AI Invoice Processing Workflow
Automate invoice ingestion, data extraction, validation, and routing using AI agents and document processing pipelines.
Document Ingestion & Parsing
Convert invoice PDFs and scanned documents into structured, LLM-ready text
Purpose-built document converter that handles PDFs, scanned images, and office formats into clean structured output ready for downstream AI processing
Specializes in transforming complex PDF layouts (tables, multi-column invoices) into LLM-friendly markdown with high fidelity
Enterprise-grade document ETL that can ingest invoices from diverse sources and normalize them into structured data
Data Extraction & Structuring
Use LLMs with structured output to extract invoice fields like vendor, line items, amounts, and dates
Enforces structured outputs from LLMs via Pydantic schemas — ideal for extracting typed invoice fields (amounts, dates, line items) with validation built in
Full agent framework with native Pydantic validation, useful when extraction requires multi-step reasoning or tool calls against reference databases
Programmatic approach to prompt optimization — can learn to extract invoice fields more accurately over time from labeled examples
Workflow Orchestration & Routing
Orchestrate the end-to-end invoice pipeline: extraction, approval routing, exception handling, and integration with accounting systems
Visual workflow builder with native AI nodes, perfect for connecting invoice ingestion → extraction → approval → ERP push with branching logic and error handling
Python-native workflow orchestration with retries, scheduling, and observability — suits teams that prefer code-first pipeline definitions
Durable execution engine that guarantees invoice workflows complete even through failures — critical for financial processing reliability
LLM Gateway & Cost Management
Route LLM calls through a unified gateway for cost tracking, rate limiting, and provider failover across the invoice pipeline
Unified API proxy supporting 100+ LLM providers — enables cost tracking per invoice batch and automatic failover if a provider is down
High-performance AI gateway with built-in guardrails, useful for enforcing spending limits and content policies on invoice-related LLM calls
Observability & Evaluation
Monitor extraction accuracy, track pipeline performance, and evaluate LLM output quality across invoice batches
Traces every LLM call in the invoice pipeline with cost and latency metrics — enables debugging extraction errors and measuring accuracy over time
Debug and evaluate LLM extraction quality with dataset-driven testing — catch regressions when invoice formats change
AI observability platform that visualizes embedding drift and model performance — useful for detecting when new invoice formats degrade extraction quality