ResolveAR AI
AI Accounts Receivable Operations
AI-assisted accounts receivable operations that help finance teams prioritize collections and streamline follow-up.
- Category
- SaaS
- Type
- Product
- Status
- pre-production validation
- Website
- resolvear.vercel.app
01 · Overview
The problem and the solution
Business problem
Accounts receivable teams manage large volumes of open invoices, follow-ups, and disputes with spreadsheets and email, making it hard to focus effort where it matters most.
Solution
ResolveAR AI centralizes receivables work and applies AI to prioritize accounts, draft follow-up communication, and keep disputes moving toward resolution.
02 · Functional
What the product does
Key features
Receivables prioritization
Focus the team on the accounts that need attention first.
AI-drafted follow-ups
Generate context-aware follow-up messages for review.
Dispute tracking
Track disputes and their resolution steps in one workflow.
AR operations overview
See open work and status across the receivables portfolio.
User workflows
- Open invoices and account history are imported.
- Accounts are prioritized using balance, age, and payment history.
- AI drafts follow-up messages with account context for the team to review and send.
- Disputes are logged and tracked through each resolution step.
- The team sees open work and status across the portfolio.
03 · Technical
How it's built
Technology stack
- TypeScript
- PostgreSQL
- Redis
- OpenAI
- AWS
- Next.js
- Java
- Python
- React
- Spring Boot
- Agentic AI
- Docker
- LangChain
- LangGraph
- RAG
- pgvector
- devOps
- CICD
System architecture
ResolveAR AI is designed as a NestJS service with a Next.js front end and PostgreSQL. Imports run as background jobs that normalize invoice and payment data from different sources into one receivables model.
Prioritization combines transparent rules (age, balance, history) with AI assistance, so the team can see why an account is at the top of the list. Message drafting uses an LLM with account context and templates approved by the finance team.
Engineering highlights
- Import pipeline that normalizes invoices and payments into a single receivables model.
- Explainable prioritization: every ranking shows the factors behind it.
- Template-guided AI drafting that keeps tone and terms consistent.
- Audit trail for follow-ups and dispute changes.
Challenges and design decisions
- Explainability: finance teams need to justify decisions, so prioritization is visible and rule-based rather than a black box.
- Data quality: source systems export inconsistent formats, so imports are validated and errors surfaced before data is used.
- Sensitive data: access is role-based and every change is recorded.
04 · Infrastructure
Hosting, delivery, and operations
- The NestJS API and Next.js front end run as containers on AWS behind a load balancer, with PostgreSQL on Amazon RDS and Redis for job queues and caching.
- Import and prioritization jobs run on scheduled, retryable background workers.
- Infrastructure is defined as code (Terraform), with separate staging and production environments.
- Encryption in transit and at rest, role-based access, and an audit log for every change to accounts and disputes.
- Structured logging, metrics, and alerts for failed imports and queue backlogs.
Interested in ResolveAR AI?
Visit the product website, or get in touch for a walkthrough or a technical discussion.