Enrichment

Enrichment agents complete the data you already have. Send partial records in input_data — CRM leads missing funding stages, product rows missing specs — and the agent researches each gap on the live web and fills it. Your values are treated as ground truth and passed through untouched. Only the blanks get researched.

IN    "company_name":   "Acme Corp"
      "headquarters":   null
      "key_contacts":   null

      ── the agent researches only what's missing ──

OUT   "company_name":   "Acme Corp"          · yours, untouched
      "headquarters":   "Shelton, CT"        · cited: acme.com
      "key_contacts":   [{"name": "..."}]    · cited: acme.com/leadership

Example Request

An enrichment run needs the rows (input_data) and the shape (output_schema, on the request or the agent). The lead-enrichment template ships a schema covering company identity, headquarters, decision-makers, and buying signals:

agent = nimble.agents.create(template="lead-enrichment")

run = nimble.agents.runs.create(
    agent.id,
    input="Fill in the missing fields for these companies.",
    input_data=[
        {"company_name": "Nimble", "headquarters": None, "key_contacts": None},
        {"company_name": "Bright Data", "headquarters": None, "key_contacts": None},
    ],
)

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

const run = await nimble.agents.runs.create(agent.id, {
  input: "Fill in the missing fields for these companies.",
  input_data: [
    { company_name: "Nimble", headquarters: null, key_contacts: null },
    { company_name: "Bright Data", headquarters: null, key_contacts: null },
  ],
});

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

run, err := client.Agents.Runs.New(ctx, agent.ID, nimble.AgentRunNewParams{
    Input: "Fill in the missing fields for these companies.",
    InputData: nimble.AgentRunNewParamsInputDataUnion{
        OfMapOfAnyMap: []map[string]any{
            {"company_name": "Nimble", "headquarters": nil, "key_contacts": nil},
            {"company_name": "Bright Data", "headquarters": nil, "key_contacts": nil},
        },
    },
})

nimble agents:runs create \
  --agent-id "$AGENT_ID" \
  --input "Fill in the missing fields for these companies." \
  --input-data '[{company_name: "Nimble", headquarters: null, key_contacts: null}, {company_name: "Bright Data", headquarters: null, key_contacts: null}]'

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": "Fill in the missing fields for these companies.",
    "input_data": [
      {"company_name": "Nimble", "headquarters": null, "key_contacts": null},
      {"company_name": "Bright Data", "headquarters": null, "key_contacts": null}
    ]
  }'

input_data accepts a list of partial rows (or a single object) mirroring the output schema. An output_schema — on the request or the agent — is required.

Example Response

The result is type: "json" — your rows back, gaps filled:

{
  "type": "json",
  "content": [
    {
      "company_name": "Nimble",
      "website": "https://www.nimbleway.com",
      "headquarters": "New York, NY, United States",
      "key_contacts": [
        { "name": "Uri Knorovich", "title": "Co-founder & CEO", "seniority": "c_suite" }
      ],
      "buying_signals": ["Launched the Web Search Agents API (July 2026)"],
      "summary": "Nimble is an AI-native web data platform providing search, extraction, and research agent APIs."
    }
  ],
  "trust": {
    "confidence": "high",
    "reasoning": "High confidence - cells fill rate is 100% and trust per claim ratio is 94%",
    "claims": [
      {
        "path": "$[0].company_name",
        "confidence": "pre_existing",
        "reasoning": "Provided in input_data"
      },
      {
        "path": "$[0].headquarters",
        "confidence": "high",
        "reasoning": "Backed by a primary source (official)",
        "citations": [
          {
            "url": "https://www.nimbleway.com/about",
            "excerpts": ["Headquartered in New York"],
            "source_category": "official"
          }
        ]
      }
    ]
  }
}

Trust in enrichment runs

Every value in the result carries a claim per JSON path in the trust report. The grades distinguish your data from researched data:

Confidence	Meaning in an enrichment run
`pre_existing`	Came from your `input_data` — carried over unchanged
`high` / `medium` / `low`	Researched — with citations and excerpts

For example, $[0].headquarters carries a citation and a high grade, while $[0].company_name is marked pre_existing. Route low-confidence fills to review and sync the rest into your CRM, with source URLs alongside. Two additional rules:

The run’s overall grade counts only researched values. pre_existing data is passed through, never graded.
A gap the agent could not fill comes back as a cited null (verified absence), distinguishing “no funding data exists” from “not found”.

Use cases

CRM hygiene — complete funding, headcount, and tech-stack fields across the pipeline, with buying signals like recent hiring flagged (lead-enrichment).
Financial datasets — fill valuation, investor, and filing gaps from primary sources (financial-intelligence).
Product catalogs — complete specs, pricing, and availability across competitor listings (ecommerce-intelligence).

To enrich thousands of rows on a schedule, hand the same agent to Jobs — CSV or Parquet in, enriched rows delivered to your warehouse.

Trust

How per-path claims, citations, and pre_existing fit together

Getting Started

Web Tools

Extract Templates

Web Search Agents

Jobs

Guides

SDKs

Admin

Proxy

Example Request

Example Response

Trust in enrichment runs

Use cases

Trust

​Example Request

​Example Response

​Trust in enrichment runs

​Use cases

Trust

Example Request

Example Response

Trust in enrichment runs

Use cases