ResolveOps

The description states that ResolveOps investigates EDI invoice exceptions using deterministic business rules and presents results in a guided operator workflow. It claims to:

• Detect invoice-document mismatches
• Search and rank ERP document candidates
• Compare supplier, store, document reference, amount, VAT, and date
• Match items through exact or alternate EANs
• Recognize package-to-piece conversions
• Detect quantity differences
• Detect blocking price differences
• Refuse automatic matching when candidates are ambiguous
• Propose the next safe action
• Require preview and explicit operator approval before write actions
• Revalidate the current state before execution
• Make execution idempotent
• Verify the resulting state and retain an audit record

The system uses ASP.NET Core for backend, React/TypeScript/Vite for frontend, and OpenAI's Responses API for explanations. It does not connect to production ERP databases but is built on real retail EDI scenarios.

Evidence The author's own write-up describes these features in detail.

Inference ResolveOps appears to be a workflow automation tool that combines deterministic logic with AI-generated explanations for human operators to review and approve.

The description states that ResolveOps was built to make manual invoice reconciliation faster without allowing AI to bypass accounting and inventory controls. It positions itself as an assistant that improves understanding and navigation rather than replacing controls.

It claims to be more than a chatbot over invoice data, emphasizing structured workflows, safety models, and deterministic decision-making.

Evidence The author's own write-up describes this positioning.

Inference This is a self-reported attempt to create a hybrid system where AI supports human operators while maintaining strict control over operational actions.

The description states that ResolveOps targets retailers who still rely on employees to manually enter receiving documents and reconcile supplier invoices inside their ERP systems. It specifically mentions problems like:

• Invoice and receiving-document references written differently
• Invoices linked to documents from the wrong store
• ERP receiving documents missing
• Supplier item codes and EANs not matching
• Invoice quantities expressed in packages while ERP quantities are stored in pieces
• Prices or quantities differing

The author notes that resolving these cases often requires opening several ERP screens, comparing records manually, understanding technical matching rules, and deciding whether a correction is safe.

Evidence The author's own write-up describes the target use case.

Inference The primary customer segment appears to be mid-to-large retail companies with ERP systems and manual invoice reconciliation processes.

Not evidenced. The description does not contain any information about pricing, monetization strategy, or business model.

The author states that ResolveOps uses:

• ASP.NET Core for backend
• React, TypeScript, and Vite for frontend
• Synthetic JSON data for demonstration
• Deterministic matching and safety services
• OpenAI Responses API for explanations
• Structured outputs with defined JSON schema
• In-memory preview, approval, execution, verification, and audit services
• Automated frontend and backend tests

It also mentions using Codex and GPT-5.6 during development.

The system does not connect to production ERP databases but uses synthetic data derived from real retail scenarios.

Evidence The author's own write-up describes the technical stack and architecture.

Inference This is a prototype built in one week with synthetic data, not production-ready software.

Not evidenced. There is no mention of revenue, customers, adoption, or any traction metrics beyond the fact that it was submitted to a hackathon.

Not evidenced. The description does not contain any information about competitors or market positioning relative to existing solutions.

1. Prototype vs Production: The system is described as a one-week hackathon project using synthetic data and in-memory services, not connected to production ERP systems.
2. No Real-World Testing: The author explicitly states that the system does not connect to real databases or production environments.
3. Single Developer: The team size is listed as 1 person (Marko Mitrovic).
4. Unverified Claims: All claims are self-reported and unverified.
5. Limited Scalability: No evidence of scalability beyond a single-person prototype.
6. AI Dependency: While the system separates AI from operational authority, it still depends on OpenAI's API for explanations.

1. What specific ERP systems does ResolveOps support or plan to support?
2. How will the system integrate with existing retail EDI infrastructure?
3. What are the actual performance requirements for production use?
4. How is the deterministic matching logic validated against real-world data?
5. What is the plan for handling edge cases not covered in the demo?
6. How does the system handle concurrent users or high-volume processing?
7. Are there any existing partnerships or pilot customers?
8. What are the key assumptions about user behavior and operator workflows?
9. How will the system be maintained and updated post-hackathon?
10. What is the roadmap for moving from synthetic to real data?