Mercury AI: Quote Ingestion and Smarter Insurance Work

Start the quote with information, not keystrokes

An agent arrives with information about a risk. That information may already be organized in a competitor quote, a spreadsheet or another input, yet the next step can still become an exercise in entering it again.

Mercury AI document ingestion changes that starting point. Competitor quotes or plain data can be imported without retyping the quote information. The focus moves from recreating the input to working with it inside the quoting process.

Consider what you want your team to do when a new opportunity arrives. You want people to understand the submission, consider the business and move the quote forward. Copying information from one place to another is not the expertise you hired them to provide.

This is a concrete way to evaluate AI in an insurance system. Start with an input your people handle, follow it into the system and examine the work it removes from their day. A useful demonstration begins with your quoting process rather than an abstract promise about transformation.

Hundreds of horses, without retyping the quote

One Mercury customer provides a clear example of AI ingestion at work. Its agents can upload competitor quotes containing information for hundreds of horses, and the quote data is imported automatically.

The agent does not have to recreate the quote information horse by horse. The information already supplied becomes the input for the next stage of work in Mercury.

That distinction matters in a quote with a large amount of detail. The practical value is not a new place to store a document. It is the ability to use the information in that document without first assigning someone to enter it again.

For a carrier considering a similar workflow, the demonstration should follow a representative quote from upload to imported information. Look at what your team needs next and how that starting point changes the work of preparing a quote.

The equine example also makes the business case easy to understand. Hundreds of horses represent hundreds of opportunities for repetitive entry; Mercury puts AI to work on the information that is already there. Your people can bring their attention back to the insurance opportunity rather than the keyboard.

Loss runs in different formats, one less typing burden

A second company uses Mercury AI to ingest loss runs. These documents arrive in many different formats, but their information still needs to become usable within the system.

Without ingestion, that work can mean reading the source, typing the details and then repeating the process for the next loss run. Format differences add another layer of effort to a task the team has already performed many times.

Mercury applies AI to ingest those loss runs rather than make staff type and retype the information. For this customer, the capability saves substantial time spent on repetitive entry and helps move the work beyond document preparation.

The value is closely connected to quoting. Loss history becomes useful when the team can work with it, not merely when a file has arrived. Ingestion addresses the gap between receiving the document and having its information in the system.

When you explore this capability, bring the range of loss-run formats your operation receives. Discuss what your people need from them and examine the import process against those inputs. The strongest demonstration is one that resembles the work your team actually owns.

Predictive AI built around the client’s own claims

Ingestion is one part of Mercury’s AI capability. The system also uses predictive AI trained on a client’s own claims to help predict frequency and severity.

Those are two related but different questions: how often claims may occur and how large the losses may be. Mercury brings the client’s claims experience into that analysis instead of making the discussion about AI in the abstract.

The models are refined over time as more data is used in training. This gives the learning process an ongoing relationship with the client’s claims experience rather than treating the initial training data as the end of the story.

For an insurance leader, the useful conversation is about the decisions this information can help inform. Which business questions matter? What claims experience is available? How should the team examine frequency and severity together?

This capability deserves its own place in a Mercury demonstration. It is different from importing a quote or reading a document: it uses accumulated claims experience to help the business look ahead. Together, ingestion and predictive AI show how Mercury applies different tools to different kinds of insurance work.

Ask one question across several documents

Sometimes the information is already in the file, but the answer is not in one place. A claims professional may need to read several documents and combine their details before answering a simple question.

Mercury supports AI document and information retrieval using natural-language processing. It can pull information from multiple sources and compile that information into an answer.

Consider the question, “What was the last medication prescribed to this claimant?” The useful answer may need more than the name of the medicine. The person asking may also need to know how much was prescribed and what it cost.

Mercury can bring together information from an explanation of benefits, or EOB, a hospital invoice and a doctor’s notes to answer that question. Three separate documents can contribute to one response containing the medication name, prescribed amount and cost.

The example illustrates the difference between locating a document and retrieving an answer. Instead of handing the user three files to read, the system combines relevant information to address the question being asked.

For a claims team, that makes a useful demonstration concrete. Begin with a question whose answer spans documents, identify the sources involved and see how Mercury compiles the information. The result connects the question to information already present across the claim file.

Four capabilities, one practical standard

These examples involve different kinds of AI, but the standard for evaluating them is the same: what work does the capability help your people complete? A quote upload, a loss-run import, a prediction and a multi-document answer should each have a clear business purpose.

For small and midsized carriers, this is a way to discuss technology in terms of the people doing the work. You can start with a repeated task and explore the capability that addresses it, without making the first conversation about every possible use of AI.

Billion-dollar carriers also use Mercury. The same practical approach gives larger organizations a clear starting point: identify the information problem, examine the workflow and evaluate the system against a task that matters.

See AI doing the work in Mercury

AI earns its place in an insurance operation through useful work. Mercury’s customer examples put that work in plain view: quote information for hundreds of horses imported without retyping, and loss runs ingested across differing formats instead of being entered again.

Predictive claims analysis and multi-document retrieval extend that approach. They use information to help people look ahead or get an answer, while ingestion gets information into the workflow in the first place.

Start your own evaluation with one process. Bring the quote your agents have to re-enter, the loss runs your team repeatedly types, the claims question you want to explore or the answer that currently requires opening several documents.

Take a closer look at Mercury and schedule a focused demonstration with Quick Silver Systems. Let’s show you how AI can do useful work in your insurance operation, beginning with the information you already have.