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Automus has developed several AI-based products that solve problems almost every Client faces in managing their Enterprise Applications. FAiS is the backbone behind all Automus AI products.

FAiS Close

FAiS Close is an AI-based tool that helps Clients complete their month-end financial close process.  AI is leveraged throughout the tool to optimize the close process further.  As data is available in the sub-ledgers or the General Ledger, validation can be performed in advance using FAiS to reduce overload once the month-end cycle begins.  FAiS AI models are used to analyze data, identify issues earlier, and suggest solutions to mitigate them.

Dashboard interface displaying financial metrics, bar charts, and contract breakdowns, with options for data queries via a virtual assistant called Fais. Sections include key metrics and prompts for reports, risk evaluation, and forecasting.

Figure 1. A dashboard with custom widgets populated by KPIs defined by the client and Automus Financial Experts is accessible at any time during or after the close process.  Each widget includes an Ask FAiS button that triggers a prompt summarizing the widget’s data.  The user will also be able to create reports, tables, forecasts, and more from the Ask FAiS panel.

Dashboard showing progress of financial tasks for February 2025 across different accounting standards like US GAAP, MX GAAP, and IFRS. Includes status bars for accounts payable, accounts receivable, and cash management with overall completion percentages.

Figure 2. As the user enters FAiS close, they are presented with the state of progress for each ledger. Clicking a ledger row lets the user view the next section & step(s) that must be completed.  Users can also select previous close periods, view the dashboard, and ask FAiS a question.

A financial software interface showing a checklist for fixed assets. The left panel lists various steps like Mass Additions, Manual Additions, Adjustments, and more, each with a status indicator and related actions. The right panel features a chat window with "FaiS," a checklist assistant, offering help with financial data queries. The interface includes options for dependency data summary, risk evaluation, and trend analysis.

Figure 3. In each section (Accounts payable, Accounts Receivable, Cash Management, Fixed Assets), the user can view all checklist steps. The next step the user needs to work on will be highlighted, with buttons to get instructions. Mark the step as complete, then launch it.  The Ask FAiS panel has been triggered in this image, allowing the user to use custom canned prompts or enter a new query.

Automus Conversion Engine (ACE), Powered by FAiS

Legacy data conversion to the Cloud is a key risk in any ERP implementation and is often the source of delays. Despite this common knowledge, many projects encounter issues for similar reasons: 1) Data Complexity & Volume, 2) Data Quality, 3) System Differences & Data Compatibility, 4) Business Process Changes affecting data, 5) Testing and validation, and 6) Client Time constraints.

Classic risk mitigation tactics include:

  • Perform Data Cleansing Early – Identify and fix data quality issues before migration.

  • Define a Clear Data Mapping Strategy – Ensure alignment between old and new data structures.

  • Use Automated Migration Tools – Leverage ETL (Extract, Transform, Load) tools to streamline the conversion process.

  • Conduct Multiple Test Runs – Validate data integrity before final migration.

  • Engage Business Users – Involve domain experts to verify data accuracy and usability.

While known, these tactics can still be challenging to perform when much of the effort rests on the Client.

“Automus has changed how we think of data conversion
with their Conversion Engine!”

Flowchart depicting data processing stages: "Extract Data out of the source systems," "Cleanse / Hydrate Data," "Transform Data," "Load Data," and "Reconcile Data." Labels "Client" and "Automus" indicate involvement in each stage.

Figure 4. There are five main steps in the conversion process, with the Client responsible for 4 of them. This is the industry standard.

Leveraging ACE changes the conversion approach, with Automus doing more work and the Clients’ tasks becoming more simplified.

  1. Client extractions are simplified as they no longer need to transform the data.

  2. Automus takes over the transformation responsibilities, reducing overall work and risk for the Client.

  3. The FAiS data cleansing tool leverages AI to point the Client to the areas of the data that need cleaning.

  4. Automus loads the data in accordance with industry standards; however, it leverages pre-validation rules to ensure the data aligns with Oracle requirements before loading, significantly reducing loading complexity.

  5. The reconciliation process is simplified because the ACE tool provides reconciliation reports in a consolidated, easy-to-view format, rather than sifting through siloed reports.

The Ask Fais ChatBot for ACE will make it easier to interact with the tool, as the bot allows users to ask questions about conversion outcomes using natural language, such as “What is the status of the customer conversion?”

Data processing workflow diagram with five stages: Extract, Cleanse/Hydrate, Transform, Load, Reconcile, labeled by roles: Joint, Client, Automus, Joint.

Figure 6: While using ACE, there is a significant shift in ownership and complexity for our Clients, thereby reducing the risk of data conversion errors and resulting delays.

Automus Integration Hub (AIH), Powered by FAiS

Enterprise Applications invariably have a complex set of integrations with 3rd party systems.  While the Oracle OIC console can monitor jobs at a macro level to view status, AIH provides updates and status during the integration process. With a single pane of glass, all integrations can be evaluated after execution and during execution with meaningful details. Messages are provided as each code section is completed, including Extraction, Transformation, Inbound Submission, Call Back, or File Server interaction.  Users can now ask FAiS, “What is the status of this integration?” and understand if there are any issues. 

Dashboard displaying integration data with three sections: a status summary on the left showing integration counts, a pie chart in the center illustrating integration run percentages, and a bar chart on the right showing integration process status.

Figure 7: AIH Dashboard Landing Page: Provides an intuitive dashboard and menu to navigate each feature and to summarize overall integration health.

Screenshot of a web application interface displaying "Interface Details" with a list of interface codes, names, source systems, and target systems. The sidebar menu includes options like Dashboard, Interface, Register, Run Logs, EDI, Manage Integrations, and Data Sync.

Figure 8: Integration Registration: Configure the integrations in AIH and enable or disable logging.

 With AI capabilities being released, the AIH dashboard will soon be able to leverage the FAiS framework to identify code issues, remediate them, and re-run the integration.  This fundamentally transforms how integrations will be monitored and fixed as we move into a significant shift in approach, all powered by FAiS.