Finance teams have no shortage of data. The challenge is turning that data into timely answers, useful analysis, and informed action, without exporting reports, stitching together spreadsheets, or granting an AI tool broad, ungoverned access to the general ledger.
That is the purpose of the Sage Intacct AI Gateway; a secure, standards-based connection layer that lets organizations connect Sage Intacct data to external AI applications and large language models (LLMs).
Rather than replacing Sage Intacct or Sage Copilot, AI Gateway expands what finance and technology teams can build around the ERP. It gives them a way to bring governed Sage Intacct data into the AI experience, workflow, and application of their choice.
Sage Intacct AI Gateway is a secure bridge between Sage Intacct and external AI tools. It provides developer-friendly access through two complementary methods:
In practical terms, the Gateway lets an AI application retrieve relevant, permissioned financial context from Sage Intacct before it responds to a question, produces an analysis, or participates in a workflow.
Sage describes MCP as similar to a universal connection port; one standard that allows compatible AI tools to securely connect with the organization’s financial data, rather than requiring a separate, bespoke integration for every model or application. The Gateway is designed to preserve role-based permissions, governance, and auditability as organizations experiment with AI.
The Gateway does not “train” an LLM on a company’s Sage Intacct data. Instead, it provides a controlled way for an LLM-powered application to request relevant information when it needs it.
Here’s how a typical interaction works:
The MCP Server provides read-only access, while Sage Intacct’s REST APIs can support full access for applications designed and governed to use them.
This distinction matters. An MCP-connected assistant can help a finance team ask questions of live data and receive concise answers without altering financial records. More advanced, custom applications can use REST APIs to support controlled automation where appropriate.
Sage Intacct AI Gateway is intended to support your preferred LLM or AI application, provided it can connect through MCP or is incorporated into a custom application using the REST APIs. That means the Gateway is not positioned as a single-model integration. It is an open connectivity layer.
Organizations may therefore use it with:
The key point, Sage Intacct AI Gateway is model-flexible, not model-exclusive. Claude is a specifically cited current connection option, but the MCP and REST-based architecture is designed for broader compatibility as the AI ecosystem evolves.
Access should always be defined by permissions and the integration method selected. Currently, read-only AI Gateway access is focused on the following Sage Intacct modules:
For finance leaders, this makes the Gateway particularly well suited to analysis, reporting, planning support, exception identification, and narrative generation. It creates a safer starting point for AI adoption: the model can help people understand financial information without independently changing it.
Once connected, an LLM can make Sage Intacct data easier to explore, interpret, and communicate. Here are several practical examples.
Instead of navigating to a report, applying filters, exporting data, and interpreting the output manually, a finance leader could ask: “Summarize revenue, gross margin, and operating expense performance for the current month compared with budget and the prior month. Highlight the three largest drivers of variance.”
The LLM can retrieve authorized data, organize the findings, and produce a finance-ready narrative. The result should still be validated, but the time required to move from question to first draft can be dramatically reduced.
Month-end close is a high-value use case because it combines repetitive information gathering with the need for clear communication.
For example, a controller could ask: “Identify unusual changes in operating expenses compared with the previous three months, grouped by department. Draft follow-up questions for the budget owners.”
The connected LLM could:
The LLM does not replace the close process or accounting judgment. It reduces the manual effort involved in finding the issues that deserve attention.
Accounts receivable teams can use a connected assistant to identify collection priorities and prepare customer communications.
Example prompt: “Show customers with invoices more than 30 days overdue, prioritize them by outstanding balance, and draft a professional payment reminder for each customer.”
The LLM could help produce:
This approach keeps finance users in control while reducing the time spent compiling lists and writing repetitive emails.
For accounts payable, an LLM can help finance teams understand upcoming obligations and investigate exceptions.
Example prompt: “What payments are due in the next 14 days? Separate critical vendors from noncritical vendors, flag unusually large bills, and summarize potential cash-flow pressure.”
Potential outputs include:
With the right workflow design, the same assistant could also prepare a payment-review packet, while actual payment decisions and approvals remain under established controls.
Cash forecasting often requires teams to reconcile payables, receivables, and known commitments.
Example prompt: “Based on open receivables, upcoming payables, and cash balances, explain the major factors that could affect cash over the next 30 days. Provide an optimistic, expected, and conservative view.”
A connected LLM can turn raw financial information into an executive-friendly explanation. It can also help finance leaders rapidly answer follow-up questions such as:
Finance teams routinely translate system data into management and board-level narratives. An LLM can speed up that translation.
Example prompt: “Draft a one-page CFO commentary on this month’s performance. Include revenue, expenses, cash, receivables aging, and the most important risks and actions.”
The model can produce an initial management narrative, leaving the CFO or controller to validate the numbers, adjust tone, and add business context.
This is especially useful when leaders need multiple versions of the same story:
Purchasing data can become more actionable when it is accessible conversationally.
Example prompt: “Which vendors have had the largest increase in purchasing spend this quarter? Identify possible duplicate or fragmented purchasing patterns and summarize opportunities to consolidate spend.”
The LLM can support procurement analysis by turning transaction-level detail into questions and findings that the purchasing team can investigate.
For organizations using Order Entry, a connected LLM can help commercial and finance teams understand customer activity.
Example prompt: “Which customers have the largest open orders, and which orders may be at risk because of overdue receivables or unusual order patterns?”
This kind of insight can help connect finance data to operational decisions, enabling earlier coordination between accounting, sales, and operations.
The Gateway becomes even more valuable when Sage Intacct data is combined with other approved systems.
For example, a custom AI agent could combine:
A user could then ask: “Which projects are trending toward margin pressure, and what actions should the project managers take this week?”
The agent could bring together financial actuals, project status, customer context, and operational milestones, provided each data source is connected and properly governed.
It is important to distinguish Sage Copilot from Sage Intacct AI Gateway.
In short: Copilot provides built-in intelligence inside Sage Intacct, while AI Gateway gives organizations the flexibility to create their own AI-enabled experiences around Intacct data.
The most meaningful promise of Sage Intacct AI Gateway is not simply that finance teams can “chat with their ERP.” It is that organizations can connect trusted financial data to the rapidly expanding LLM ecosystem while retaining control over permissions, access, and workflow design.
The right initial use cases are usually read-focused and human-reviewed:
As confidence, governance, and integration maturity grow, organizations can extend those foundations into more sophisticated custom agents and workflow automations through Sage Intacct’s REST APIs.
For finance leaders, the opportunity is clear. Move beyond static reports and disconnected AI experiments toward a governed model where LLMs help teams turn live financial data into faster, more useful decisions.
Getting there takes more than flipping a switch. It means choosing the right use cases, setting up permissions correctly, and designing workflows that keep people accountable while AI does the heavy lifting. That's where Blytheco comes in.
As a Sage partner with deep Intacct implementation experience, Blytheco can help you evaluate whether AI Gateway is the right fit, plan a rollout that starts with low-risk, high-value use cases, and connect the right LLMs and applications to your financial data securely.
Whether you're exploring your first AI-powered finance workflow or ready to build custom agents on top of Sage Intacct's REST APIs, our team can guide you from strategy to execution.
Contact Blytheco today to start the conversation about bringing governed AI to your finance function.
Sage Intacct Solutions Engineer
Todd has over two decades of experience in marketing, sales, presales, and professional services and held roles as Director of IT and CFO. Throughout his career, he successfully established and developed nine practices from the ground up. This experience equipped him with a proven track record of implementing and managing various accounting and ancillary software solutions, including Sage Intacct. He leverages his vast business and software background to lead companies to successful and efficient solutions.
