It provides practical, reliable intelligence that enhances, not replace, the work of finance teams. AI validates evidence, highlights risks and improves accuracy early in the process, ensuring that issues are caught before month end pressure sets in.
AI evaluates supporting documents, reviews comments, identifies anomalies in ERP data and provides insight into reconciliation quality. These capabilities reduce rework, strengthen compliance and allow preparers and approvers to focus their time where it matters most.
“Aico doesn’t overpromise or underdeliver.”
AI evaluates attachments, comments and journal details, reducing errors and improving auditability before posting.
AI rates the quality of supporting evidence and highlights exceptions, helping teams focus on the items that truly need attention.
Use AI to check ERP reports, detect anomalies and enhance control activities at key workflow points.
With live ERP integration, AI runs on accurate, up to date data. Validations, checks and reports reflect your true system status instantly
AI reviews attachments, raw data and comments, then cross checks ERP information to confirm completeness, relevance and accuracy.
Set AI instructions for specific workflow steps—from journal creation to reconciliation review—to reduce manual effort and ensure consistency.
Define when AI runs, what data it reviews and how results are used. Configure rules by task type, threshold or accounting period
Yes, Aico has designed AI as core infrastructure, not a standalone feature.
AI is embedded directly into workflows and activated through configurable agent instructions, allowing it to be deployed consistently across reconciliation, close, and journal processes. By returning structured, auditable results, AI strengthens controls, accelerates execution, and supports automation while keeping accounting judgment firmly with the customer.
First example: AI evaluates whether supporting documentation and reconciliation data constitute suitable evidence.
How it works:
Aico sends reconciliation headers, rows, and linked reports to Azure OpenAI.
The AI rates the quality of evidence (e.g. 1–10), considering factors such as unreconciled balance and relevance of attachments.
The score is written back to a header and used by auto-reconciliation rules.
Business value:
Enables auto-reconciliation when evidence quality exceeds a defined threshold.
Reduces manual review effort while maintaining audit discipline.
Second example: AI-Driven Task Auto-Completion in Close (Closing Tasks)
AI checks ERP or file-based reports for specific required content and automatically completes close tasks when conditions are met.
How it works:
AI is instructed to search report attachments for defined criteria (e.g. confirmation that a provision has been posted for a given period).
The response is structured as Yes/No.
A Boolean result is copied into the “mark as completed” header to auto-complete the task.
Business value:
Speeds up the financial close.
Ensures consistency in close checks without relying on manual inspection.
Third example: Review Quality Check on Approver Comments (Journals)
AI checks whether approvers have meaningfully reviewed supporting evidence.
How it works:
Only header-level comments are sent to the AI.
AI confirms whether comments explicitly state that evidence was reviewed and deemed appropriate.
Returns a Yes/No result used in workflow validation.
Business value:
Strengthens governance and control over approvals.
Ensures approvals are substantive, not just procedural.
AI is embedded as part of Aico’s core workflow infrastructure. It runs through configurable automation tasks on existing templates, rather than as a standalone feature or bolt-on.
AI enforces consistent evidence standards, validates approvals, and documents its assessments in the audit trail. This improves control consistency, reduces manual review, and supports SOX and audit requirements.
Yes. Customers define exactly what data, attachments, and comments are sent to the AI, as well as the instructions and response format. Outputs are structured so they can be used reliably in validations and automation.
No. AI does not post entries or make accounting judgments. It evaluates evidence, comments, and documents against rules and instructions defined by the customer and returns structured results (scores or Yes/No) that drive workflow controls.
AI results can be reviewed, validated, or overridden based on customer-defined rules. Overrides are visible and reportable, ensuring accountability and transparency rather than blind automation.
Yes. Aico uses Microsoft Azure OpenAI and Azure AI Document Intelligence within a secure enterprise cloud environment. Customer data is not used to train public AI models and remains within controlled Azure services.
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