Agentic AI

AI Agents That Work for You

Meet the autonomous agents that continuously analyze your NetSuite data, surface insights, and take action—all while maintaining complete transparency and control.

Specialized Agents

Your Autonomous Finance Team

Six purpose-built agents working 24/7 to analyze your NetSuite data, detect anomalies, forecast performance, and answer questions—all while maintaining complete transparency and control.

Financial Close Agent

Automates close workflow orchestration and anomaly detection

Monitors 10,000+ transactions per close cycle
Flags anomalies with 92% precision rate
Reduces close time by average 40%
Reconciles intercompany balances across all subsidiaries automatically
Generates close checklist with real-time task completion tracking
Validates revenue recognition compliance with ASC 606 rules

EXAMPLE AUTOMATION

Automatically validates intercompany eliminations across 5 subsidiaries, flags $47K variance in Q4 revenue recognition, and alerts controller 3 days before close deadline.

Variance Analysis

Explains budget vs. actuals discrepancies with root cause analysis

Analyzes P&L line-by-line
Generates natural language commentary
Drills down to transaction detail

Cash Flow Forecasting

Predicts 13-week rolling cash position with ML models

93% forecast accuracy
Predicts late payments 85% accurately
Updates daily with AR/AP changes

AP Automation

Validates vendor invoices and flags duplicate payments

Three-way match automation
Duplicate invoice detection
Policy compliance verification

Natural Language Query

Translates questions into NetSuite SuiteQL and visualizations

Understands finance terminology
Handles complex multi-table joins
Auto-generates visualizations
How Agents Work

From Data Connection to
Continuous Intelligence

Each agent follows a sophisticated workflow: contextual understanding, continuous monitoring, pattern recognition, and intelligent action—all with human oversight and approval.

01

Schema Discovery

Agent analyzes your NetSuite data model, custom fields, and relationships

02

Context Building

Historical analysis to understand normal patterns, seasonality, and baselines

03

Real-Time Monitoring

Continuous processing of new transactions using NLP and ML models

04

Pattern Recognition

Statistical analysis to detect anomalies, trends, and forecast deviations

05

Insight Delivery

Alerts, dashboards, and natural language summaries with full audit trails

Technical Architecture

How the AI Works

Enterprise-grade NLP, machine learning models, and explainable decision logic—all designed for finance domain expertise.

Our NLP engine uses fine-tuned large language models (GPT-4 and domain-specific models) to understand finance terminology, NetSuite object relationships, and complex multi-step queries.

Example Query Processing:

"Show me AR aging for customers over 60 days where balance exceeds $50K"
Parsed into: SuiteQL query joining Customer + Transaction + AR Aging tables
Filters: aging_bucket = '61-90' OR 'Over 90', amount > 50000
Returns: Interactive table + aging chart, executed in 1.2 seconds
Context Window
128K tokens
Full conversation history retained
Query Success Rate
94%
First-attempt query accuracy

We use ensemble ML techniques combining XGBoost, ARIMA, and neural networks to generate forecasts. Models are trained on your historical data and continuously retrained as new actuals arrive.

Revenue Forecasting Model Architecture:

Feature Engineering

70+ features: seasonality indicators, customer cohort metrics, product category trends, historical growth rates, external economic indicators

Ensemble Methods

Weighted average of XGBoost (60%), ARIMA (25%), LSTM neural network (15%) — weights adjusted based on recent performance

Continuous Learning

Models retrain nightly with rolling 24-month window. Hyperparameters optimized via Bayesian optimization. A/B testing between model versions.

Training Data
24 months
Rolling window
Forecast Horizon
13 weeks
Rolling cash flow
Confidence Intervals
80% / 95%
Prediction bands

Anomaly detection combines statistical methods (Isolation Forest, Z-score analysis) with rules-based business logic to flag outliers while minimizing false positives.

Multi-Layer Anomaly Detection:

1
Statistical Outlier Detection

Isolation Forest algorithm identifies transactions 3+ standard deviations from historical norms for account/vendor/amount combinations

2
Business Rules Engine

Configurable rules: duplicate invoices (fuzzy matching), vendor policy violations, approval threshold breaches, inter-subsidiary imbalances

3
Contextual Validation

Agent checks if outlier is expected (e.g., new product launch, seasonal spike) by analyzing memo fields, customer notes, prior approvals

4
Severity Scoring & Routing

Each anomaly assigned severity (1-10) based on financial impact, confidence level, account materiality. Critical alerts (8+) escalate immediately.

Detection Precision
92%
True anomalies / total flagged
Average Detection Time
4.7 hours
From transaction to alert

All NetSuite access is read-only via OAuth 2.0 authenticated RESTlets. Agents respect role-based permissions and never modify your data without explicit approval.

Access Control Architecture:

Authentication Layer
  • OAuth 2.0 token-based authentication (tokens refresh every 60 minutes)
  • Dedicated integration user with read-only role assignment
  • IP allowlisting restricts access to NSGPT infrastructure only
  • TLS 1.3 encryption for all API calls
Permission Inheritance
  • Agents query NetSuite using credentials scoped to user role permissions
  • If user cannot see Subsidiary B in NetSuite, agent also cannot access it
  • Custom record permissions, field-level security, and record restrictions honored
  • Audit trail includes both agent action and underlying NetSuite user
Query Patterns
  • SuiteQL for complex analytical queries (90% of agent queries)
  • RESTlet custom endpoints for saved search execution
  • SuiteTalk SOAP for metadata discovery and schema mapping
  • Rate limiting: 50 queries/second per customer, auto-throttling during peak hours

Zero Data Replication: Your NetSuite data never leaves your instance. Agents query in real-time and cache only aggregated summary statistics (e.g., monthly totals) for performance optimization.

See how the Natural Language Query Agent handles a complex multi-step financial analysis request.

User (CFO)

"Show me Q4 gross margin by product category, compare to Q3, and flag any categories where margin dropped more than 5 points"

Agent (Processing...)
Parsed intent: Gross margin analysis with time comparison + threshold alerting
Identified tables: Transaction (Sales Orders), Item (Product Category), Accounting Period
Generated SuiteQL with revenue - COGS calculation, grouped by category, filtered Q3/Q4 2024
Executed query (1,247 transactions processed in 0.8 seconds)
Agent Response

I analyzed Q4 gross margin across 8 product categories. Here's what I found:

Electronics: 42.3% (Q3: 44.1%) — ⚠️ Down 5.8 points
Apparel: 58.7% (Q3: 57.2%) — ✓ Up 1.5 points
Home Goods: 51.2% (Q3: 51.8%) — ✓ Down 0.6 points
+ 5 more categories (all within 5 point threshold)

Alert: Electronics margin decline flagged. Primary driver: Cost per unit increased 8.2% (avg $47.18 to $51.05) while ASP held flat. Recommend vendor contract review.

User (CFO)

"Drill into Electronics — which vendors drove the cost increase?"

Agent Response

Analyzed 124 Electronics purchase orders in Q4. Top cost increases:

TechSupply Corp: +$12.40/unit (was $38.50, now $50.90) — 37% of volume
Global Components: +$4.20/unit (was $29.00, now $33.20) — 28% of volume
3 other vendors: Average +$1.80/unit

TechSupply contract expired Sept 15. No volume discount renegotiation. Opportunity: Consolidated volume could unlock 12-15% discount.

Multi-Turn Context: The agent maintained context from the first question (Q4 Electronics margin) to the follow-up drill-down, automatically joining purchase order data and providing actionable vendor insights—all in under 3 seconds.

Trust & Control

Enterprise Governance Built In

Every agent action is transparent, traceable, and controllable. You maintain complete oversight with human-in-the-loop approval workflows and configurable guardrails.

Full Transparency

See exactly what each agent analyzed and why

Human-in-the-Loop

Approval workflows for sensitive actions

Complete Audit Trail

Every agent action is logged and traceable

Configurable Scope

Define what each agent can access and do

Configurable Approval Workflows

Set dollar thresholds, materiality rules, and approval chains that align with your financial controls.

EXAMPLE RULE

"Any anomaly flagged in GL accounts 4000-4999 (Revenue) exceeding $25K requires VP Finance approval before auto-adjustment"

Activated Anomaly Detection
EXAMPLE RULE

"Cash flow forecasts deviating >15% from prior week require FP&A team review before publishing to executive dashboard"

Triggered 3 times in Q4
EXAMPLE RULE

"Natural language queries accessing payroll or compensation data restricted to HR-Finance role, logged with user identity"

Enforced via RBAC integration

Agentic AI vs. Traditional Analytics

Traditional tools wait for you to ask. NSGPT agents proactively discover what matters and take action.

Traditional BI

  • You write the queries
  • Scheduled report runs
  • Reactive to your questions
  • Static dashboards
  • Manual investigation
  • Rule-based alerts only

NSGPT Agents

  • Agents explore autonomously
  • Real-time continuous analysis
  • Proactive insight delivery
  • Dynamic, adaptive views
  • AI-powered investigation
  • ML-based anomaly detection

See AI Agents in Action

Request a personalized demo to see how NSGPT agents can transform your NetSuite analytics with measurable ROI.

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