lens with a curated evidence base. For company-specific executives (e.g. "Nike CMO", "Apple CEO", "Target CFO"), you can pass analyst_id: "brand-cmo" with company: "Nike", or pass analyst_id: "Nike CMO" directly (auto-resolves to analyst_id: "brand-cmo" and company: "Nike"). Call list_analysts first to find the right expert ID. Responses may include a coverage status (in/adjacent/out), source attribution, and referrals to other expert graphs. Any referrals to other expert graphs are returned in third-person platform voice with an offer to query the referred graph.
consult_analyst
Launch an autonomous Deep Research session that combines Fodda knowledge graph intelligence with live web research to produce a comprehensive editorial-quality report. The Research Agent plans its own strategy, searches multiple graphs, validates with institutional data, and synthesizes into a narrative brief with inline source citations. Use for complex, multi-faceted questions that need both curated expert intelligence AND current web context — e.g., strategic briefings, market landscape reports, competitive deep dives. Automatically includes earnings-call intelligence and macro/supplemental data when the topic warrants it (public companies, sectors, economic conditions). You do not need to call the earnings or supplemental tools separately before or after.
deep_research_topic
Cross-company analyst-management divergence detection from the knowledge graph (legacy-thematic). Surfaces where executives are deflecting, reframing, or avoiding specific topics — the gap between what analysts press on and how management responds. Use for "where are executives deflecting?" or "divergence in [sector] earnings." For per-ticker deflection signals, use get_company_earnings with view=qa and filter by response_directness.
get_earnings_divergence
Discover what's connected to a specific trend — related brands, technologies, locations, and cross-domain links. Returns curated editorial connections between trends. Use after search_graph to map the territory around a trend, find which brands are connected, or understand cross-domain relationships. Requires node_id from a prior search_graph result.
get_neighbors
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 all brands, locations, technologies, audiences, or trends within a specific knowledge graph. Use to explore what a graph contains — e.g., "what brands are in the retail graph?" or "what locations does the fashion graph cover?". To get a complete list of every trend in a graph, call with label="Trend" — this returns the full deterministic list, useful for industry-report graphs where search may return partial results.
get_label_values
The canonical per-ticker earnings source. Returns the full truth-layer record for covered tickers (517 consumer-sector companies) — analyst concerns, sentiment labels, strategic activity (marketing/retail/technology/sustainability), CEO intelligence, and validated consumer trends from Fodda's quarterly analysis pipeline. Falls back to web-backfill for uncovered tickers. Use this for company-specific data. Use get_earnings_intelligence for cross-company thematic comparisons.
get_company_earnings
Returns Fodda's main capabilities / features / offerings / products / services / tools and how to use them. Call this for any question about what Fodda can do or what's available.
get_capabilities
A standard layer for macro, institutional, and real-time market data. Call this tool when curated coverage is thin, empty, or when the query is explicitly demand/attention-shaped (e.g. to get search volume, economic series, or census data). It retrieves data from 80+ authoritative sources (Google Trends, FRED, BLS, Census, etc.) fanned out in parallel. Returns categorized data blocks with source attribution and metadata. Note: call after search_graph indicates thin/empty coverage via its coverage annotation.
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.
get_node
Get the source articles, case studies, and statistics behind a specific trend — with full citations and publisher attribution. Each item includes source URL, location, brand names, publication date, category, and a formatted citation. Use after search_graph when you need the supporting proof behind a trend. This is a direct lookup by trend ID — not a text search tool.
get_evidence
Returns market-validated consumer trends from corporate earnings reports cross-validated by Fodda's analysis pipeline. Connects earnings commentary (analyst concerns, CEO statements) with consumer trend signals.
get_validated_trends
Lists available human agents and synthetic analysts (e.g. brand-cmo, brand-ceo, brand-cfo, human experts like Anu Lingala). To query a company-specific synthetic expert (e.g., "Nike CMO", "Apple CMO", "Adidas CEO"), consult brand-cmo (or relevant role ID) and supply the target company name in the company parameter (e.g. company: "Nike").
list_analysts
List all knowledge graphs the user can access — IDs, descriptions, authors, sectors, signal counts. Use FIRST in any session to discover available sources before searching. Returns graph metadata needed for graphId parameters in other tools. Deprecated: waldo, psfk (use retail/tech/food/travel/fashion/beauty/sports instead). (Fast graph registry metadata lookup.)
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, or sector filters. 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.
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 only: expert quotes, editorial analysis, and strategic perspectives on a topic — sourced from named strategists and industry leaders. Returns qualitative evidence (quotes, interpretations) with source attribution and parent trend context, NOT raw numbers. For hard data points, market sizes, and growth rates, use search_statistics instead. Works on ALL graphs. Use when you need authoritative voices, strategic framing, or analytical depth with source attribution.
search_insights
Find trends, signals, and expert insights across 100+ curated knowledge graphs covering retail, beauty, tech, food, travel, sports, and 30+ specialist domains. Returns trend data with cited evidence, source attribution, and lifecycle stage (emerging/building/mature/fading). If graphId is omitted, searches ALL accessible graphs in parallel (recommended default). Use for market trends, competitor analysis, innovation signals, consumer behavior, cultural shifts, or any topic where you want curated, cited expert intelligence.
search_graph
HARD NUMBERS only: specific figures, market sizes, growth rates, and quantitative data points across Fodda's knowledge graphs. Each result links back to the expert trend it supports. Use when a question asks for a number or statistic — try this BEFORE supplemental data tools, as Fodda's experts may have already curated the answer. For expert quotes, editorial analysis, and narrative interpretation, use search_insights instead. Works on ALL graphs — domain, expert, and report. Search multiple graphs for best coverage.
search_statistics

Fodda FAQ

How do I improve a ChatGPT Plugin's discoverability?

The levers are the listing surface agents actually read: names, descriptions, keywords, tool metadata, and registry health. Which lever matters depends on where discovery breaks, which is what continuous measurement shows.

What are Fodda alternatives on ChatGPT?

As of 2026-09-26, Fodda competes with Algolia Productivity, Analytics Brain, Atlan, BitsWeave, BuildBetter.ai, Circuitry, Context Link, Coveo, Data Dolphin, Deep Document Search, DNAObs, Dropbox Dash, Egnyte, eIgreja, Endgame, GoLinks, GoSearch, Hypha, iManage Work, Kumbukum, Lito, Lloyd by The L Suite, My AskAI, Native Soil, NeuronSearchLab, Olimpo, Planning Center, Qontext, Realpage Lumina, Sensei Knowledge Base, Sleuth Skills, Socra Cortex, Stack Overflow For Agents, Stele, StorageChain, Stratta, Synaply, TALON Knowledge, Tela, Unabyss, Unblocked, V7 Go (US), VATES, WarmHub, Within in ChatGPT Enterprise Knowledge Search & AI Context Layer, ranked by public Discoverability Score.

Where is this profile measured?

This profile uses the geography attached to the latest public registry snapshot: US. Locale tags are intentionally omitted.

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24 tools agents can invoke