GDPVal: The First Comprehensive AI Benchmark for Manufacturing

Generative AI has transformed how we process text, but for industrial sectors like manufacturing and logistics, "good enough" isn't enough. A hallucinated CAS number or a misread concentration percentage in a Technical Data Sheet (TDS) doesn't just look bad—it shuts down production lines.

Today, we are releasing insights from GDPVal (Grounding, Data, Parsing Validation), an internal benchmark we developed to evaluate how well Foundation Models (FMs) handle the messy, unstructured reality of industrial procurement.

Why General Benchmarks Fail Manufacturing

Popular benchmarks like MMLU (Massive Multitask Language Understanding) or GSM8K (Grade School Math) are excellent for measuring general reasoning. However, they lack the specific noise patterns found in supply chain data.

In the real world, a customer doesn't ask "What is the capital of France?". They send a PDF titled RFQ_final_v2.pdf containing a table where the SKU column is merged with the description, the units are ambiguous, and the "Part Number" is actually a competitor's discontinued grade.

Introducing GDPVal

GDPVal consists of 500 anonymized, real-world datasets collected from partner manufacturers (with permission). It tests three core capabilities:

• Entity Extraction from NoiseParsing SKUs, quantities, and grades from email bodies, email signatures, and messy CSV attachments.
• Cross-Reference LogicMapping a requested competitor product (e.g., "ChemCo 91234") to an internal inventory equivalent based on specs.
• Constraint SatisfactionRespecting Minimum Order Quantities (MOQs) and pack sizes (e.g., "Customer wants 50kg, but we only sell in drums of 200kg").

Results: Specialized Agents vs. Vanilla Models

We tested vanilla GPT-4o, Claude 3.5 Sonnet, and Poka Labs' specialized parsing agents. The metric is Order Accuracy—a binary pass/fail on whether the final drafted line items perfectly matched the customer's intent.

The gap is driven by context awareness. Generic models treat every number as equally important. Poka Labs agents are trained to distinguish between a phone number in a signature and a quantity in a table, and they know that a CAS number usually resembles a specific pattern.

"The ability to ingest a 50-page TDS and output a perfect CSV quote in 30 seconds is not just a productivity hack; it's a competitive advantage."

The Future of Industrial AI

We are moving from "Chat with your Data" to "Work with your Data". Benchmarks like GDPVal ensure that as we deploy agents into critical workflows, they are held to the same standard as an experienced sales engineer.

We will be releasing a subset of the GDPVal dataset to the research community later this year to encourage more development in industrial-grade AI.