Data enrichment for AI agents: turn raw records into verified, real-time business data

TL;DR

• Live-web data enrichment beats static databases because an AI agent fetches and verifies each field at query time and attaches a source citation to every attribute, instead of pulling from a table that goes stale within months.
• Legacy providers (ZoomInfo 321M profiles, Apollo 240M, Clay's 50+ provider waterfall) sell frozen snapshots. Fill rate and match rate look high on day one and decay silently after that.
• Linkup has four endpoints for enrichment: /search for single-fact lookups (1–3s, $0.005–$0.006), /research for deep company briefs (2–20 min async, 61% SealQA), /tasks for batch enrichment of up to 100 records in one submission, and /extract (closed beta) for pulling structured rows from listing pages.
• Linkup grounds every enriched field in live, cited web sources, scores 92% F-score on Verified SimpleQA (Standard) and 94% on Linkup Fast - fewer hallucinated attributes than static providers - and offers Zero Data Retention on all plans plus BYOC so you enrich sensitive records without leaking them to a data broker.
• Start free with 4,000 queries at app.linkup.so/sign-up.

Data enrichment works best as a live-web problem, not a database purchase. An AI agent should fetch and verify each field (headcount, funding stage, HQ, tech stack, recent news) from current web sources at the moment you need it, and cite where each value came from. That is the difference between an enriched record you can defend in an audit and a row copied from a snapshot that was accurate six months ago. This post covers why static enrichment decays, the exact API pattern for on-demand enrichment, and how to keep sensitive customer records out of a third-party broker.

Why static data enrichment goes stale (and why you can't see it)

Static enrichment databases decay the moment they are shipped, and the decay is invisible until a deal breaks. Vendors advertise large profile counts and high validity rates - but those are day-one numbers measured against their own snapshot. They do not tell you that a contact changed jobs last week, that a company raised a round yesterday, or that HQ moved cities. Waterfall enrichment (Clay chaining 50+ providers) raises match rate versus a single source, but it is still stitching together multiple frozen tables, not reading the live web.

The hidden cost is auditability. When a static provider returns "headcount: 450", you cannot see the source, the date, or the confidence. For a sales list that is annoying. For a compliance-reviewed AI product it is disqualifying. The real cost of RAG is the same story: retrieval that cannot cite its source is retrieval you cannot trust in production.

The live-web enrichment pattern: fetch and verify per field

Live enrichment reverses the model: instead of buying a database, you query the current web once per record, per field, and store the citation alongside the value. The flow is three steps.

1. Resolve the entity. Take the raw record (a company name, a domain, an email) and run a search to confirm you have the right entity before enriching it.
2. Fetch each attribute with a structured query. Ask for the specific field you need ("latest funding round for X", "current employee count of X") and request the answer with sources.
3. Store the value and its citation. Persist not just "Series B" but the URL and the retrieval timestamp, so any downstream reviewer can verify the field.

This is why accuracy matters more than raw database size. Linkup's /search API scores 92% F-score on Verified SimpleQA, the highest among sub-second web search APIs, which directly reduces hallucinated attributes in an enrichment pipeline. For deeper, multi-hop enrichment (funding history, competitor moves, litigation), the /research endpoint runs asynchronously in 1 to 10 minutes and scores 61% on SealQA-0. Choose /search for single facts at signup latency, /research for a full company brief.

Working example: enrich a company record on demand

Here is the exact pattern for enriching one record with cited fields, using the Linkup Python SDK.

import os
from linkup import LinkupClient

client = LinkupClient(api_key=os.environ["LINKUP_API_KEY"])

def enrich_field(company: str, question: str) -> dict:
response = client.search(
query=f"{question} for {company}",
depth="standard",
output_type="sourcedAnswer", # returns answer + source citations
)
return {
"value": response.answer,
"sources": [s.url for s in response.sources],
}

raw_record = {"company": "Acme Robotics", "domain": "acme.example"}

fields = {
"funding_stage": "latest funding round and total raised",
"headcount": "current employee count",
"hq": "headquarters city and country",
"recent_news": "most recent significant company announcement",
}

enriched = {
field: enrich_field(raw_record["company"], q)
for field, q in fields.items()
}

for field, data in enriched.items():
print(field, "=>", data["value"])
print(" sources:", data["sources"])


Each field comes back with a value and the URLs it was drawn from. At $0.005 to $0.006 per /search request, a four-field enrichment costs roughly two to three cents per record, and you only pay when you actually enrich. Compare that to Clay at $167/month (Launch) to $446/month+ (Growth) for seat-based access to their provider waterfall, where you pay for the subscription whether you enrich or not. If you are already on a SERP or scraping stack, see how to migrate from SERP APIs to Linkup.

Bulk enrichment: the Tasks endpoint

Single-record enrichment is one call per field. When you need to enrich a list - 50 companies before a sales call, 200 accounts at pipeline review - calling /search synchronously in a loop adds latency. The Tasks endpoint solves this: submit up to 100 Search, Fetch, or Research calls in a single request, each running in parallel, and poll once for all results.

import os, httpx, time

api_key = os.environ["LINKUP_API_KEY"]

companies = ["Acme Robotics", "Zenith Labs", "Orbit Health"] # up to 100

# Submit batch — body is a direct array, params go under "input"
tasks = resp = httpx.post(
"https://api.linkup.so/v1/tasks",
headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
json=[
{
"type": "search",
"input": {
"q": f"latest funding round and total raised for {company}",
"depth": "standard",
"outputType": "sourcedAnswer",
}
}
for company in companies
]
).json()
task_ids = [t["id"] for t in tasks]

# Poll until all complete
while True:
results = [
httpx.get(
f"https://api.linkup.so/v1/tasks/{tid}",
headers={"Authorization": f"Bearer {api_key}"},
).json()
for tid in task_ids
]
if all(r["status"] == "completed" for r in results):
break
time.sleep(2)

for company, result in zip(companies, results):
print(company, "=>", result["output"]["answer"])

Tasks are billed at the same rate as direct endpoint calls - no batching surcharge. A 100-record enrichment at four fields each is 400 /search calls at $0.005–$0.006, roughly $2–2.50 total, running in parallel instead of sequentially.

Structured extraction: the Extract endpoint (closed beta)

For enrichment from structured sources - a company directory, a list of job postings, a table of funding rounds - the Extract endpoint goes further than search. You give it a seed URL and a natural-language description of the rows you want, and it returns structured NDJSON with one row per entity.

Example use case: pull all companies from an accelerator portfolio page, with their names, domains, and founding years, as structured rows your pipeline can ingest directly.

Extract is currently in closed beta. Request access if you need to enrich from structured web sources at scale.

Enriching sensitive records without leaking them to a data broker

The overlooked risk in enrichment is where your input records go. When you upload a customer list to a static enrichment vendor for matching, you hand that list to a data broker. For regulated teams that is a data governance event, not a convenience. Live-web enrichment with the right provider keeps the sensitive record on your side.

Linkup addresses this at the infrastructure layer:

• Zero Data Retention, available on all plans at no extra cost (requires activation in dashboard settings), so query inputs are not stored.
• SOC 2 Type II included on all plans, with GDPR compliance and EU data residency available.
• BYOC (Bring Your Own Cloud) as a custom enterprise option: Linkup deploys inside your Azure, AWS, or GCP, so enrichment queries never leave your VPC. For banks and insurers, Private Link routes traffic over a private network instead of the public internet.

This is the angle legacy CRM and data vendors avoid, because their business model is aggregating and reselling the very records you want to protect. If enrichment touches customer PII, read what enterprise teams should demand from an AI search API on compliance before you send a single row to a third party.

Start with a single-field enrichment against a company you know well, check the citations, then widen the field set. The API docs walk through all four endpoints - /search, /fetch, /research, and /tasks - and the free tier covers 4,000 queries so you can benchmark freshness against your current provider before switching.

FAQ

What is data enrichment for AI agents?

Data enrichment for AI agents is the process of adding verified attributes (headcount, funding, HQ, tech stack, recent news) to a raw record by having an agent fetch and cite each field from live web sources at query time, instead of copying values from a static database.

Why is real-time web enrichment better than a static database?

Static databases are snapshots that decay within months, and they return values without a source or a date. Real-time web enrichment fetches the current value per field and attaches a citation, so each attribute is fresh and auditable.

How is Linkup different from Clay or Apollo for enrichment?

Clay and Apollo aggregate data from multiple static providers and charge seat-based subscriptions (Clay from $167/month). Linkup enriches on demand from the live web at $0.005 to $0.006 per /search request, cites every field with a source URL, and never requires uploading your customer list to a third-party database.

How much does live data enrichment cost with Linkup?

Linkup /search is $0.005 to $0.006 per request, so a four-field company enrichment costs roughly two to three cents per record. The /research endpoint for deep company briefs is $0.25 to $2.50 per request, and the first 4,000 queries are free.

Can I enrich sensitive customer data without exposing it to a third party?

Yes. Linkup offers Zero Data Retention on all plans (no extra cost, activation required in dashboard settings), SOC 2 Type II on all plans, and BYOC as a custom enterprise option so enrichment queries run inside your own cloud and never leave your VPC.

Which endpoint should I use for enrichment?t

Four options, matched to the use case:

• /search - single-fact lookup, 1–3s, 92% SimpleQA (Standard) / 94% (Fast). Best for one field per record on demand.
• /research - deep multi-hop brief, 2–20 min async, 61% SealQA. Best for full company profiles with funding history, litigation, competitor context.
• /tasks - batch up to 100 /search, /fetch, or /research calls in parallel, same pricing. Best for enriching a list of records.
• /extract (closed beta) - seed URL + natural-language query → structured NDJSON rows. Best for pulling entities from directory or listing pages.

How does bulk enrichment work with Tasks?

Submit up to 100 enrichment requests (Search, Fetch, or Research) in a single POST to /tasks. Each runs in parallel and is billed at the same rate as a direct call. Poll /tasks/{id} for individual results. No batching surcharge.