Automous has developed several AI-based products that solve problems almost every Client faces in managing their Enterprise Applications.
FAiS Close
FAiS Close is an LLM-based chatbot with pre-trained prompts that help Clients complete their month-end financial close process. As data is available, validation can be performed in advance to reduce the overload once the month-end cycle begins. AI models are used to analyze data and identify issues earlier and suggest solutions to mitigate.
Figure 1. A dashboard with custom widgets populated by KPI’s that are defined by the client and Automus Financial Experts, is accessible at any time during or after the close process. Each widget has an Ask FAiS button that triggers a prompt that will summarize the widget’s data. The user will also be able to create reports, tables, forecasts, and more from the Ask FAiS panel.
Figure 2. As the user enters FAiSclose, they are presented with the state of progress for each ledger. Clicking on a ledger row will let 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.
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 and launch the step. 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)
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 conversion.
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 the their Conversion Engine!”
Figure 4. There are 5 main steps in the conversion process with the Client being responsible for 4 out of the 5 areas. This is the standard approach in the industry.
Leveraging ACE changes the conversion approach, with Automus doing more work and the Clients’ tasks becoming more simplified.
Client extractions are simplified as they no longer need to transform the data.
Automus takes over the transformation responsibilities, reducing overall work and risk for the Client.
Automus loads the data as per industry standards.
The reconciliation process is simplified because the ACE tool provides reconciliation reports in a consolidated and easy-to-view manner, rather than sifting through various siloed reports.
The soon-to-be-released AskFais ChatBot for ACE will make it easier to interact with the tool. The bot allows users to ask questions about conversion outcomes using natural language.
Figure 6: While using ACE there is a significant shift in ownership and complexity for our Clients, thereby reducing risk of data conversion errors and resulting delays.
Automus Integration Hub (AIH)
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 see status, AIH is providing updates and status while the integration process is running. 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.
Figure 7: AIH Dashboard Landing Page: Provides an intuitive dashboard and menu to navigate each feature and provide a summary of overall integration health.
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 identify the issue in the code, remediate it and re-run the integration. This fundamentally transforms how integrations will be monitored and fixed as we move into a significant shift in approach.