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Qualify a company list against your ICP before you pay to enrich it

The same list, cut down to the companies that actually fit your desk, with a written reason for every company that was dropped, before a single enrichment credit is spent.

use when
a search returned a few hundred companies and you are about to spend real money finding contacts at all of them
starts from
A list

The prompt

Paste it into Claude Code or the Claude desktop app with Hyreflow connected. The first line tells your agent to use Hyreflow, so it reads the play, asks before it spends anything, and hands the work back to you.

paste this into Claude
Use hyreflow and qualify a company list against my ideal client before we
spend anything on contacts.

I have <N> companies from <WHERE THEY CAME FROM>.

My ideal client: <PASTE IT, OR POINT AT THE FILE>

Do it in three passes:
1. Hard filters first, using data we already have. Size band, industry,
   geography, and my exclusions. This should cost nothing.
2. Then your own judgement on the survivors. Does this company actually
   look like the ones I win, beyond the firmographics lining up.
3. Only for the ones you genuinely cannot call, go and look at their site
   and come back with evidence.

Give me the passes and the rejects separately, with a reason on every
rejected row so I can argue with it.

Tell me how many companies made it and what enriching only those will cost
compared with enriching the whole list.

Replace every <PLACEHOLDER> with your own detail. Everything else can stay as written.

What you need first

  • A list of companies from any source, including one you uploaded
  • A description of your ideal client, written down

Tools it can reach for

The agent picks per step from what your workspace has. Nothing here is required by name.

What happens when you run it

Free steps are marked free. Anything that spends credits is marked, and the agent asks before the first paid run of any size.

  1. 1

    Read the profile

    free

    Industry, company type, stage and size, geography and your explicit exclusions, plus the qualitative signals: what the companies you actually win have in common.

  2. 2

    Apply the hard filters

    free

    Run against data the sourcing step already returned. Most search results carry industry, size and location, so this pass usually costs nothing and removes most of the list.

  3. 3

    Fill only decisive gaps

    credits

    Where a company is missing the one field that would decide it, a single cheap company lookup fills it. This is the only routinely paid part, and it runs on individual rows rather than the whole list.

  4. 4

    Judge the survivors

    free

    The qualitative pass: does this company look like your clients in the ways a filter cannot express. Assigned as a tier or a score with a one-line reason. Free, because it is reasoning rather than a lookup.

  5. 5

    Verify the genuinely unclear ones

    credits

    For borderline companies only, the agent reads the company's own site and the public record and returns evidence instead of a guess.

  6. 6

    Split the list and price it

    free

    Passes and rejects come back separately, every rejection carrying its reason, alongside what enriching the qualified subset costs against enriching everything.

What a run costs

Credits are spent per company the play actually works, and a lookup that finds nothing usually costs nothing. The two figures are the run where the first provider answers and the run where every lookup walks its full chain.

companiesif the first provider answersif every lookup walks the chain
25under $17.8 credits$110 credits
100$3.131 credits$4.141 credits
500$16160 credits$21210 credits
1,000$31310 credits$41410 credits

Free before anything is charged

  • Read the profile
  • Apply the hard filters
  • Judge the survivors
  • Split the list and price it

What moves the number

  • How many companies survive the free filters. Everything dropped before the paid steps costs nothing.
  • Providers you connect with your own key. Those calls bill your account, not your credits.

An estimate, not a quote, priced at the volume credit rate. Your agent sizes the run against your own workspace and tells you what it will cost before it spends anything.

The step that decides what everything else costs

Sourcing is cheap and contact enrichment is not. That single fact drives the economics of an automated desk, and it is why the order of operations matters more than the choice of provider.

A search returns three hundred companies. Enriching all of them means paying to find contacts at the two hundred and forty that were never going to be your clients. Qualifying first is free, and it cuts the bill by roughly the proportion of the list that does not fit.

What you get back

Two lists, not one score. The companies that pass and the companies that do not, kept apart, so the qualified set is ready to hand straight to an enrichment play and the rejected set stays available to argue with.

A reason on every rejection. Which part of the profile the company failed, in a line. This is what makes the gate correctable: a filter that is throwing out companies you would have taken is visible in the reasons long before it is visible in your pipeline.

A tier and a one-line justification on every company that passes. The judgement pass says why this one resembles your clients, so the ranking is something you can check rather than a number to trust.

The bill you avoided. What contact enrichment would have cost on the whole list, against what it costs on the qualified subset. That number is the argument for running the gate at all.

Three funnels, narrowing

Hard filters run on data you already have. Size, industry, geography, exclusions. Free, and they remove most of the list.

Judgement runs on what survives. This is the part a filter cannot do: whether the company actually resembles the ones you win, which is often about their stage, their structure or how they buy rather than their industry code.

Verification runs only where the answer is genuinely unclear, and it is the only expensive part. It goes and looks at the company rather than guessing.

Each funnel is cheaper per company than the one after it and removes more of the list, which is what keeps the whole thing proportionate.

Why the profile has to be written down

An ideal client profile that lives in someone's head cannot be applied consistently and cannot be argued with. Written down, it becomes something you can point a gate at, correct when it drops a company you know is good, and hand to a colleague.

Derive it from the clients you actually make money on rather than the ones you would like to. The gate is only as good as that definition.

Where this goes wrong

Skipping it because the list looks fine. Lists always look fine. The rejects are what tells you whether it was.

Filtering on a currently open role. Whether a company is hiring today is timing, not fit. A company that matches your profile and has nothing open is still a target account.

Verifying everything. Verification is the paid pass. Running it across the full list instead of the borderline cases converts a nearly free gate into an expensive one for very little accuracy gained.

Questions

Why is this a separate play rather than part of the others?

Because it belongs in front of all of them. Any sourcing step can hand its output to this gate, including a list you built by hand or exported from somewhere else. Keeping it separate means it is one thing to get right rather than the same logic reimplemented in six places.

How much does qualification itself cost?

Close to nothing in the normal case. The hard filters run on data you already have, and the judgement pass is reasoning by the agent you are already talking to rather than a metered service call. The only spend is filling a decisive missing field on individual rows, and verifying the genuinely borderline ones.

Why keep the rejected companies?

So you can check the work. A gate that silently discards two hundred companies is asking for a lot of trust. With the reason on every rejected row you can spot a filter that is too aggressive, notice a company you know is a good fit, and correct the profile rather than the output.

What if I have not written down an ideal client profile?

Then write one, or derive it. The most reliable route is your own best clients: what do the five you make most margin on have in common. That derived version is usually more accurate than the aspirational one people write for a website, and this gate is only as good as the definition behind it.

Does this work on candidates as well as companies?

This one is for companies. Candidate qualification against a job spec is the same idea applied to people, and it is built into the sourcing plays: score against the spec for free, then only pay for contact details on the ones that pass.

Run this play on your own desk

Connect hyreflow to Claude or ChatGPT, paste the prompt above, and fill in the blanks. By default it shows you the cost before it spends.