Dataset Building

Dataset Building agents create structured datasets from data scattered across the web. Describe what you’re looking for, define the row shape, and the agent discovers, verifies, and structures the results — no scraper to build, no site list to maintain, no deduplication pipeline. Every cell arrives cited.

Example Request

The business-discovery template is tuned for local-business datasets — verified addresses, phone numbers, and ratings:

agent = nimble.agents.create(template="business-discovery")

run = nimble.agents.runs.create(
    agent.id,
    input=(
        "Find all spas and salons in New York City — real addresses, "
        "phone numbers, and ratings from actual review sites."
    ),
)

const agent = await nimble.agents.create({ template: "business-discovery" });

const run = await nimble.agents.runs.create(agent.id, {
  input:
    "Find all spas and salons in New York City — real addresses, " +
    "phone numbers, and ratings from actual review sites.",
});

agent, err := client.Agents.New(ctx, nimble.AgentNewParams{
    Template: param.NewOpt("business-discovery"),
})

run, err := client.Agents.Runs.New(ctx, agent.ID, nimble.AgentRunNewParams{
    Input: "Find all spas and salons in New York City — real addresses, " +
        "phone numbers, and ratings from actual review sites.",
})

AGENT_ID=$(nimble agents create --template business-discovery | jq -r .id)

nimble agents:runs create \
  --agent-id "$AGENT_ID" \
  --input "Find all spas and salons in New York City — real addresses, phone numbers, and ratings from actual review sites."

curl -X POST "https://sdk.nimbleway.com/v2/agents/$AGENT_ID/runs" \
  --header 'Authorization: Bearer <YOUR-API-KEY>' \
  --header 'Content-Type: application/json' \
  --data '{
    "input": "Find all spas and salons in New York City — real addresses, phone numbers, and ratings from actual review sites."
  }'

Example Response

The result is type: "json" — an array of rows matching the template’s schema, each with a cited source:

{
  "type": "json",
  "content": [
    {
      "business_name": "Haven Spa",
      "address": "150 Mercer St, New York, NY 10012",
      "phone_number": "+1 212-555-0181",
      "website": "https://havenspanyc.com",
      "rating": "4.6",
      "review_count": "412",
      "category": "Day spa",
      "source_url": "https://www.yelp.com/biz/haven-spa-new-york"
    }
  ]
}

Each value carries a per-path claim in the trust report:

{
  "path": "$[0].phone_number",
  "confidence": "high",
  "reasoning": "Supported by 2 independent sources",
  "citations": [
    { "url": "https://www.yelp.com/biz/haven-spa-new-york", "excerpts": ["(212) 555-0181"] },
    { "url": "https://havenspanyc.com/contact", "excerpts": ["Call us: (212) 555-0181"] }
  ]
}

The dataset gets a deterministic overall grade — the lower of its cell fill rate and its per-claim trust ratio — stated in the run’s trust reasoning: “High confidence - cells fill rate is 96% and trust per claim ratio is 91%”. A verifiably empty cell (a business with no website) comes back as a cited null graded high: verified absence.

Custom output schema

Define your own row shape on the agent or per run. Filters in the input are treated as hard constraints — the company-discovery template’s first goal is “Treat user filters (location, vertical, size, funding) as hard constraints — never dilute to inflate volume.”

run = nimble.agents.runs.create(
    agent.id,
    input=(
        "Find SF tech companies that have adopted AI agents and have active "
        "engineering teams — potential CRM leads — explaining why each qualifies."
    ),
    output_schema={
        "type": "object",
        "properties": {
            "leads": {
                "type": "array",
                "items": {
                    "type": "object",
                    "properties": {
                        "company": {"type": "string"},
                        "why_qualified": {"type": "string"},
                        "engineering_signal": {"type": "string"},
                    },
                },
            }
        },
    },
)

const run = await nimble.agents.runs.create(agent.id, {
  input:
    "Find SF tech companies that have adopted AI agents and have active " +
    "engineering teams — potential CRM leads — explaining why each qualifies.",
  output_schema: {
    type: "object",
    properties: {
      leads: {
        type: "array",
        items: {
          type: "object",
          properties: {
            company: { type: "string" },
            why_qualified: { type: "string" },
            engineering_signal: { type: "string" },
          },
        },
      },
    },
  },
});

run, err := client.Agents.Runs.New(ctx, agent.ID, nimble.AgentRunNewParams{
    Input: "Find SF tech companies that have adopted AI agents and have active " +
        "engineering teams — potential CRM leads — explaining why each qualifies.",
    OutputSchema: map[string]any{
        "type": "object",
        "properties": map[string]any{
            "leads": map[string]any{
                "type": "array",
                "items": map[string]any{
                    "type": "object",
                    "properties": map[string]any{
                        "company":            map[string]any{"type": "string"},
                        "why_qualified":      map[string]any{"type": "string"},
                        "engineering_signal": map[string]any{"type": "string"},
                    },
                },
            },
        },
    },
})

nimble agents:runs create \
  --agent-id "$AGENT_ID" \
  --input "Find SF tech companies that have adopted AI agents and have active engineering teams — potential CRM leads — explaining why each qualifies." \
  --output-schema '{type: object, properties: {leads: {type: array, items: {type: object, properties: {company: {type: string}, why_qualified: {type: string}, engineering_signal: {type: string}}}}}}'

curl -X POST "https://sdk.nimbleway.com/v2/agents/$AGENT_ID/runs" \
  --header 'Authorization: Bearer <YOUR-API-KEY>' \
  --header 'Content-Type: application/json' \
  --data '{
    "input": "Find SF tech companies that have adopted AI agents and have active engineering teams — potential CRM leads — explaining why each qualifies.",
    "output_schema": {
      "type": "object",
      "properties": {
        "leads": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "company":            {"type": "string"},
              "why_qualified":      {"type": "string"},
              "engineering_signal": {"type": "string"}
            }
          }
        }
      }
    }
  }'

Run it on a schedule

A dataset run produces a snapshot. To turn it into a recurring feed, point a Job at the same agent with a list of inputs — one per market, category, or account — and Nimble runs it on a schedule and delivers results to S3, Databricks, or your warehouse.

Templates

Template	Built for
`business-discovery`	Local businesses with verified contact details and ratings
`company-discovery`	Companies matching hard filters: location, vertical, size, funding
`gtm-lead-discovery`	Qualified sales leads with the reason each qualifies
`open-positions-search`	Live job openings across companies and markets
`social-media-monitor`	Structured social activity around brands and topics

Web Search Agents overview

Research, Enrichment, and Dataset Building side by side

Getting Started

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Web Search Agents

Jobs

Guides

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Admin

Proxy

Example Request

Example Response

Custom output schema

Run it on a schedule

Templates

Web Search Agents overview

​Example Request

​Example Response

​Custom output schema

​Run it on a schedule

​Templates

Web Search Agents overview

Example Request

Example Response

Custom output schema

Run it on a schedule

Templates