Growing companies tend to think they have a communication problem, a documentation problem, or a productivity problem. Sometimes they do. But often there is another issue sitting underneath all of it.
They have a knowledge problem.
Not because the information does not exist. Usually it does. The problem is that it is buried across drives, docs, systems, old tickets, chat threads, wikis, meeting notes, and tools no one thinks to check until it is too late. Useful context gets scattered. Past decisions disappear into folders. Teams recreate work that has already been done. People ask the same questions over and over because finding the answer takes longer than asking again.
That is one of the quieter problems inside growing companies. It builds slowly. A few more people join. More tools get added. More systems get layered in. More files get created. More internal knowledge gets trapped in places that make perfect sense to the person who put it there and almost no sense to everyone else.
Earlier this year, our company announced that we would be moving from Google Workspace to Microsoft. It was a decision made behind closed doors, with no employee input, and it created an immediate challenge. Years of files, notes, systems, and even automations had been living inside Google, and suddenly an entire organization was expected to shift into a new environment that many people had never used before.
It was a steep learning curve. For months, people were trying to figure out where things had gone, which folder held the right file, and whether the document they found was even the latest version. That creates a much bigger problem than people realize. Once information starts moving without clear structure, date history, and version confidence, it becomes much harder to know what is current, what is accurate, and what can actually be trusted.
That is the kind of moment where a company realizes it does not just have a platform migration on its hands. It has a knowledge problem.
At some point, the company starts paying for this in ways that do not always look obvious at first.
It shows up in wasted time. In repeated work. In slower onboarding. In support teams struggling to find the right answer. In engineers rebuilding something that already exists. In leaders making decisions without the full picture because the full picture is spread across too many places.
That is why tools like Curiosity are interesting.
What Curiosity is really speaking to is not just search. It is the fact that enterprise knowledge is fragmented. On its site, the company describes its platform as a way to link information across systems into a knowledge graph and make it usable with AI. It also emphasizes hybrid search, AI assistants, and enterprise-scale retrieval across files, systems, and custom applications.
That matters because most companies do not have a lack of information. They have a lack of access to usable information.
And those are not the same thing.
A shared drive full of documents is not the same as a connected knowledge layer. A pile of tickets is not the same as insight. A wiki that no one trusts is not the same as institutional memory. If your teams cannot find, reuse, and trust what already exists, the company keeps burning time rediscovering itself.
That is where the knowledge problem becomes expensive.
Curiosity’s pitch is essentially that scattered enterprise data can be connected and retrieved in a more intelligent way. Its product pages describe “one search for everything,” support for over 120 file types, keyword plus semantic search, and a knowledge graph meant to ground AI in actual company data.
That is a much more useful way to think about AI inside a company.
Not as a machine that magically generates answers from nowhere, but as something that becomes more valuable when it can actually access the right context.
Because AI is only as helpful as the information it can reach.
If company knowledge is trapped in silos, out of date, permissions-sensitive, or spread across too many tools, then people end up doing what they have always done: asking around, guessing, recreating, or moving on without the full answer. Curiosity leans heavily into this on the enterprise side, with messaging around permissions-aware access, governance, auditability, and deployment in the customer’s environment.
That is especially relevant for growing companies because growth tends to multiply fragmentation.
A small team can get away with tribal knowledge for a while. A few people know where everything lives. They know which Slack thread mattered, which document has the real answer, which person to ask, which workaround still applies. But growth changes that. New hires do not have that context. Cross-functional teams do not share the same habits. The organization becomes more distributed, more specialized, and more tool-heavy.
What used to live in someone’s head now needs to live somewhere the business can actually use.
And that is where companies often lag behind.
They keep investing in more tools, more systems, more content, more documentation, and more AI experiments without addressing the more basic issue: can anyone actually find what already exists?
That is what makes this such an important problem.
Curiosity’s engineering use case makes this concrete. It says the platform helps engineers find technical information, understand design decisions, and reuse past work so they can move faster and avoid repeated effort. That same pattern applies far beyond engineering. Support teams need it. Operations teams need it. Sales teams need it. Leadership needs it. The specifics change, but the friction is often the same.
The company already knows more than it can easily access.
That is the bottleneck.
And this is where I think a lot of businesses misunderstand the AI opportunity. They assume the value is in generating more. More content. More summaries. More outputs. More automation. But one of the smartest uses of AI may be helping the business retrieve, connect, and reuse what it already knows.
That is a very different kind of leverage.
Instead of asking, “What else can AI create for us?” a better question might be, “Why is it still so hard for our teams to find and trust the knowledge we already have?”
That is the knowledge problem inside growing companies.
And solving it may be one of the most practical shortcuts available.
Want more practical shortcuts like this?
Explore my curated library of AI tools, prompts, and workflows at resources.taneilcurrie.com