Operations teams are bleeding hundreds of hours monthly on manual data entry, and the executive reflex to slap a chatbot wrapper on the inbox is failing hard. SZG Labs recently published a production blueprint detailing why single-prompt LLMs create more problems than they solve in enterprise resource planning (ERP) workflows. The core issue isn't model capability; it's architectural. When you treat a probabilistic text generator as a deterministic calculator, you get silent data corruption at scale.

The 95% Accuracy Trap

In isolated demos, an LLM might hit 95% accuracy on a clean PDF invoice. In a mid-market distributor processing 2,000 purchase orders a month with 40 line items each, that 5% error rate translates to 4,000 corrupted database records monthly. SZG Labs identifies three specific anti-patterns causing this failure: probabilistic arithmetic where the model guesses totals, unvalidated SKU guessing that matches competitor parts to wrong internal IDs, and synchronous worker starvation where a 15-second API call freezes the entire ERP UI for warehouse staff.

Decoupling Extraction from Validation

The proposed solution is a multi-stage, asynchronous pipeline that completely separates the AI's job from the math. The architecture routes inbound emails and PDFs to a FastAPI and Redis/Celery ingestion queue. Stage 1 uses the LLM solely for layout and entity extraction, pulling raw line items without calculating anything. Stage 2 employs Pydantic models to enforce strict mathematical invariants, such as verifying that quantity * unit_price equals the line_total within a $0.05 tolerance. If the math fails, the document is quarantined rather than guessed.

Atomic ERP Writes and Idempotency

For the final stage, the blueprint emphasizes writing to ERPs like Odoo via XML-RPC or REST using atomic, one-shot calls. This prevents 'half-populated' orders if a network blip occurs mid-write. Crucially, the system checks for existing client_order_ref values to ensure idempotency, meaning worker retries never create duplicate orders. The AI never blocks an ERP worker thread, and it never auto-confirms an order that fails an invariant check. Instead, ambiguous documents go to a triage queue for a 30-second human review, preserving trust.

Key Takeaways

  • Let LLMs extract text, but let Python handle all calculations and tax math.
  • Move OCR and extraction to async workers to prevent UI latency in synchronous ERPs.
  • Enforce strict schema validation with Pydantic before any data touches the production database.
  • Use idempotent checks to prevent duplicate orders during worker retries.

The Bottom Line

Stop trying to make LLMs do accountant work. If your pipeline doesn't have a deterministic math gate between the AI and your ledger, you are just automating errors.