A finance or IT team asks Claude or ChatGPT a question about the company's own numbers, and the answer comes back generic — because the model has no way to reach the data that actually lives on-premises: in SAP, in internal databases, in systems that were never built to talk to an AI assistant.
This is the question behind that gap: how do you actually connect on-premises data to a model like Claude or ChatGPT, without exposing sensitive information or rebuilding your entire data architecture?
Why this isn't a simple plug-and-play problem
Claude and ChatGPT are language models — they don't have native access to a company's SAP instance, internal databases, or spreadsheets sitting on a local server. Connecting them isn't about giving the model "more data" in a chat window. It's about building a reliable path between systems that were designed independently of each other.
For a company with structured finance or operations teams, that path also has to satisfy conditions that a quick workaround can't: which data can the model see, how is access controlled, and can every answer be traced back to its source.
The three approaches companies typically try
Manual exports and file uploads. Someone downloads a report from SAP or an internal system and pastes or uploads it into the chat. It works for a one-off question, but it doesn't scale — it depends on a person doing that work every time, and the model only ever sees a static snapshot.
Custom-built integrations. An internal engineering team writes scripts or middleware to pull data from on-premises systems into a format the model can use. This gives more control, but it's slow to build and every change to the source system risks breaking the connection.
A connection layer built for this purpose, like MCP (Model Context Protocol). MCP is an open standard that lets a model like Claude call out to a company's own data as a tool, inside the same conversation — without the data ever needing to leave the company's existing systems to "train" or be copied into the model itself.
Where Teramot fits into this
Teramot connects Claude to a company's existing data sources — SAP, internal databases, spreadsheets — through MCP, integrating with the architecture the company already has instead of requiring it to be replaced. The model can query real, current data as part of a conversation, while the underlying systems and access controls stay where they are.
This is the same problem finance and control teams already run into with reporting, cash flow consolidation, or preparing information for SAP: the data exists, but converting it into something usable takes manual work. Teramot's role is to automate that connection, not to replace the systems the data already lives in.
What to check before connecting your data to an AI model
Before implementing any approach, it's worth asking: does this method require moving or duplicating sensitive data, or can the model query it in place? Who controls what the model can and cannot see? And is there a way to trace an answer back to the exact data it came from?
Analyzing your current systems and processes is a more useful starting point than assuming any single connection method will fit without adjustment.