Delana Technologies designed and built a production conversational AI analytics agent that lets sales, finance, and leadership ask plain-English questions about revenue, margin, and collections and get structured answers in seconds. This case study covers what the agent does, how it unifies three ERP systems, and why self-service analytics changes how a commercial team operates.
The Problem: Answers Locked Behind Reporting Cycles
In most distribution and manufacturing businesses, a simple question, such as which customers are past due, which SKUs are selling, or where margin is leaking, requires someone to write SQL, pull a report, or wait on the data team. When the business runs on multiple ERP systems, it is worse: the same customer exists in different shapes in different platforms, and every answer starts with reconciliation rather than analysis.
What We Built
The Sales Agent is a conversational AI analytics agent built in n8n and powered by Anthropic’s Claude. It acts as a natural-language interface to a company’s sales, order, and accounts-receivable data. It unifies data across three ERP systems (P21, QAD, and M2M) into a single gold layer, so users get one consistent view of the business regardless of which back-end system a customer or order lives in.
Core Capabilities
The agent orchestrates roughly two dozen specialized query tools plus sub-agents, giving it broad functional coverage across the commercial data landscape.
Revenue and Sales Analytics
- Rank active customers by year-to-date and trailing-90-day revenue
- Year-over-year sales comparison
- Top-selling SKU analysis
- Revenue by source and fulfillment channel
- New versus lapsed account identification
Finance and Credit Risk
- Accounts receivable aging with full 30/60/90-day bucketing
- Past-due and negative-margin detection
- Gross margin and margin-by-vertical analysis
- Invoice and payment tracking
Operations
- Open orders by status
- Ship-to location lookups
- Recent orders and quotes
- Returns and RMAs
- Bill-of-materials lookups
Conversation and Document Handling
The agent maintains conversational memory, so users can drill down naturally, for example following a revenue question with “now show me AR aging for that customer.” It also includes document-extraction abilities for PDF and file parsing, plus a Full Sales Playbook sub-agent for multi-step analyses that chain several queries together.
Business Impact
The agent’s main value is that it collapses what would normally be analyst-led reporting cycles into an instant self-service conversation. Sales reps, finance teams, and leadership get answers to revenue, margin, and collections questions without writing SQL, pulling reports, or waiting on a data team. By consolidating three separate ERP systems into one queryable layer, it removes the friction of reconciling data across platforms and gives the organization a single source of truth.
In practical terms, it accelerates decisions about which accounts to prioritize, where credit risk is concentrated (for example, identifying customers carrying 100% of their balance past due), and where margin is leaking. It scales the reach of the analytics function, letting more people make data-driven decisions faster while freeing skilled analysts for higher-value work.
Technology Stack
- n8n agent orchestration
- Anthropic Claude as the reasoning model
- P21, QAD, and M2M ERP data sources
- Unified gold-layer data model over the three systems
- 20+ specialized SQL query tools and sub-agents
- Conversational memory and PDF/document extraction
Is This Right for Your Business?
This pattern fits distributors, manufacturers, and multi-entity businesses running more than one ERP or accounting system, especially teams where finance and sales questions currently queue behind a small analytics group. The same architecture applies to inventory, procurement, service, and field-operations data.
Talk With Delana Technologies
Call 239.414.5126 or contact us online to scope an agentic AI analytics project across your ERP and financial systems.