AI Agent

Transaction Reconciliation AI Agent

Close the books faster without the reconciliation headaches

"Why doesn't this balance?" — the question your team will stop asking. Automated matching, variance investigation, and exception resolution.

Benefits

End Month-End Reconciliation Chaos

Reconciliation delays the close. Manual matching is tedious and error-prone. Unresolved variances carry forward. The Transaction Reconciliation Agent matches transactions and investigates exceptions automatically.

3

Days faster close

95%

Auto-match rate

Zero

Carryover variances

Agentic Flows

Complete Reconciliation Automation

Automated reconciliation workflow: multi-source data import, rule-based and ML-enhanced matching, variance threshold analysis, exception categorization and auto-resolution, preparer and reviewer workflow support, and audit documentation.

How it works

Intelligent Transaction Matching

The agent imports data from multiple sources, applies configurable matching rules, investigates common exception types automatically, and routes complex items to your team with full context.

Self-learning agents

The agent improves with every task, adapting to outcomes, applying feedback, and self-correcting using Constitutional AI.

Upto 98% accuracy

As a result of constant feedback loops, Beam AI Agents refine their approach with every cycle, leading to 98% accuracy across flows.

Smart model switching

We call it ModelMesh. Each agent selects the right model for the task, balancing speed, accuracy, and cost in real time.

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Self-learning agents

The agent improves with every task, adapting to outcomes, applying feedback, and self-correcting using Constitutional AI.

Upto 98% accuracy

As a result of constant feedback loops, Beam AI Agents refine their approach with every cycle, leading to 98% accuracy across flows.

Smart model switching

We call it ModelMesh. Each agent selects the right model for the task, balancing speed, accuracy, and cost in real time.

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Integrations

Connects to Your Financial Systems

Direct integration with SAP, Oracle, NetSuite, and BlackLine. Bank feed connections through Plaid and Yodlee. Imports from any source via Excel, CSV, or API including sub-ledgers and external systems.

Implementation

Reconciling Faster in 10 Days

Connect your data sources and configure matching rules. Set exception thresholds and routing logic. Most teams run their first automated reconciliation within two weeks of kickoff.

Step 1

Agent discovery

Conduct workshop(s) with relevant stakeholders to identify and prioritize use cases and map the requirements

Step 1

Agent discovery

Conduct workshop(s) with relevant stakeholders to identify and prioritize use cases and map the requirements

Step 2

Agent setup

Develop and launch your first agent with basic logic and integration (process scoping and recording, test dataset of 30-50 examples, baseline agent running and testing with target output mapping)

Step 2

Agent setup

Develop and launch your first agent with basic logic and integration (process scoping and recording, test dataset of 30-50 examples, baseline agent running and testing with target output mapping)

Step 3

Agent training

Test performance and gather feedback from process users (agent optimization up to 80% against expected output, variable-level accuracy measurement and optimization, integrations setup, and feedback API interfacing)

Step 3

Agent training

Test performance and gather feedback from process users (agent optimization up to 80% against expected output, variable-level accuracy measurement and optimization, integrations setup, and feedback API interfacing)

Step 4

Agent live

Extend to more workflows and client teams (live and continuous monitoring, human-in-the loop interfacing, node auto-tuning for automated prompt enhancements, >90% accuracy improvement, weekly business logic improvements)

Step 4

Agent live

Extend to more workflows and client teams (live and continuous monitoring, human-in-the loop interfacing, node auto-tuning for automated prompt enhancements, >90% accuracy improvement, weekly business logic improvements)

Key Features

Smart Reconciliation Tools

Flexible Matching Rules

Configure one-to-one, one-to-many, and many-to-many matching. Set tolerances by account type and materiality.

Auto-Resolution Engine

Learns common exception causes and resolves automatically. Timing differences, rounding, and known adjustments.

Variance Investigation

Traces unmatched items to root cause. Suggests resolution actions based on historical patterns.

Demo

See Reconciliation in Action

Watch the agent process a month-end bank reconciliation, matching transactions, identifying timing differences, resolving known exceptions, and routing true variances for review.