Turn event attendees into pipeline: enrichment that runs in your browser | Retriever AI Blog

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9 data sources<br>LinkedIn, Crunchbase, Google Maps, and more

Works behind logins<br>Attendee lists, directories, and searches only your browser can see

Generated pipelines<br>A fresh waterfall written for every request

Approval-gated<br>Costs shown before any paid call runs

Agentic Dataset Enrichment demo<br>Watch Retriever research 500 attendees and contact the top 10.

2:45

You came back from the conference with 500 names. An attendee list you can open in a tab — and not much else.

You know what happens to those names today. Either it's 10–15 minutes each on LinkedIn, tab by tab, until you give up somewhere around forty. Or it's the export route: scrape to CSV, upload to an enrichment tool, map columns, buy a credit bundle, and pay for every lookup that comes back empty. By the time the outreach goes out, the event is cold.

We shipped something different: agentic dataset enrichment, running right in your browser — on the pages you're already logged into.

The demo

You're looking at 500 guests on a Luma event page — a list that only exists behind your login, on a page server-side enrichment tools will never see. You tell Retriever:

text<br>Enrich these guests with LinkedIn profiles and work emails,<br>score them against my ICP, and reach out to the top 10.

Retriever pulls all 500 guests off the page, matches them against preindexed people, company, and contact datasets, fills in LinkedIn profiles and work emails, scores everyone for fit, and sends connection requests plus emails to the top 10.

500 attendees researched. Top 10 contacted. One prompt.

No export. No column mapping. No spreadsheet cleanup. Matched records stream into a Google Sheet as they land, and every message can mention the event you were both at.

What changed

Retriever could already research a prospect the way you do it — open the profile, read it, move on. That's why prospecting eats a week.

Now it looks up hundreds of people and companies in one shot against preindexed datasets, and searches the live web only for what's still missing. Under the hood, Retriever writes a JavaScript enrichment plan against the rtrvr.* harness, shows you the estimated cost, and — after you approve — executes it in a sandbox in your extension.

It isn't picking from someone's prebuilt waterfall template. It writes your waterfall, for this list, this request, this page — and you can read every line it runs.

The mechanics

For the GTM engineers: this is the actual program it writes. The new harness helper, rtrvr.enrich, is wired into our code-as-plan architecture and backed by Bright Data's dataset marketplace:

Instant lookup — filterable queries over the LinkedIn people, LinkedIn contact-enriched (work emails / phones), and LinkedIn company datasets. The agent composes typed filters (=, in, includes, nested and/or groups) from your plain-English ask.

Live scrape — fresh scrape-by-URL for Crunchbase, Instagram, Amazon, Zillow, Indeed, and Google Maps, up to 20 URLs per call.

Free discovery — listing datasets and inspecting their field schemas costs nothing, so the agent explores before it spends.

The part that matters: everything around the paid lookup is deterministic code, not model turns. Key extraction, batching, retries, joins, dedup, projection — a for-loop should not cost tokens, and here it doesn't:

js<br>// Build lookup keys from your sheet rows — the LinkedIn profile slug, not the URL.<br>const ids = rows<br>.map(r => r.linkedinUrl?.split('/in/')[1]?.split(/[/?#]/)[0].toLowerCase())<br>.filter(Boolean);

// Estimate cost and get explicit approval before any paid call.<br>const estCredits = Math.ceil(ids.length * 0.25);<br>const { answers } = await rtrvr.askUser({<br>questions: [{<br>key: 'enrichApproval',<br>query: `Enrich ${ids.length} LinkedIn profiles for ~${estCredits} credits?`,<br>choices: ['Yes', 'No'],<br>}],<br>});<br>if (answers.enrichApproval !== 'Yes') return { summary: 'Enrichment declined.' };

// One instant lookup — batching...