Prophecy Launches DataGPT, Bringing Enterprise Data Workflows to Claude, ChatGPT, and Copilot

DataGPT turns AI conversations into visual data workflows that business teams can check. It also turns approved work into reusable, controlled data products that run on the cloud data platforms companies already own.

Prophecy, an AI platform for business data work, announced Prophecy DataGPT. DataGPT allows business data analysts to handle the entire data process inside ChatGPT, Claude, and Microsoft Copilot. They can start with messy files and finish with ready-to-use reports. Every result includes a visual workflow that users can inspect and check. Once approved, the work becomes reusable code that runs on the data platform the company already owns.

Highlights

• Speed up data work: Analysts can describe what they need without leaving ChatGPT, Claude, or Microsoft Copilot. DataGPT builds the entire workflow within the conversation, from bringing in and preparing data to analyzing it and producing results.
• Check results visually: Every answer includes a visual view of the workflow behind it, allowing business data teams to inspect and approve each step.
• Build on shared knowledge: Prophecy uses a knowledge graph to connect business information with data workflows. This helps teams apply the right definitions and business rules to their data work.
• Put results into regular use: Approved workflows become data products. They run as controlled, repeatable code on Snowflake, Databricks, or BigQuery, according to a schedule.

Business data analysts sometimes upload spreadsheets to AI apps such as ChatGPT or Claude to compare and match a quarter's worth of transactions. The AI then writes and runs a script in the background to produce the result. But because the analysts cannot read that code, they cannot check whether it is correct, improve it, or confidently take responsibility for the result.

When the chat session ends, the workflow disappears too. The steps that turned messy data into a finished result are lost. So is the business knowledge the user provided, including company rules, definitions of revenue, and approved exceptions.

This problem is growing as more data is created by AI. Gartner predicts that by 2028, “50% of organizations will adopt a zero-trust data governance as unverified AI-generated data grows.”

“Enterprise AI is moving from answering questions to doing repeatable work. Generating an answer has gotten simpler. The challenge now is making the underlying data verifiable, governed, and reusable. Companies that can close the gap between business intent and execution will improve decision velocity,” said Michael Ni, VP & Principal Analyst at Constellation Research.

“Coders got AI that changed the way they work, collaborating with them on code all the way from development to production, unlocking tremendous productivity. Business data analysts got question answering, and nothing else changed,” said Raj Bains, founder and CEO of Prophecy. “We are closing that gap. Business analysts now get what coders got – the visual workflow behind every result, in a form they can verify and change, built on governed knowledge. The work worth keeping moves to production on their data platform.”

AI that does the data work, not just answers questions

AI assistants can answer questions about data that has already been prepared. But enterprise data teams rarely start with data that is ready to use. Their real work begins with getting the data ready. They must bring in files, fix differences between systems, match records that have no shared identifier, and clean and change the data. Then they must produce reports, dashboards, and official filings in exact formats, making sure every number is correct and checked.

DataGPT is designed for analysts in finance, operations, and other business teams who work with spreadsheets and desktop tools. In one conversation, an analyst can upload a messy Excel file, compare it with an audit list and data from SharePoint, Snowflake, and Databricks, apply a pricing rule for discounted product bundles, review the results, and schedule the workflow to run again every month. DataGPT connects to files, SharePoint, data warehouses, ERP systems, and CRM systems. Every workflow also has open code stored in Git, so its logic remains clear and can be checked later.

Data teams review work instead of rebuilding it

With DataGPT, business analysts no longer need to create every workflow from the beginning. AI builds the workflow, and analysts check it visually instead. They can review each step and its results on screen, make small changes when needed, and approve the final version. Approved workflows then run the same way every time, without generating new AI content or adding further AI costs.

Bringing unofficial AI use under company control

Many business analysts already paste company spreadsheets into AI chat tools without IT knowing or approving it. This is one of the most common forms of shadow AI, which means using AI tools outside a company's approved processes. With DataGPT, uploaded files pass through Prophecy to the company's own data platform instead of going into an AI chat. Data platform leaders and IT teams can see every workflow and control it using the platform they already own, without creating another separate system.