How Catalog Agents Extract Product Attributes at SKU Scale
The attribute agent reads unstructured descriptions and raw supplier feeds, reasons about what the data means, and outputs structured fields your channels can act on.
A supplier sends you a product with this description: "Heavy-duty industrial degreaser, works on metals and composites, safe for use in enclosed spaces, 1-gallon container, Part #DGR-4400." That's it. No attributes. No taxonomy. No hazmat classification. No compatibility data.
Your catalog needs structured fields: chemical composition category, application surface, ventilation requirements, container size, SKU, hazmat flag, OSHA compliance status. None of that is in the description. A catalog agent has to extract it — not by searching for keywords, but by reasoning about what the description implies.
Why keyword matching fails at this task
The traditional approach to attribute extraction is pattern matching. Find "1-gallon" and write "1 gal" to the volume field. Find "metals and composites" and write it to the compatibility field. This works on data that's already well-structured and consistently formatted. It breaks the moment suppliers describe the same thing differently.
One supplier writes "approved for enclosed spaces." Another writes "low-VOC, suitable for indoor use." A third writes "ventilation not required." These are three descriptions of the same attribute — indoor/ventilation safety — expressed in three different ways. A pattern matcher misses two of them. A catalog agent reads all three and writes the same structured value to the same field.
The difference is reasoning about meaning, not matching strings. Catalog agents use language models trained to understand what product descriptions imply, not just what they say explicitly.
The extraction pipeline
When a catalog agent processes a SKU, the extraction happens in stages:
1. Source assembly. The agent gathers all available inputs for the SKU — supplier description, title, raw spec sheet text, any existing partial attributes, category context. The richer the input set, the more accurate the extraction.
2. Field identification. The agent determines which attributes are required for this SKU's category. A degreaser needs different fields than a laptop stand. The agent knows the target schema and reasons backward from what's needed to what to look for.
3. Attribute reasoning. For each required field, the agent reasons about whether the value is stated, implied, or inferable. "Safe for enclosed spaces" is stated. The ventilation flag can be inferred from it. The VOC classification might be inferrable from the product category and usage context if the supplier didn't provide it.
4. Confidence scoring. Each extracted attribute gets a confidence score. High-confidence attributes go directly to the catalog. Lower-confidence ones get flagged for human review or are left empty rather than filled with a guess.
5. Format normalization. Extracted values get normalized to your schema's format. "1-gallon," "1 gal," "1G," and "gallon (1)" all become the same structured value. Units are standardized, taxonomies are mapped, controlled vocabularies are applied.
What the agent actually extracts from a description
| Supplier description phrase | Extracted attribute | Field | Confidence |
|---|---|---|---|
| "works on metals and composites" | metals, composites | Compatible surfaces | High |
| "safe for use in enclosed spaces" | Yes | Indoor safe | High |
| "safe for use in enclosed spaces" | Low | Ventilation required | Medium (inferred) |
| "1-gallon container" | 1 gal / 3.785 L | Volume | High |
| "heavy-duty industrial degreaser" | Industrial / Maintenance | Product category | High |
| "heavy-duty industrial degreaser" | Review flagged | Hazmat classification | Low (needs verification) |
| "Part #DGR-4400" | DGR-4400 | Supplier part number | High |
The hazmat classification gets flagged rather than filled — the agent knows it can't confidently infer the exact hazmat class from the description alone. That's the right behavior. A catalog with a wrong hazmat flag is more dangerous than one with an empty field.
Handling sparse and contradictory inputs
Most supplier data isn't this clean. Common scenarios:
Sparse descriptions. Some suppliers provide one line: "Industrial cleaner, gallon." The agent extracts what it can, marks the rest as missing, and can optionally query secondary sources (product images, web-sourced spec data, manufacturer documentation) to fill gaps.
Contradictory fields. The description says "lightweight" but the weight field shows 47 lbs. The agent flags the conflict rather than silently accepting either value. Unresolved contradictions surface for human review.
Non-English inputs. Supplier data from international markets often arrives in mixed languages. Attribute extraction works across language boundaries — the agent reasons about meaning regardless of whether the input is in English, German, or Mandarin.
Image-based inputs. Some attributes can only be extracted from product images — color, form factor, label text, physical port layouts. Multimodal catalog agents can process product images alongside text descriptions and extract attributes from both.
What structured extraction changes for AI commerce
Once attributes are structured, they're available at the filter step of AI shopping agents. A buyer query that includes "compatible with 18-gauge aluminum, indoor use, under 2 gallons" can now match against your product because the agent populated those exact fields. Without extraction, the product is invisible to that query regardless of how well the title was written.
Attribute extraction isn't a data hygiene project. It's the difference between being in the consideration set and not being there at all.
See how Paladio Catalog Agents handle attribute extraction →