Find everything.Send only what matters.Act on every copy.
Connecting AI to your files was never the hard part. Finding what matters inside them is where it gets tricky. Finding every copy of it across an entire enterprise is where it gets really hard. And taking one consistent remediation action against every file that contains that content is where it gets interesting.
Octosight is how you find, analyze, and protect large volumes of sensitive data with AI.
Spend tokens on the analysis, not on the search.
One search, everywhere your files are.
Search by keyword, topic, or pattern across SharePoint, Google Drive, Gmail, Outlook, and the files on your laptop at once. Every one of those searches differently. You write one search. Octosight builds its own indexes rather than using the search each platform comes with. Several of them, built for keyword, topic, pattern, and for agents to work with, so nothing is sampled and nothing is quietly left out.
Every result opens in a full preview pane. You read the file itself, with your search terms highlighted, so you can see exactly why it came back. Tag the ones that matter. Send those files to the model to fill in the diligence questionnaire, the risk assessment, the analysis. Ask questions of them. Foreign-language files come back in English. Sweep them for personal, financial, and health data.
You found the files. Now what?
Because Octosight connects to your files where they live, it knows where every copy of every file is, and every file that contains the targeted content. Take an action on one, and it takes the same action on all of them. Move it to the buyer, to the seller, or somewhere secure. Preserve it for legal hold. Or delete it.
Then prove it. The audit trail shows which action ran, on which files, and what the result was. Deterministic and repeatable, not a model that has to choose correctly twice. That is the answer a deal counterparty, a legal team, or a regulator will accept.
Eight years of hands-on delivery in regulated environments.
Anyone can build a working demo in a weekend now. Octosight’s approach was built the slow way, on work that got examined after it was delivered. We presented one of those systems live to the United States Department of the Treasury.
The approach did not exist before our team built it in the field, where it delivered sensitive data remediation across billions of files and stood up to government, deal counterparty, and legal scrutiny. That team left their jobs to form Octosight together, and the platform has since run production workloads for deals teams doing diligence, tax teams working through restructurings, and legal discovery for litigation trial prep.
We built it, then took it down.
For years we did this work with whatever parts we could get our hands on, assembled differently for every engagement. Octosight is the system we needed the whole time and never had: built from the ground up, for the cloud and for working with AI, doing the whole job in one place. It is built, it has run in production, and it can do that work today.
We took it down anyway. Two things about how it gets delivered no longer match how the work happens, and in regulated work you do not bolt those on and hope.
- Our own interface. Every feature of the platform is built into a screen we designed. But a team running its day inside Claude or ChatGPT should never have to open it. Octosight already runs as an MCP server, so you can call it from the Claude and ChatGPT desktop apps, search everything you have connected, and get results back without leaving the session. It reaches out to other MCP servers too, so EDGAR market filings and arXiv research papers arrive the same way. Search works that way today. The rest of the interface does not, and delivering it as a headless skill or tool call is a challenge we are still working on.
- Hosting. Octosight is a cloud-native platform, and we architected it to run inside a company, so the company keeps control of their own sensitive data. That requirement has not changed. What changed is who is left to run it. Fortune 500 companies used to keep teams of people who maintained their on-premise data centers and cloud environments. Those data centers moved to the cloud, companies are trying to maintain what remains of them with AI, and people are being let go. There is often nobody left on the company side to make the changes our deployment needs. They still want to hold their own data. They no longer want to run the environment we require them to run to hold it.
Four questions we have not answered yet
- Everything our interface does, without our interface. We built every feature of the platform into a screen we designed. A team working inside Claude or ChatGPT should never have to open it. Search already reaches them there. How do we deliver all of it there rather than a slice?
- Fewer tools, and the spend already committed. Enterprises are converging on a short list: Microsoft and Google for the workspace, Anthropic and OpenAI for the models, AWS, GCP and Azure underneath, most of it on contracts that lock in what they will spend. The money is going to a few very large players. Teams still have to be able to search their own data. Where does a tool like ours fit?
- The company owns all of it, and we keep it running. The data, the cloud environment that holds it, the indexes we maintain inside that environment, and the model contracts would all sit with the company, with us operating the platform on top. Companies are seeking efficiencies, which often means letting go the people we would depend on to maintain what we deploy. We can deliver a better solution when we control how our models behave, but an enterprise only gets credit against its committed spend if the data passes through models it controls. Where the line falls is the open question.
- The work moves faster than procurement. A deal moves in weeks. A national security agreement (NSA) typically leaves ninety days to complete the data risk mitigation work. Procurement for an AI system already runs longer than for one that is not, and where the data is regulated those reviews are getting longer rather than shorter, for good reason. The two timelines are moving apart, and that makes us hardest to buy for exactly the teams who need us most. The question is whether being built deeply into the model frameworks a company has already approved is itself a way to clear that review.
Get in touch.
If any of this sounds familiar, we would love to talk.