You can now attach and download files in chat.

Release Date: September 1, 2026
*This feature works only with the Claude model.

File Attachments (New Feature)

You can now attach files to the AI chat and ask questions or make requests regarding their contents.

Supported formats: Excel (.xlsx) / Word (.docx) / PDF / CSV / Text (.txt, .md, .log)

• The AI reads the contents of the attached file and uses that information to summarize, analyze, and answer questions.

• Once a file has been attached, it is saved in the conversation, so there's no need to reattach it when asking follow-up questions.

• You can attach up to 5 files per message, with a maximum size of 10 MB per file.

Examples of Use

Attach the CSV file containing the outage reports and say, "Please summarize last month's outages by type."

Attach the PDF of the procedure manual and ask, "Which parts of this procedure could be automated?"

File Generation and Export (New Feature)

You can now receive chat responses as files that you can use as-is.

• AI-Generated Files — If you make a request such as “Please convert these summary results into an Excel file,” the AI will generate files in Excel, Word, PDF, or CSV format. You can save them immediately by clicking the download button displayed in the response.

• Exporting Responses — We've added a "Download as File" menu option for each AI response message. You can now save any response in Excel (.xlsx), Word (.docx), PDF, or CSV format.

Examples of Use

"Please compile a table of this month's incidents and send it to me in Excel."

Download the failure analysis responses in Word and use them directly as a draft for your report.

Why Is This Feature Necessary?

Challenges to Date

Much of the information handled in incident response and day-to-day operations exists outside of chat.

CSV files exported from monitoring tools, PDFs of incident reports received from vendors, configuration lists and duty rosters managed in Excel, and procedure manuals in Word, among other documents. Until now, in order to consult the AI chatbot, a person had to read through these documents and copy and paste the key points.

There were three problems with this "pasting process."

1. It's time-consuming — CSV files with hundreds of rows or PDFs with dozens of pages simply can't be pasted in their entirety. By the time a person has to sort through and select the relevant data beforehand, information is already missing before it even reaches the AI.

2. Transcription errors occur — When responding to an urgent incident, it’s easy for omissions or misaligned lines to occur.

3. Can't be used in emergencies — When you're in the thick of an incident, you don't have the time to "open a file, format it, and paste it." As a result, people ended up getting by without asking the AI, which prevented its use from becoming standard practice.

The AI's response was confined to the chat.

The same applies in the opposite direction. Even when we had the AI perform data aggregation and analysis, the results remained as text within the chat window. To use them for reports to supervisors, explanations to customers, or materials for regular meetings, a person had to manually transcribe them into Excel or Word. It defeats the whole purpose if it takes several tens of minutes to turn the analysis—which the AI generated in just a few minutes—into a presentable document.

What Will Change with This Feature?

You can “send a file and receive a file” all within a single chat.

• I simply attach the CSV file from the monitoring tool and ask, “Please compile last month’s outages by type.” Then, I download the compiled data into Excel and use it as-is in my regular meeting materials.

• I attach the vendor's incident report PDF and ask them to "summarize the impact on our system." I then receive the summary in Word format and use it as a draft for an internal report.

By eliminating the manual work that precedes and follows the use of AI—namely, preprocessing (data entry and formatting) and postprocessing (compiling into a report)— the idea that “it’s faster to just ask the AI” actually becomes a reality.

Especially in time-sensitive situations like incident response, the ability to simply submit raw files to begin analysis directly contributes to a rapid initial response.

In addition, since attached files are carried over within the conversation, you can naturally continue delving deeper into topics—such as “Regarding the third column in this table” or “Following up on the log from earlier”—without having to reattach them. The goal of this feature is to transform the chat into a space where you can work alongside AI while referencing files, rather than just using it as a tool for one-off questions.

Why Use Incident Lake Instead of General-Purpose AI?

"If all you're doing is having an AI read and analyze a file, couldn't ChatGPT do that too?" That's a valid question, but in the context of incident response, there are three key differences that general-purpose AI chatbots simply can't bridge.

1. You can perform analyses that “connect” with your company’s historical data

Even if you provide a CSV file containing monitoring logs to a general-purpose AI, the AI will only know the contents of that file. Incident Lake's chat operates in the same location as your company's past incidents, knowledge base, and service configurations.

By analyzing the attached logs, we can determine that “there have been three previous incidents with similar symptoms, all of which were caused by the database connection pool running out.” This kind of analysis — which goes beyond examining a single file to take the organization’s response history into account —can only be performed by the entity that holds the data.

2. Incident data does not have to be shared externally

In Incident Lake, attachments are stored in self-managed storage that is isolated by tenant, and access permissions (RBAC) and audit mechanisms are subject to the same controls as existing incident data. Administrators can also monitor usage on a per-tenant basis. To balance the need for convenience with governance, the correct approach is to bring AI to where the business data resides.

3. The analysis results directly lead to "next steps"

With general-purpose AI, the conversation ends at the chat screen. With Incident Lake, if the analysis concludes that “this is an incident,” you can create a ticket on the spot; the generated report remains on the same screen as the incident, and the team can take immediate action via notifications in Slack or Teams.

In short, the fundamental difference is that AI is integrated into the entire incident response process—analysis → assessment → ticket creation → resolution → reporting. This ensures that the time and context that were previously lost every time you copied and pasted between tools are now preserved.