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Bloomberg announced on October 15, 2026, that ASKB—its conversational AI interface for Bloomberg Terminal users—is generally available to financial professionals. Unlike a consumer chatbot, ASKB combines Bloomberg’s licensed market intelligence with a firm’s permissioned chats, watchlists, Worksheets, research and files, returning answers with source attribution.
What Bloomberg ASKB is
ASKB is Bloomberg’s conversational and agentic AI layer inside the Bloomberg Terminal. Users ask questions in natural language instead of navigating separate searches, analytics screens and document repositories. The system coordinates Bloomberg AI agents across structured and unstructured market data, news, research, documents and analytics, while also using proprietary material that a client has authorized.
Bloomberg describes the product as an evolution from data discovery to “institutional intelligence.” Wayne Barlow, Bloomberg’s Global Head of Terminal Products, said the goal is to bring an investor’s professional context together with Bloomberg’s market intelligence “in one conversation.” Chief Technology Officer Shawn Edwards summarized the approach as: “This isn’t Bloomberg building on AI. It’s AI built on Bloomberg.”
What changed on October 15, 2026
The October launch moved ASKB from beta to general availability for financial professionals. The release adds the ability to bring Instant Bloomberg (IB) chats, Worksheets and files into conversations, so an answer can reflect both Bloomberg information and the user’s existing work.
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Searchable Instant Bloomberg conversations
When enabled, ASKB can search as much as three months of a client’s chat history using plain-language questions and cite the source chat. That lets an analyst locate a prior discussion, decision or market observation without manually scrolling through messages. Bloomberg says roughly 40 million IB chats are generated each day, making conversational retrieval useful for firms with substantial internal discussion history.
Worksheets and Bloomberg Drive files
ASKB can analyze securities already tracked in Bloomberg Worksheets and answer questions about files stored in Bloomberg Drive. Uploaded files remain private unless the uploader shares them with colleagues, preserving the permission boundaries around firm material.
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How ASKB produces and supports an answer
- Interpretation: The user asks a question in natural language, such as a request to examine securities in a Worksheet or find a conclusion in prior IB chats.
- Agent coordination: ASKB selects and coordinates Bloomberg AI agents that can work across market data, news, research, documents, analytics and approved proprietary sources.
- Context-aware analysis: The response can combine Bloomberg’s content with the firm’s permissioned chats, watchlists, files and research rather than treating each source as an isolated search.
- Evidence display: Bloomberg says answers include transparent attribution to original sources and cited passages. For data-analysis responses, users can expose the underlying Bloomberg Query Language (BQL) code.
This design addresses a central problem with financial AI: a fluent answer is not enough if an investment professional cannot identify the evidence, reproduce the calculation or verify whether private context was used.
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Where Bloomberg says ASKB fits in the investment process
The April 16, 2026 roadmap described ASKB as an agentic engine embedded across Terminal workflows rather than a single chat window. The planned and described areas include:
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The roadmap shows the intended direction; the general-availability announcement confirms the October release’s chat, Worksheet and Bloomberg Drive capabilities. Bloomberg has not published a public price in the materials describing this launch.
Why Bloomberg’s data scale matters to the pitch
Bloomberg’s differentiation is the combination of its proprietary financial-data environment and a client’s own institutional context. Bloomberg says its platform carries more than 5,000 Bloomberg News stories each day, more than 1.1 million curated news items daily from the web, social media and other sources, and sell-side research from more than 1,200 providers. Those figures describe the breadth of material ASKB may draw on; they are not a guarantee that every answer uses every source.
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Bloomberg also says it has built and used AI in finance since 2009. That history is part of its argument that ASKB is an extension of an established financial-information platform, not a generic chatbot connected to a market-data feed.
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| Capability | ASKB’s stated approach |
|---|---|
| Primary audience | Financial professionals using Bloomberg Terminal |
| Knowledge base | Bloomberg market data, news, research, documents and analytics plus permissioned firm content |
| Private context | IB chats, watchlists, Worksheets and Bloomberg Drive files, subject to permissions |
| Verification | Source attribution and cited passages; BQL visibility for data-analysis responses |
| Workflow scope | Portfolio, risk, research management, monitoring and other Terminal processes |
| Access and cost | Commercial institutional service; public launch materials do not state a standalone price |
The practical distinction is not simply that users can type questions. It is that the conversation can be grounded in licensed financial content and the firm’s authorized working materials, with an audit trail intended for professional review.
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Trust, permissions and remaining boundaries
Financial users should still treat an AI response as an analytical starting point, not an automatic investment decision. Attribution helps a reviewer inspect the underlying material, while cited chat messages and passages make it easier to distinguish a source-backed answer from an unsupported synthesis. Exposing BQL can help technically skilled users check how a data query was formed.
Permissions remain important. ASKB’s access to chats and files depends on what a client enables and what a user is allowed to see; a private file is not automatically available to every colleague. Bloomberg’s descriptions establish these controls and attribution features, but they do not promise that AI-generated conclusions are error-free or that every Terminal workflow is fully automated.
What general availability means for Terminal users
For existing Bloomberg customers, ASKB represents a shift in how the Terminal can be used: users can start with an investment question and move through retrieval, analysis and workflow actions without switching among as many interfaces. The strongest use cases are likely to be questions that require both broad market evidence and narrow institutional memory—for example, connecting current news with a firm’s prior IB discussion or examining a watchlist alongside internal research.
It is not a standalone consumer product. Access is tied to Bloomberg’s commercial professional-services environment, and the roadmap’s integrations, permissions and data entitlements determine what an individual user can actually ask and retrieve.
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