Release date: September 1, 2026
*This feature works only with Claude models.
File attachments (new feature)
You can now attach files to the AI chat and ask questions or make requests about their contents.
Supported formats: Excel (.xlsx) / Word (.docx) / PDF / CSV / Text (.txt, .md, .log)
- AI reads the contents of attached files and uses them for summaries, analysis, and answering questions.
- Once attached, a file is remembered throughout the conversation, so you don't need to reattach it for follow-up questions.
- You can attach up to 5 files per message, up to 10 MB per file.
Examples
Attach a CSV of incident reports and ask, "Summarize last month's incidents by type."
Attach a PDF runbook and ask, "Which steps in this procedure could be automated?"
File generation and export (new feature)
You can now receive chat answers as ready-to-use files.
- AI file generation: Ask something like "Put these results in Excel," and AI generates a file in Excel, Word, PDF, or CSV format. Save it right away with the download button shown in the answer.
- Answer export: We added a "Download as file" menu to each AI answer message. You can save any answer in Excel (.xlsx), Word (.docx), PDF, or CSV format.
Examples
"Put this month's incidents in a table and give it to me as an Excel file."
Download an incident analysis answer as a Word file and use it as the first draft of a report
Why this feature matters
The problem until now
Much of the information used in incident response and operations lives outside the chat.
CSVs exported from monitoring tools, incident report PDFs from vendors, configuration lists and on-call rosters managed in Excel, runbooks in Word, and so on. Until now, consulting the AI chat meant someone had to read through these files and copy and paste the key points.
This "pasting" had three problems.
- It takes effort: A CSV with hundreds of rows or a PDF with dozens of pages simply can't be pasted in full. Once a person has to pick and choose in advance, information is lost before it ever reaches the AI.
- Transcription errors happen: The more urgent the incident response, the more likely it is that something gets missed or rows get misaligned.
- It doesn't work in an emergency: In the middle of an incident, there's no time to "open the file, format it, and paste it." People end up not asking the AI at all, which kept usage from taking hold.
AI answers were stuck inside the chat
The same was true in the other direction. Even when AI ran an aggregation or analysis, the results stayed as text in the chat window. To turn them into a report for a manager, an explanation for a customer, or materials for a regular meeting, someone had to transcribe them into Excel or Word. If an analysis the AI produced in minutes takes tens of minutes to turn into a document, that defeats the purpose.
What this feature changes
"Hand over a file, get a file back" now happens entirely within a single chat.
- Attach a CSV from your monitoring tool as-is, ask "Summarize last month's incidents by type," download the results as an Excel file, and use them directly in your regular meeting materials.
- Attach a vendor's incident report PDF, ask "Summarize the impact on our systems," receive the summary as a Word file, and use it as the first draft of an internal report.
By eliminating the manual work before (transcription and formatting) and after (creating documents) using AI, "it's faster to ask the AI" actually becomes true.
Especially in time-critical situations like incident response, being able to start an analysis just by dropping in a raw file translates directly into a faster initial response.
Attached files also carry over throughout the conversation, so you can naturally keep digging deeper, such as "about the third column of this table" or "continuing from that log earlier," without reattaching anything. The goal of this feature is to let you use the chat not as a one-off Q&A tool, but as a place to work with AI around your files.
Why do this in Incident Lake instead of a general-purpose AI?
"If all you're doing is having AI read and analyze a file, couldn't you just use ChatGPT?" It's a fair question. But in the context of incident response, there are three gaps a general-purpose AI chat can't close.
1. Analysis that's connected to your organization's historical data
If you give a general-purpose AI a CSV of monitoring logs, all it knows is what's in that file. Incident Lake's chat runs in the same place as your past incidents, knowledge base, and service configuration.
When you have it analyze an attached log, it can go as far as saying, "There have been three past incidents with similar symptoms, all caused by DB connection pool exhaustion." The result is not an analysis of a file in isolation, but an analysis informed by your organization's response history, something only possible where the data lives.
2. Your incident data stays in-house
In Incident Lake, attached files are stored in self-managed storage isolated per tenant, and access permissions (RBAC) and auditing fall under the same controls as your existing incident data. Admins can also see usage at the tenant level. To balance convenience with governance, the right approach is to bring AI to where your business data already lives.
3. Analysis results lead straight to the next action
A general-purpose AI's answer is a dead end in the chat window. In Incident Lake, if the analysis shows "this is an incident," you can file it on the spot, generated reports stay on the same screen as the relevant incident, and your team can get moving right away through Slack or Teams notifications.
In other words, the essential difference is that AI is built into the entire incident response flow: analysis, decision, filing, response, and reporting. The time and context that used to be lost every time you copied and pasted between tools are preserved.