Data enrichment

Lasso Enrich takes partial product records — as little as a product name or SKU — and fills in every missing field with sourced, cited data. Each value comes with a basis showing exactly where the information came from.

How it works

Pass in what you have — Provide one or more items with whatever data you already know (name, SKU, brand, etc.).
Define the target schema — Tell Lasso what fields you want filled in (or use the default product schema).
Lasso researches each product — The AI searches Lasso’s database, finds the product, and extracts structured data.
Get back complete records — Every field is filled in with citations, reasoning, and confidence scores.

The basis

Every enriched field includes a basis that tells you:

citations — The sources used (URL, title, excerpt)
reasoning — How the AI determined the value
confidence — high or low

This transparency is critical for product data quality. You can verify values, filter by confidence, or show sources to your users.

Schema options

Same as Search — use schema_id, inline columns, or omit both for the default product schema.

Sync vs async

Sync (default): For up to 5 items, Lasso processes and returns results in the same request.
Async: For larger batches (>5 items) or when you pass webhook_url, Lasso returns 202 immediately and delivers results via webhook.

Example: Search then Enrich

A common pattern is to search for products first, then enrich the results with additional detail.

// 1. Search for products
const search = await client.search({
  query: "best wireless mice for gaming 2025",
  columns: [
    { key: "name", label: "Name", type: "text" },
    { key: "brand", label: "Brand", type: "text" },
  ],
  max_results: 5,
});

// 2. Enrich with full detail
const enriched = await client.enrich({
  items: search.results.map(r => ({ data: r.data })),
  columns: [
    { key: "name", label: "Name", type: "text" },
    { key: "brand", label: "Brand", type: "text" },
    { key: "price", label: "Price", type: "number" },
    { key: "dpi", label: "Max DPI", type: "number" },
    { key: "weight", label: "Weight (g)", type: "number" },
    { key: "features", label: "Features", type: "tags" },
    { key: "description", label: "Description", type: "text" },
  ],
});

for (const item of enriched.items) {
  console.log(`${item.data.name} — ${item.data.price}g, ${item.data.dpi} DPI`);
}

# 1. Search for products
search = client.search(
    query="best wireless mice for gaming 2025",
    columns=[
        {"key": "name", "label": "Name", "type": "text"},
        {"key": "brand", "label": "Brand", "type": "text"},
    ],
    max_results=5,
)

# 2. Enrich with full detail
enriched = client.enrich(
    items=[{"data": r["data"]} for r in search["results"]],
    columns=[
        {"key": "name", "label": "Name", "type": "text"},
        {"key": "brand", "label": "Brand", "type": "text"},
        {"key": "price", "label": "Price", "type": "number"},
        {"key": "dpi", "label": "Max DPI", "type": "number"},
        {"key": "weight", "label": "Weight (g)", "type": "number"},
        {"key": "features", "label": "Features", "type": "tags"},
        {"key": "description", "label": "Description", "type": "text"},
    ],
)

for item in enriched["items"]:
    print(f"{item['data']['name']} — {item['data']['price']}g, {item['data']['dpi']} DPI")

Credits

Credit cost depends on the thinking level you choose:

Thinking	Model	Credits/item	Best for
`hard`	Gemini 3.1 Pro	4	Complex or ambiguous products
`medium`	Gemini 3.1 Lite	2	General-purpose enrichment (default)
`low`	Gemini 2.0	1	Fast, straightforward lookups

Web search

By default, Lasso uses web search to research each product and provides a basis with citations for every enriched field. You can disable this with web_search: false — the AI will fill fields from its own knowledge without citations, which is faster and still accurate for well-known products.

Next steps

Enrich API reference — Full parameter and response documentation.
Search — Discover products to enrich.
Glossary — Control terminology during enrichment with use_glossary: true.

Getting started

TypeScript

Python

Learn

Data enrichment

How it works

The basis

Schema options

Sync vs async

Example: Search then Enrich

Credits

Web search

Next steps

​How it works

​The basis

​Schema options

​Sync vs async

​Example: Search then Enrich

​Credits

​Web search

​Next steps

How it works

The basis

Schema options

Sync vs async

Example: Search then Enrich

Credits

Web search

Next steps