> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nimbleway.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Dataset Building
> One prompt in, a verified dataset out — every cell cited
**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](/nimble-sdk/web-search-agents/trust).
## Example Request
The `business-discovery` template is tuned for local-business datasets — verified addresses, phone numbers, and ratings:
```python Python theme={"system"}
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."
),
)
```
```typescript TypeScript theme={"system"}
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.",
});
```
```go Go theme={"system"}
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.",
})
```
```bash CLI theme={"system"}
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."
```
```bash cURL theme={"system"}
curl -X POST "https://sdk.nimbleway.com/v2/agents/$AGENT_ID/runs" \
--header 'Authorization: Bearer ' \
--header 'Content-Type: application/json' \
--data '{
"input": "Find all spas and salons in New York City — real addresses, phone numbers, and ratings from actual review sites."
}'
```
## Example Response
The result is `type: "json"` — an array of rows matching the template's schema, each with a cited source:
```json theme={"system"}
{
"type": "json",
"content": [
{
"business_name": "Haven Spa",
"address": "150 Mercer St, New York, NY 10012",
"phone_number": "+1 212-555-0181",
"website": "https://havenspanyc.com",
"rating": "4.6",
"review_count": "412",
"category": "Day spa",
"source_url": "https://www.yelp.com/biz/haven-spa-new-york"
}
]
}
```
Each value carries a per-path claim in the trust report:
```json theme={"system"}
{
"path": "$[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](/nimble-sdk/web-search-agents/trust#trust-for-structured-outputs).
## 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."*
```python Python theme={"system"}
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"},
},
},
}
},
},
)
```
```typescript TypeScript theme={"system"}
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" },
},
},
},
},
},
});
```
```go Go theme={"system"}
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"},
},
},
},
},
},
})
```
```bash CLI theme={"system"}
nimble agents:runs create \
--agent-id "$AGENT_ID" \
--input "Find SF tech companies that have adopted AI agents and have active engineering teams — potential CRM leads — explaining why each qualifies." \
--output-schema '{type: object, properties: {leads: {type: array, items: {type: object, properties: {company: {type: string}, why_qualified: {type: string}, engineering_signal: {type: string}}}}}}'
```
```bash cURL theme={"system"}
curl -X POST "https://sdk.nimbleway.com/v2/agents/$AGENT_ID/runs" \
--header 'Authorization: Bearer ' \
--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](/nimble-sdk/agentic/jobs) 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 |
Research, Enrichment, and Dataset Building side by side