On October 22, London Stock Exchange Group agreed to buy Octus for $3.535 billion in cash. The deal is a headline-sized vote of confidence in credit data, but the more useful lesson for business owners is hiding in the details.
Octus did not build its value by making another AI model. It built a business around information that is difficult to find, difficult to interpret and valuable once it is organized. Its teams gather hard-to-access credit documents, including loan covenants and deal terms, turn them into searchable data, and connect that information to tools customers use to do their work.
That may sound less dazzling than the latest AI breakthrough. It is also a reminder that in the AI era, the winning advantage may not be the model itself. It may be the useful, trusted information the model can work with.
What LSEG is buying
Founded in 2013 by Kent Collier, Octus specializes in intelligence, data and workflows for private, illiquid and distressed credit. The New York-headquartered company has more than 900 employees, 750 customers and over 40,000 individual users, including investment firms, banks, law firms and advisory firms.
Its niche matters because not every credit market works like a public stock exchange. In private and less-liquid markets, critical details may be buried in documents that are hard to locate and compare. Octus says its teams source those materials and convert them into structured, searchable information. Customers can use that data through Octus’s platform, portfolio-management tools, an MCP connector or AI workflows.
LSEG already has a substantial presence in liquid credit, with financial news, pricing and analytics products. Octus adds a stronger position in private and illiquid credit. Put simply, the acquisition is intended to help LSEG serve customers across more of the credit lifecycle, from finding opportunities to assessing risk and managing portfolios.
The transaction is expected to close in the first half of 2027, subject to regulatory approval. After completion, Octus will join LSEG’s Data & Analytics division, with Collier continuing to run the business.
The value is in the work before the AI answer
There is a tempting story about AI that goes like this: build a powerful model, connect it to a business, and watch the value appear. But a model can only be as useful as the information and context it can access.
If a company’s most important knowledge is scattered across PDFs, spreadsheets, inboxes and people’s memories, AI may help search the mess, but it cannot automatically make the underlying information reliable, consistent or ready to use. Someone still needs to identify what matters, organize it and keep it current.
That is the quieter work at the center of Octus’s offering. The value is not merely that a document exists. It is that someone has found it, understood what is important inside it, expressed those details consistently and made them usable in a workflow.
For a credit professional, that can mean being able to search across loan terms rather than reopen piles of documents one by one. For a business, the equivalent could be finding the right supplier agreement, understanding a recurring customer pattern or seeing how a process performs across multiple projects.
Octus is forecast to generate more than $300 million in 2026 revenue, with strong double-digit growth and an adjusted EBITDA margin of about 40%. Those are company expectations, not a promise that any business built around data will achieve the same results. Still, the figures show why specialized information can be commercially powerful when customers depend on it to make decisions and complete work.
Your own “boring” data may be more valuable than you think
Most businesses already possess information that took years to accumulate. It may not look like a product yet.
A creative agency may have project timelines, client preferences, revision patterns and production costs. A retailer may know which suppliers consistently deliver on time, which products generate repeat purchases and where returns tend to come from. An independent artist may have years of audience, release and performance data. A service business may have a rich record of customer questions and the solutions that actually worked.
On its own, that information can be difficult to use. Organized with care, it can help a business make better decisions, provide more consistent service and reduce time spent repeating the same research.
The first step is recognizing that information is an asset, not just admin to be stored and forgotten. The next is making it useful to someone, including the people who already work inside your business.
Four practical steps to turn knowledge into an asset
1. Identify what only you know.
Look for information that is proprietary because it comes from your customers, your history, your process or your relationships. Ask: What questions do we answer repeatedly? What do we track that others in our space do not? Which decisions rely on knowledge held by just one or two people?
2. Make it structured and searchable.
A folder full of files is not the same as a usable dataset. Start with a small, clear system: consistent names, categories, dates, owners and definitions. If you record customer feedback, for example, decide what counts as a product issue, a service request or a feature idea. Consistency makes patterns easier to spot and reduces confusion later.
3. Connect the information to a workflow.
Data becomes more useful when it helps someone take a next step. A supplier record might support purchasing decisions. A customer-history view might help a team prepare for a meeting. A library of proven campaign results could guide the next launch. The goal is not to collect information for its own sake; it is to make work better.
4. Prepare for AI access, with sensible safeguards.
Once your information is organized, explore ways to make it available to the tools your business uses, such as through secure integrations, connectors or APIs. Set permissions, protect personal or confidential data, and decide which information should never be sent to an external tool. The point is not to rush every file into AI. It is to make approved, reliable knowledge easier to find and use.
AI needs a foundation worth building on
The LSEG–Octus agreement does not mean every business needs to become a data company. It does suggest that information gains value when it is hard to replicate, well-organized and built into the way customers work.
That can be encouraging for smaller teams. You do not need billions of dollars or a giant technology department to begin. You may already have the raw material: customer insight, a distinct process, years of experience or a clear understanding of a niche market. The opportunity is to stop letting that knowledge remain scattered and start shaping it into something reliable and reusable.
AI can help people work with information faster. But the advantage starts earlier, with knowing what information matters, taking the time to structure it and connecting it to a real need.
The most valuable asset in your business may not look like a breakthrough at first. It might be the spreadsheet you finally clean up, the process you document or the customer pattern you learn to recognize. In an AI-driven market, that kind of groundwork can become the difference between having access to a tool and having something truly useful to put behind it.
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