Past battle · 2024-07-10 UTC
AI Data Analysts Showdown — July 10, 2024
From the AI Data Analysts category. 8 marks placed across 4 fighters. Fyva AI took the crown.
Final standings
The line-up
The fighters
Profiles of every tool that competed in this battle, ranked by their final score.
Fyva AI
AI copilot that helps analysts generate equity research reports from filings and market data.
Fyva AI is a research assistant built for equity analysts, investment teams, and finance professionals. It ingests company filings, financial data, and other source material to help users draft research notes, summaries, and investment insights more quickly than manual workflows allow. The tool focuses on accelerating repetitive parts of the research process, such as extracting key figures from 10-Ks and 10-Qs, summarizing earnings calls, and structuring initial report drafts. Analysts can then refine the AI-generated output with their own judgment and proprietary views before publishing internally or to clients.
Criteria breakdown
- Automated equity report generation
- Filings and document analysis
- Earnings and financial data summarization
- Insight extraction for investment theses
- Analyst-focused research workspace
MinusX is an AI agent that plugs directly into analytics platforms like Jupyter, Metabase, and Tableau, acting as a hands-on data analyst within the tools teams already use. Instead of exporting data or switching contexts, users can ask questions in natural language and have MinusX query data, build charts, and edit dashboards on their behalf. The agent can interpret schemas, write SQL or Python, explain its reasoning, and iterate on results based on follow-up prompts. It is aimed at analysts, data scientists, and business users who want to speed up exploratory analysis, reporting, and routine dashboard maintenance. By operating inside the host tool rather than as a standalone interface, MinusX fits into existing workflows and respects the permissions and connections already configured there.
Criteria breakdown
- Browser extension for analytics tools
- Natural language to SQL and Python
- Automated dashboard creation and edits
- Context-aware schema understanding
- Iterative chat-based analysis
- Support for Jupyter, Metabase, Tableau, and more
Shortcut (Excel AI)
AI Excel agent that builds and edits spreadsheets, models, and analyses through chat and a native Excel add-in
Shortcut is an AI agent purpose-built for spreadsheet work, designed to plan, build, and edit Excel models, analyses, and reports from natural-language instructions. It positions itself for finance professionals — analysts at hedge funds, asset managers, and similar institutions — where accuracy and auditability matter more than raw speed. The company markets it as deployed across large multi-strategy hedge funds and thousands of daily active seats. The tool can be used in two ways: a standalone web application and a native Excel plug-in. The web app is described as offering roughly 95% feature parity with Excel, while the plug-in is meant to deliver full parity by working directly inside the user's existing Excel environment, including macros, keyboard shortcuts, and large files. Files can be opened and exported in Excel format without loss of formatting, formulas, or features, which lowers the friction of fitting it into established workflows. There is also a terminal-first CLI (ShortcutXL) aimed at power users who want to build and edit multiple models in parallel inside desktop Excel. A central design emphasis is correctness. Shortcut claims its outputs are formula-driven rather than hard-coded, so results update dynamically with the underlying data instead of breaking when inputs change. It applies professional-grade formatting and is built to place edits precisely without overwriting existing data — a common failure mode of generic AI spreadsheet tools. The company points to SpreadsheetBench results and a reported 90% win rate against first-year analysts in head-to-head challenges as evidence of its accuracy claims. Auditability and trust are framed as first-class concerns. Shortcut shows every changed cell, indicates which values are hard-coded and why, and lets users revert, restore, or undo any step in the action sequence. On security, it advertises SOC 2 Type II compliance, AES-256 encryption at rest and TLS 1.3 in transit, role-based access controls, zero-retention agreements with its AI providers, and a policy that paid-plan data is never used for model training. Compared with general-purpose assistants like ChatGPT, Claude, or Microsoft Copilot in Excel, Shortcut is narrowly specialized for spreadsheet construction and claims meaningfully higher accuracy on benchmark tasks. Its differentiation rests on Excel-native operation, formula-driven outputs, and the auditability features that institutional finance users require. The trade-off of that specialization is a tight focus on Excel-centric finance and data work rather than broad office productivity, and many of its performance claims are vendor-reported benchmarks that prospective buyers will want to validate against their own workflows.
Criteria breakdown
- Native Excel plug-in plus standalone web app
- ShortcutXL terminal-first CLI for power users
- Formula-driven, dynamically updating outputs
- Cell-level change auditing with revert/restore/undo
- Professional industry-standard formatting
- Lossless Excel file import and export
SigTech MAGIC
AI agents for quantitative financial research, analysis, and strategy backtesting
SigTech MAGIC is an AI-driven offering from SigTech, a company known for providing institutional-grade quantitative investment technology. The product applies large language model agents to financial research and analysis workflows, aiming to let users interrogate market data, build and test investment strategies, and generate analysis through natural-language interaction rather than writing large amounts of code by hand. SigTech's broader platform has historically focused on systematic trading and backtesting, giving quants and portfolio managers access to clean historical data, instrument pricing, and a Python-based research environment for developing and validating strategies. MAGIC extends this lineage by layering AI agents on top of that data and tooling, with the goal of automating parts of the research process such as data retrieval, exploratory analysis, and the construction of backtests. The intended audience is institutional finance professionals — quantitative analysts, portfolio managers, and research teams at asset managers, hedge funds, and banks — who need to move from a research question to a tested hypothesis quickly. By combining conversational AI with the underlying quant infrastructure, the tool is positioned to reduce the time spent on routine data wrangling and boilerplate coding. Because reliable, detailed public information about MAGIC's exact current capabilities is limited, prospective users should verify specifics — supported data sets, model behavior, and integration options — directly with SigTech. As with any AI applied to financial analysis, outputs warrant careful human review before being used in any investment context.
Criteria breakdown
- AI agents for financial research and analysis
- Natural-language strategy development
- Portfolio and strategy backtesting
- Access to historical market and instrument data