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      <a href="/blog" className="tm-article-back">← Blog</a>
      <h1 className="tm-article-title">How Do I Connect On-Premises Data to Claude or ChatGPT?</h1>
      <p className="tm-article-titlesub">Three approaches, and what to check before connecting your data</p>
      <div className="tm-article-byline">
        <span className="tm-article-author">By the Teramot team</span>
        <span className="tm-article-date">November 2026</span>
      </div>
    </header>

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      <p>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.</p>
      <p>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?</p>

      <h2>Why this isn't a simple plug-and-play problem</h2>
      <p>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.</p>
      <p>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.</p>

      <h2>The three approaches companies typically try</h2>
      <p><strong>Manual exports and file uploads.</strong> 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.</p>
      <p><strong>Custom-built integrations.</strong> 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.</p>
      <p><strong>A connection layer built for this purpose, like MCP (Model Context Protocol).</strong> 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.</p>

      <h2>Where Teramot fits into this</h2>
      <p>Teramot connects Claude to a company's existing data sources — <a href="/integraciones/sap">SAP</a>, internal databases, spreadsheets — through <a href="/plataforma/mcp">MCP</a>, 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.</p>
      <p>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.</p>

      <h2>What to check before connecting your data to an AI model</h2>
      <p>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?</p>
      <p>Analyzing your current systems and processes is a more useful starting point than assuming any single connection method will fit without adjustment.</p>
    </div>

    <footer className="tm-article-footer">
      <p><em>Teramot is the AI Data Platform that runs in your infrastructure: it connects to your systems and delivers finished work, with every number traceable to its source. Learn more at <a href="https://teramot.com">teramot.com</a>.</em></p>
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      headline="How Do I Connect On-Premises Data to Claude or ChatGPT?"
      description="Three ways to connect on-premises data like SAP and internal databases to Claude or ChatGPT, and what to check on access control and traceability first."
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      datePublished="2026-11"
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