Beyond a web subscription. Your entire research system.

Put Your AI Assistant to Work with CSIMarket

Give your AI assistant access to accurate, structured, and quality-controlled financial data for professional company, industry and quantitative research.

Until now, research meant doing the work yourself, opening one company page at a time, copying figures into a spreadsheet, hunting for the right ratio on the right report, and repeating it for every peer. A web subscription showed you the data; you still had to assemble the analysis by hand.

With MCP, that changes completely. Your AI assistant connects directly to the CSIMarket database and does the gathering, structuring and analysis for you. You ask a research question in plain language, and the assistant retrieves the exact data it needs, combines it across companies, industries, sectors, quantitative factors, ownership and historical periods, and turns it into a finished, reasoned answer, in minutes, not hours.

That means you can now:

• Research at a scale that was never practical by hand.Analyze 5, 20 or 100 companies in a single workflow instead of opening each page one by one. Compare a whole industry, benchmark against sector peers, or track how leverage and profitability have shifted over ten years, all from one question.
• Ask instead of search.You no longer need to know which CSIMarket page holds the number. Describe the research you want, and the assistant finds and assembles it for you.
• Work with quant-grade data, not just headline figures.Factor scores, risk measures, z-scores, signals, normalized fundamentals and industry benchmarks become inputs your assistant can actually reason over, the same building blocks professional analysts use.
• Get the deliverable, not just the data.Your research comes back as a finished Excel model, PDF report, Word document, PowerPoint deck or CSV dataset, formatted and ready to use, not raw numbers you still have to shape.
• Trust what comes back.Every answer is built on CSIMarket's accurate, structured and quality-controlled data so your AI reasons from real financial intelligence, not from generic, unverified sources.

This is research and data extraction reimagined: the depth of a professional financial database, combined with an AI assistant that does the heavy lifting. You bring the question, CSIMarket and your AI bring the answer.

Build Your Own Financial Research Platform

Bring CSIMarket's structured, quality-controlled financial data directly into your own research systems, AI workflows and applications.

That means you can:

• Build your own financial research platform and proprietary dashboards.
• Benchmark companies and industries across growth, profitability, valuation and financial strength, using 20 years of historic data.Benchmark your company, portfolios or custom inputs against industry peers, market segments and broader company universes. Ask your AI assistant or your financial system to benchmark your numbers against CSIMarket data.
• Power credit and risk analysis with structured financial data.Run quantitative credit and risk analysis, benchmark financial strength, identify deteriorating companies and detect early-warning signals across companies, portfolios and industries.
• Build quantitative investment strategies using factors, scores, risk metrics and historical data.
• Factor models and factor investing.Build single- and multi-factor models across value, quality, profitability, growth, leverage, momentum and risk. Rank the universe on each factor, combine factors into a composite score, and form long, short or long-short baskets. CSIMarket's factor scores and z-scores give you the normalized, cross-sectionally comparable inputs a factor model needs, so you're not re-deriving them from raw statements.
• Systematic screening and stock selection.Turn any investment thesis into a repeatable rule set (for example, high quality, improving profitability, reasonable valuation, low financial risk) and run it across the whole universe. The screener and factor/risk panel let you define, save and re-run these screens as the market moves, which is the backbone of any rules-based selection strategy.
• Cross-sectional ranking and scoring systems.Score every company on a custom weighted blend of metrics and rank them within their industry or sector. This is how you build "top decile / bottom decile" strategies, tilt portfolios, and watchlists that update as fundamentals change.
• Risk modeling and screening.Use the risk measures, leverage metrics and z-scores (including distress-style signals) to build risk filters: screen out high-financial-risk names, cap sector exposure, or flag deteriorating balance sheets before they show up in price.
• Industry and sector rotation.With industry and sector analytics and benchmarks, build rotation models that overweight sectors improving on profitability, margins or growth and underweight those deteriorating: a classic top-down systematic overlay.
• Ownership and positioning signals.Ownership data supports strategies built around institutional accumulation and distribution, concentration, and changes in holder composition as a secondary signal layer.
• Fundamental momentum and revision strategies.Track quarter-over-quarter and year-over-year changes in margins, returns, leverage and growth to build "fundamental momentum" signals (buying improving fundamentals, avoiding deteriorating ones) over multi-year history.
• Peer and relative-value strategies.Because every company can be benchmarked against its industry peers, you can build relative-value strategies: long the cheapest and highest-quality names relative to their own peer group rather than the market as a whole.
• Portfolio construction and backtesting.Feed any of the above signals into portfolio construction (equal weight, factor weight, risk-managed weight) and, because the data has historical depth, backtest the strategy across multiple years and market regimes before committing capital.
• Develop and backtest factor-based models in your own research environment.
• Automate financial screening, monitoring and research workflows.
• Power AI-powered research by connecting CSIMarket to your AI assistant through MCP.
• Support consulting, compliance, benchmarking and risk analysis work, including ESG and climate use cases.Carbon footprint analysis (measuring a company's overall carbon footprint), ESG data and analytics (the broader category covering environmental, social and governance data), and climate risk analysis (assessing financial exposure to climate-related risks).
• Supply-chain concentration analysis.Identify dependence on major customers, suppliers, industries or geographic markets and quantify concentration risk.
• Relationship-driven investment research.Use supplier, customer and corporate relationships to discover second-order investment opportunities, risks and fundamental transmission effects.