{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_slated","slug":"slated","name":"Slated","type":"product","url":"https://www.slated.ai","page_url":"https://unfragile.ai/slated","categories":["data-analysis"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_slated__cap_0","uri":"capability://text.generation.language.natural.language.financial.modeling.query.interface","name":"natural language financial modeling query interface","description":"Accepts free-form natural language questions about financial scenarios and translates them into executable financial models without requiring users to write formulas or code. The system likely uses an LLM-based query parser that maps user intent to underlying financial calculation engines, enabling non-technical users to ask questions like 'What if revenue grows 20% annually?' and receive modeled outputs. This abstraction layer removes the barrier of Excel/Python expertise while maintaining access to institutional-grade modeling logic.","intents":["Ask financial 'what-if' questions without writing Excel formulas or Python code","Quickly model revenue projections, expense scenarios, and cash flow impacts using conversational language","Explore multiple financial scenarios interactively without technical setup overhead"],"best_for":["Non-technical founders and individual investors","Finance teams without dedicated modeling engineers","Users migrating from manual spreadsheet modeling"],"limitations":["Accuracy depends on LLM's financial domain understanding — edge cases in complex accounting may be misinterpreted","Free tier likely limits query complexity, number of scenarios per session, or model depth","No visibility into how the system handles ambiguous financial terminology or multi-step reasoning"],"requires":["Web browser with internet connectivity","Basic understanding of financial concepts (revenue, expenses, margins)","Free Slated account"],"input_types":["natural language text queries","financial parameters (revenue figures, growth rates, cost structures)"],"output_types":["projected financial statements","scenario comparison tables","narrative explanations of model results"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_slated__cap_1","uri":"capability://data.processing.analysis.real.time.portfolio.risk.assessment.and.metric.calculation","name":"real-time portfolio risk assessment and metric calculation","description":"Analyzes portfolio composition and market conditions to compute risk metrics (Value-at-Risk, Sharpe ratio, correlation matrices, drawdown scenarios) with real-time or near-real-time data feeds. The system ingests portfolio holdings, market data, and historical volatility to surface actionable risk signals. Implementation likely uses vectorized financial calculations (NumPy/Pandas-style) combined with streaming data connectors to major financial data providers, enabling rapid risk re-evaluation as market conditions shift.","intents":["Understand portfolio risk exposure across asset classes and correlations in real-time","Identify concentration risk, sector exposure, and tail-risk scenarios before they materialize","Monitor portfolio Greeks, volatility, and drawdown potential without manual recalculation"],"best_for":["Individual investors managing multi-asset portfolios","Small hedge funds or family offices without dedicated risk infrastructure","Startup founders tracking cap table and equity compensation risk"],"limitations":["Real-time data freshness depends on upstream provider latency — may lag market moves by seconds to minutes","Free tier likely limits portfolio size (number of holdings), update frequency, or historical lookback window","Risk models assume historical volatility patterns; may underestimate tail risks during market regime shifts","No transparency on which risk models are used (parametric VaR, historical simulation, Monte Carlo) or their calibration"],"requires":["Portfolio data (holdings, quantities, entry prices)","Real-time or daily market data feed (likely from free sources like Yahoo Finance or premium providers)","Slated account with API access or web interface"],"input_types":["portfolio holdings list (ticker, quantity, cost basis)","market data (prices, volatility, correlations)","risk parameters (confidence levels, time horizons)"],"output_types":["risk metrics (VaR, Sharpe ratio, max drawdown)","scenario analysis results","risk heatmaps and correlation matrices","alerts and recommendations"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_slated__cap_2","uri":"capability://data.processing.analysis.multi.scenario.financial.projection.and.sensitivity.analysis","name":"multi-scenario financial projection and sensitivity analysis","description":"Enables users to define base-case, bull-case, and bear-case financial scenarios with varying assumptions (revenue growth, margin compression, interest rates, etc.) and automatically generates comparative projections across all scenarios. The system likely uses a scenario tree or branching logic engine that propagates assumption changes through financial statement templates, computing outputs for each path. This allows users to understand downside/upside outcomes and identify which assumptions drive the largest variance in outcomes.","intents":["Model bull/base/bear cases for investment thesis validation or fundraising pitch decks","Understand which financial assumptions have the highest sensitivity and impact on outcomes","Generate scenario-based financial statements for board presentations or investor updates"],"best_for":["Startup founders building financial projections for pitch decks or board meetings","Venture capitalists evaluating investment theses across multiple scenarios","CFOs stress-testing financial plans against market downturns"],"limitations":["Free tier likely limits number of scenarios (e.g., 3-5 max) or projection periods (e.g., 5 years)","Scenario branching complexity may be constrained — no support for dynamic scenario trees or conditional logic","Sensitivity analysis may be limited to one-way or two-way sensitivity; full Monte Carlo simulations likely premium-only","No built-in scenario templates for specific industries — users must manually define assumptions"],"requires":["Base financial model or historical financials","Assumption parameters for each scenario (growth rates, margins, capital expenditure)","Slated account"],"input_types":["financial statement templates (P&L, balance sheet, cash flow)","assumption sets (revenue growth %, COGS %, OpEx %, tax rate, etc.)","scenario definitions (names, probability weights)"],"output_types":["projected financial statements per scenario","sensitivity tables (tornado charts, waterfall analysis)","scenario comparison summaries","key metric dashboards (EBITDA, FCF, ROI by scenario)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_slated__cap_3","uri":"capability://text.generation.language.chatbot.driven.financial.analysis.and.insight.generation","name":"chatbot-driven financial analysis and insight generation","description":"Provides a conversational interface where users ask follow-up questions about financial models, risk metrics, or scenarios and receive natural language explanations and recommendations. The chatbot maintains context across a conversation, allowing users to drill into specific line items, ask 'why' questions, and receive interpretable explanations of model outputs. Implementation likely uses an LLM with financial domain fine-tuning, retrieval-augmented generation (RAG) to ground responses in the user's actual data, and a conversation memory system to track context across turns.","intents":["Ask clarifying questions about financial model outputs without re-running calculations","Get plain-English explanations of risk metrics, scenario outcomes, or financial ratios","Receive AI-generated insights and recommendations based on portfolio or model analysis"],"best_for":["Non-technical investors who need interpretable financial insights","Teams collaborating on financial decisions and needing a shared knowledge interface","Users exploring financial data without predefined questions"],"limitations":["LLM hallucination risk — chatbot may generate plausible-sounding but incorrect financial advice","No audit trail or explainability for recommendations — difficult to validate or challenge AI reasoning","Conversation context window likely limited on free tier, forcing users to restart conversations","Chatbot may not understand domain-specific jargon or edge cases in financial terminology"],"requires":["Active Slated session with loaded financial data or model","Web browser with chat interface","Basic financial literacy to evaluate chatbot responses"],"input_types":["natural language questions","follow-up clarifications","requests for specific metrics or comparisons"],"output_types":["natural language explanations","recommendations and insights","metric definitions and interpretations","links to underlying data or calculations"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_slated__cap_4","uri":"capability://data.processing.analysis.data.import.and.normalization.from.multiple.financial.sources","name":"data import and normalization from multiple financial sources","description":"Ingests financial data from multiple sources (CSV uploads, API connections to brokerages, accounting software integrations, manual entry) and normalizes them into a unified data model for modeling and analysis. The system likely uses schema mapping, data validation, and reconciliation logic to handle inconsistencies across sources (e.g., different date formats, currency conversions, account hierarchies). This enables users to combine data from their brokerage, accounting software, and manual inputs into a single coherent financial picture.","intents":["Import portfolio holdings from a brokerage or CSV without manual data entry","Connect accounting software (QuickBooks, Xero) to pull historical financials automatically","Combine data from multiple sources (brokerage, bank, accounting) into a unified model"],"best_for":["Users with data spread across multiple platforms (brokerage, accounting, spreadsheets)","Startups integrating financial data from multiple sources for consolidated reporting","Individual investors consolidating multi-account portfolios"],"limitations":["Free tier likely supports only CSV uploads or limited API integrations — premium tier may unlock brokerage/accounting software connectors","Data refresh frequency may be limited on free tier (daily or weekly, not real-time)","No support for complex data transformations or custom field mappings — only standard financial data types","Data validation and reconciliation may be manual or require user intervention for edge cases"],"requires":["Financial data in supported formats (CSV, API, or manual entry)","API credentials for connected services (if using integrations)","Slated account"],"input_types":["CSV files (portfolio holdings, transactions, financial statements)","API connections (brokerage, accounting software)","manual data entry forms"],"output_types":["normalized financial data tables","consolidated portfolio views","unified financial statements","data quality reports and reconciliation summaries"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_slated__cap_5","uri":"capability://image.visual.interactive.financial.dashboard.and.visualization","name":"interactive financial dashboard and visualization","description":"Renders financial models, risk metrics, and portfolio data as interactive charts, tables, and KPI cards that update in real-time or on-demand. The dashboard likely uses a web-based charting library (D3.js, Plotly, or similar) with drill-down capabilities, allowing users to click into summary metrics to view underlying details. The interface is designed for non-technical users, with pre-built layouts for common use cases (portfolio overview, risk heatmap, scenario comparison) and customization options for power users.","intents":["View portfolio composition, risk metrics, and performance in a single dashboard","Drill down from summary metrics to underlying holdings or transactions","Share financial dashboards with stakeholders or board members"],"best_for":["Individual investors monitoring portfolios","Startup founders tracking financial health and KPIs","Teams collaborating on financial decisions with shared dashboards"],"limitations":["Free tier likely limits dashboard customization, number of visualizations, or refresh frequency","No export to PDF or PowerPoint — may require screenshots or manual recreation for presentations","Real-time updates may lag on free tier due to API rate limiting","Mobile responsiveness may be limited on free tier"],"requires":["Web browser (desktop or mobile)","Financial data loaded into Slated","Slated account"],"input_types":["financial data (holdings, metrics, scenarios)","user preferences (chart types, metrics to display)"],"output_types":["interactive charts and visualizations","KPI cards and summary metrics","drill-down detail views","shareable dashboard links"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_slated__cap_6","uri":"capability://data.processing.analysis.automated.financial.ratio.and.metric.calculation","name":"automated financial ratio and metric calculation","description":"Computes standard financial ratios (liquidity, profitability, leverage, efficiency, valuation) and performance metrics (ROI, IRR, Sharpe ratio, alpha, beta) automatically from financial statements or portfolio data. The system uses formula templates for each metric, applies them to user data, and surfaces results in context-aware formats. This eliminates manual calculation and ensures consistency across analyses, enabling users to compare their metrics against industry benchmarks or historical trends.","intents":["Calculate financial ratios and performance metrics without manual formula entry","Compare personal or company metrics against industry benchmarks","Track metric trends over time to identify improving or deteriorating financial health"],"best_for":["Individual investors evaluating stock or company quality","Startup founders tracking financial health metrics","Finance teams conducting competitive analysis"],"limitations":["Free tier likely limits number of metrics calculated or historical periods tracked","Benchmark data may be limited or outdated on free tier","No support for custom metrics or industry-specific ratios","Metric definitions may not match user's preferred accounting standards (GAAP vs IFRS)"],"requires":["Complete financial statements (P&L, balance sheet, cash flow) or portfolio data","Slated account"],"input_types":["financial statement line items","portfolio holdings and prices","time periods for analysis"],"output_types":["calculated financial ratios","performance metrics","benchmark comparisons","trend analysis and charts"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_slated__cap_7","uri":"capability://automation.workflow.financial.model.versioning.and.audit.trail","name":"financial model versioning and audit trail","description":"Maintains a history of model changes, assumptions, and outputs, allowing users to revert to previous versions, compare assumptions across versions, and track who made changes and when. The system likely uses a version control backend (Git-like) with financial-specific metadata (assumption changes, output deltas, user annotations). This enables collaborative modeling, accountability, and the ability to understand how a model evolved over time.","intents":["Revert to a previous model version if new assumptions prove incorrect","Compare assumptions and outputs across different model versions","Track changes to financial models for audit and compliance purposes"],"best_for":["Teams collaborating on financial models","Regulated entities requiring audit trails for financial decisions","Users iterating on models and needing to compare versions"],"limitations":["Free tier likely limits version history depth (e.g., last 10 versions only)","No granular change tracking at the assumption level — may only show full model snapshots","Collaboration features (comments, annotations) likely premium-only","No integration with external version control systems (Git)"],"requires":["Active Slated account with model history","Web browser"],"input_types":["model changes (assumption updates, formula edits)","user annotations and comments"],"output_types":["version history list","version comparison reports","audit trail logs","revert/restore functionality"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_slated__cap_8","uri":"capability://data.processing.analysis.comparative.financial.analysis.and.peer.benchmarking","name":"comparative financial analysis and peer benchmarking","description":"Enables users to compare their financial metrics, ratios, and performance against peer companies, industry averages, or historical benchmarks. The system likely maintains a database of public company financials and industry statistics, allowing users to select peers and automatically generate comparison reports. This helps users contextualize their financial performance and identify areas of strength or weakness relative to competitors.","intents":["Benchmark company or portfolio metrics against industry peers","Identify competitive advantages or disadvantages through financial comparison","Validate financial assumptions by comparing against peer performance"],"best_for":["Startup founders evaluating company performance against competitors","Investors conducting competitive analysis","Finance teams benchmarking operational efficiency"],"limitations":["Benchmark database likely limited on free tier — may only include large public companies or specific industries","Peer selection may be manual or limited to predefined peer groups","Benchmark data may be delayed (quarterly or annual) rather than real-time","No support for custom peer groups or weighted benchmarks"],"requires":["User's financial data loaded into Slated","Peer company tickers or identifiers","Slated account"],"input_types":["user's financial metrics","peer company identifiers (tickers, names)","benchmark period and metrics to compare"],"output_types":["peer comparison tables","benchmark percentile rankings","gap analysis reports","visualization of user vs peers"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Web browser with internet connectivity","Basic understanding of financial concepts (revenue, expenses, margins)","Free Slated account","Portfolio data (holdings, quantities, entry prices)","Real-time or daily market data feed (likely from free sources like Yahoo Finance or premium providers)","Slated account with API access or web interface","Base financial model or historical financials","Assumption parameters for each scenario (growth rates, margins, capital expenditure)","Slated account","Active Slated session with loaded financial data or model"],"failure_modes":["Accuracy depends on LLM's financial domain understanding — edge cases in complex accounting may be misinterpreted","Free tier likely limits query complexity, number of scenarios per session, or model depth","No visibility into how the system handles ambiguous financial terminology or multi-step reasoning","Real-time data freshness depends on upstream provider latency — may lag market moves by seconds to minutes","Free tier likely limits portfolio size (number of holdings), update frequency, or historical lookback window","Risk models assume historical volatility patterns; may underestimate tail risks during market regime shifts","No transparency on which risk models are used (parametric VaR, historical simulation, Monte Carlo) or their calibration","Free tier likely limits number of scenarios (e.g., 3-5 max) or projection periods (e.g., 5 years)","Scenario branching complexity may be constrained — no support for dynamic scenario trees or conditional logic","Sensitivity analysis may be limited to one-way or two-way sensitivity; full Monte Carlo simulations likely premium-only","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.35833333333333334,"quality":0.7200000000000001,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:33.096Z","last_scraped_at":"2026-04-05T13:23:42.552Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=slated","compare_url":"https://unfragile.ai/compare?artifact=slated"}},"signature":"Fdk+kXlPnop1Sb3FtsP5LXXr9uvzsZgWL8M9CJZQGK5XFXEUdDw6QptwI6I2kH5MN3cSjZOmwWR+KFncyxJMDg==","signedAt":"2026-06-22T14:10:06.427Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/slated","artifact":"https://unfragile.ai/slated","verify":"https://unfragile.ai/api/v1/verify?slug=slated","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}