Multi-Engine AI and Agentic Document Automation
Data Alchemy · June 4, 2026 · 4 min read
Document processing is shifting: the conversation is no longer just about "extracting data" but about autonomous AI agents that complete entire workflows. It's the move from OCR to agentic document automation. In this article we look at what a multi-engine AI is, how document agents work, and why this approach goes beyond traditional IDP.
What Is a Multi-Engine AI?
A multi-engine AI doesn't tie the whole platform to one generic model. Each document model — supplier invoice, delivery note (DDT), purchase order, contract, price list — has its own AI engine, chosen for that document type. In Data Alchemy, for each document model you choose one AI engine: Claude (Anthropic), the engine we recommend by default; GPT (OpenAI); Data Alchemy AI, Data Alchemy's proprietary model, running on Data Alchemy's own servers and GPUs in Italy; or a compatible LLM through the European inference providers Scaleway (Generative APIs) and Seeweb Regolo.ai, on a subscription taken out directly by the customer.
Each document is processed by one engine at a time: the one assigned to its model. For example, you can run the invoice model on Claude, the delivery note model on GPT and the order model on Data Alchemy AI, or enable several models on the same engine. That is where multi-engine flexibility lies: you choose the engine model by model. With Claude the result is a stated 99.8% accuracy; with Claude Sonnet or higher, reading takes on average around 12 seconds for a one-page document and around 18 seconds for two pages. No accuracy or timing figure is published for the other engines.
From Extraction to Agentic Document Automation
Classic IDP software performs one operation: it reads the document and returns the data. Agentic automation goes a step further. An autonomous AI agent doesn't just extract — it reasons, decides and acts to complete a goal, handling exceptions the way an expert operator would.
In Data Alchemy the roles are distinct: the document is read by the AI engine paired with its document model, while the AI agent is the component that connects the platform to your ERP or CRM (through the database, TXT, CSV or JSON files or the system's native APIs) to read master data and post the data.
A typical agentic workflow in Data Alchemy:
- >Capture: the Mail service, installed at the customer's premises, manages the business mailboxes and sends the platform only the emails matching a model (invoices, orders, delivery notes); mailboxes and credentials stay within the company perimeter.
- >Classification and extraction: the document is matched to its model and the AI engine chosen for that model extracts every field, with no predefined templates.
- >Validation: the data is checked against your ERP master data and business rules; three-way matching between order, delivery note and invoice is performed.
- >Decision: if everything reconciles, the validated data moves on to the ERP with no manual step; if there's an anomaly, the document goes to a human reviewer with the issue already flagged.
- >Integration: the validated data reaches SAP, Zucchetti or TeamSystem through the AI agent, or through an integration built on Data Alchemy's REST APIs and webhooks.
The difference is fundamental: a human no longer drives every step — the system completes the workflow and asks for help only when it genuinely matters.
Why Multi-Engine Beats a Single Model
- >The right engine for each model: each document type uses the engine that gives the best results on your documents, instead of one all-purpose model imposed on everything.
- >Checks before posting: the data extracted by the engine is validated against your ERP master data and business rules; if something doesn't reconcile, the document goes to review instead of being written to your ERP.
- >Evolution: you can enable new document models, each with its chosen engine, without rebuilding the whole system.
It's the same logic behind why we recommend Anthropic's Claude AI as the starting engine and why an LLM-based approach beats traditional OCR.
Agentic AI in the Service of People
Agentic automation isn't about replacing people — it's about multiplying a team's capacity by 2.5 (60% less operational time): automation handles repetitive work 24/7, while human skills stay focused on negotiation, quality control and exception handling. That's how Data Alchemy removes manual data entry without removing the value of human work.
Want to see the multi-engine AI working on your own documents? Discover the Data Alchemy technology, explore the IDP solutions for SMEs, or activate the free Demo (10 documents); for a specific case you can contact us to plan a call with a demo on your own documents.