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GTM Engineering

Opulent x Clodo: People Search into Reviewed Pipelines

Exact-persona search wrapped into reusable workflows for expert sourcing, recruiting, research verification, and precision GTM.

Integrations
AuthorOpulent
CategoryGTM Engineering
FeaturesIntegrations
start it with one message
Find people matching this exact persona with Clodo: [describe the persona]. Enrich each with verified detail, review the list against our criteria before anything leaves, package the reviewed set into our CRM, and note which search terms produced the best fits.
Run this in OpulentCopy it, swap the names for your own, and send it.
connected systems
ApolloFind, enrich, and engage B2B prospects with Apollo
AttioManage CRM records, deals, tasks, notes, and meetings with Attio
ClayFind, enrich, and research contacts and companies
SumbleSearch and enrich organizations, jobs, and people for B2B sales intelligence
step 1

Wrap Clodo people search in a reviewable workflow

Connect Clodo for exact-persona people search, Apollo and Sumble for enrichment, and Attio as the record store. Clodo finds and reaches the right humans; Opulent wraps that search into a workflow you can rerun and review.

Capture the four moves (target, source, package, learn) in a playbook (a reusable, named set of steps), so expert sourcing, recruiting, and precision GTM all run the same reviewed shape instead of ad-hoc searches.

Playbook: !people-search

Given a persona description:
1. Target: turn the ask into a precise Clodo query, role, domain,
   signal (e.g., "former FDA reviewers now in medtech startups").
2. Source: run the search; pull the real people who match.
3. Enrich: add verified detail with Apollo and Sumble; drop
   anyone who fails the criteria.
4. Package: write the reviewed set into Attio with the reason each
   person matched.
5. Learn: record which query terms produced the best fits.
Tip

Write the disqualifiers into the playbook, not just the target. "Exclude current competitors' employees" saves more review time than any amount of tuning the positive criteria.

step 2

Run it per persona, on demand or on repeat

Most searches are on-demand: describe the persona and send it. Paste the message from the top of this page with the persona filled in, and Opulent runs the target-source-package-learn loop.

For a standing need (an always-open role, an ongoing expert network) wrap the playbook in a schedule so fresh matches arrive weekly without re-describing the persona each time.

The sharp edge: a loose persona returns a broad list that looks productive but wastes review. Tighten the query with a distinguishing signal before widening it, so the reviewed set stays small and high-fit.

step 3

Watch one persona become a reviewed list

Take an expert-sourcing ask for a due-diligence project:

Opulent reviews against the criteria before packaging, so what reaches the CRM is a vetted set, not raw search output.

Persona: former payments-fraud leads, now advising fintechs

Run actions:
- Target: built a Clodo query for ex-fraud leaders at named
  processors who now hold advisor or founder roles.
- Source: returned 47 candidate profiles.
- Enrich (Apollo, Sumble): verified current role and reachability;
  dropped 19 who had moved out of the domain.
- Reviewed the 28 against criteria, kept 21 clear fits.
- Packaged 21 into Attio with the match reason and a proof link.
- Learned: "fraud lead" + "advisor" beat generic "risk" by 3x fit.
step 4

What lands, reviewed

When the run finishes, you have a vetted people list ready to act on, each entry verifiable:

Reviewed person records in Attio, each with the reason they matched and a proof link. A dropped-candidate log showing who was filtered and why, so the review is auditable. A note on which query terms produced the best fits, ready to reuse.

The dropped-candidate log is the proof-of-work: it shows the list was reviewed against your criteria, not just scraped, which is what makes it safe to hand to a rep or a hiring manager.

step 5

Sharpen the search

The learn step feeds the next run: Opulent writes the query terms that produced the best fits into memory (the notes a run recalls next time), so each persona search starts from what already worked.

When reviewers keep rejecting a certain profile, add the disqualifier to !people-search so it never reaches review again.

The natural chain: hand the reviewed people list to Stand Up a CRM and Outreach Pipeline in One Run so vetted personas become structured records and sequences in one pass.