Rose AI

What is Rose AI?

Rose AI is a cloud data platform for finance professionals that brings dataset discovery, cleaning, transformation and charting into one browser workspace. Its own documentation calls it a top-down data tool.

The workflow starts with a dataset, either one already in Rose or a file you upload. You add code modules to a notebook, apply transformations with short commands, and view the output as an interactive chart. The documentation says the product was designed by financial professionals from some of the world’s largest hedge funds, and it highlights access to the Federal Reserve’s FRED data.

A June 2025 press release describes Rose AI as a New York company led by chief executive Alex Campbell, serving institutional investors, investment managers and quant teams. We could not read the main rose.ai pages, which load as a script-only app, so some details here come from that release and from directories. Rose AI is a research tool, not investment advice, and any trading decision you make with its data carries a risk of loss.

Key Features

Chainable transformations inside a notebook matter most, because they turn a raw economic series into a usable chart in a few commands. The other features support sourcing, sharing and auditing that data.

• Notebook workspace: The documentation describes a notebook interface where you chain data operations in code modules and save the result. Each module runs with Shift+Enter or a play button, so an analysis builds up step by step.
• Built-in transformations: Short suffix commands change a series in place, such as adding :yoy to a GDP series for year-over-year growth. One directory reports more than 100 built-in transformations of this kind.
• FRED data access: Rose connects to the Federal Reserve’s free FRED API, which its documentation says opens thousands of economic datasets. You can pull a macro series into a notebook without downloading a spreadsheet first.
• Interactive charts: Results appear as charts with an adjustable timeline and hover values for each point. This makes it quick to compare periods or check a figure before sharing the chart with colleagues.
• Data marketplace: Directories and a 2025 press release describe a marketplace where users preview, buy and sell datasets. A June 2025 partnership added blockchain data covering more than 200 networks through SQD’s OceanStream.
• Traceable outputs: Directories describe logic trees that link each output back to its raw inputs and transformations. For research teams, that audit trail makes it easier to explain how a chart or number was produced.

Pros

Speed from question to chart is why macro analysts look at Rose AI: free FRED data and one-line transformations remove most spreadsheet busywork.

• Built by finance people: The documentation says hedge fund professionals designed Rose, and the workflow reflects that. Time series, macro data and year-over-year maths are first-class ideas rather than add-ons.
• Fast macro research: FRED access and one-line transformations let you chart an economic series within minutes. Analysts can test an idea before deciding whether it deserves a full model.
• Audit trail by design: Outputs are reported to trace back to source data through logic trees. Compliance and investment committees can see how a figure was built rather than trusting a pasted chart.
• Shared datasets: Directories describe permission-based sharing of datasets and notebooks. Teams can work from one cleaned version of a series instead of passing spreadsheets around by email.
• Alternative data options: The marketplace and the OceanStream partnership extend coverage into areas such as on-chain data. Quant teams can add new sources without building every pipeline themselves.

Cons

Opaque pricing is the first hurdle with Rose AI, since we found no readable plan page and directories quote figures that do not agree.

• Unclear pricing: Rose does not show plans on a page we could read, and directories disagree on cost. You will likely need to speak to the company before you can budget for it.
• Learning curve: The notebook relies on its own command syntax, which the documentation calls rosecode. Analysts used to Excel menus may need time and examples before they feel productive.
• Thin public documentation: The public docs cover basics such as datasets, transformations and charts. Detailed guidance on security, data licensing and limits was not visible, so ask for it in writing.
• Paid datasets add cost: Licensed datasets in the marketplace are reported to need their own subscriptions or purchases. Your real cost may therefore depend more on data than on the platform itself.
• Name confusion: Several unrelated products use the Rose name, and at least one directory listing describes generic features. Check that any review you read actually refers to rose.ai.

Who is Using Rose AI?

Finance teams at institutions form Rose AI’s core audience, according to its 2025 press release, with individual researchers using the public data features.

• Investment analysts: Buy-side analysts use it to pull macro and market series into one place. They chart and transform data to support research notes.
• Quant teams: Quantitative researchers use the marketplace and data pipelines. They test new datasets, including on-chain data, before adding them to models.
• Institutional investors: Press coverage names institutional investors and investment managers as the target customers. They use it to share traceable research across teams.
• Economists and researchers: Researchers working with public economic data use the FRED connection. They build quick charts of growth rates, inflation and similar indicators.

Pricing

Rose AI did not show a public price list we could read, so we treat it as contact for pricing (directory reports, checked October 2026).

Futurepedia lists a free tier and a $49.99 monthly plan, but its description does not match the finance product, so we give it little weight.

Disclaimer: these figures come from third parties and may be out of date, so get a written quote from Rose AI and check any separate data licence fees.

What Makes Rose AI Unique?

Traceability built into a finance-first notebook is the core difference. General business intelligence tools chart whatever you load, while Rose starts from economic and market series and records how each output was derived.

The marketplace adds a second angle. Rather than only analysing data you already own, teams can preview and license new datasets, including the on-chain data added through the 2025 OceanStream partnership, inside the same workspace.

Compatibilities and Integrations

Data sources are where Rose AI connects most, with FRED named in its own documentation and further providers added through its marketplace.

• Platform: Rose AI is web-based, and one directory reports no mobile app.
• Public data: The FRED API gives access to thousands of US economic series.
• Partner data: SQD’s OceanStream supplies blockchain data across more than 200 networks.
• Spreadsheets and code: One directory lists Google Sheets, Excel and Python connections, which we could not confirm.
• Uploads: The documentation says you can upload your own datasets alongside existing ones.

Rose AI Tutorials

The official documentation at docs.rose.ai walks through datasets, transformations and charts, and the steps below follow the same order.

1. Create an account at rose.ai or ask the company for team access.
2. Open a notebook and add a code module.
3. Search for a dataset, such as a FRED series, or upload your own file.
4. Apply a transformation, for example :yoy for year-over-year change, and press Shift+Enter.
5. Adjust the chart timeline and hover over points to check values.
6. Save the notebook and share it with colleagues using the permission settings.

How We Rated It

Rose AI scores 3.5 out of 5 in our review, held back mainly by unclear pricing and thin public documentation rather than by the research workflow itself. Its traceable notebook approach is the strongest part of the product.

Summary

Rose AI suits finance and macro research teams who want traceable charts built from public and licensed data in one place. Its notebook and transformation syntax are well suited to analysts who think in time series. The main limitation is transparency, because pricing, security details and many features could only be confirmed through directories and press coverage. Treat its output as research support, not investment advice, and remember that markets can move against any position. Before committing, ask for a demo using a dataset your team already knows well.