The GTM engineering playbookwith 鹦鹉AI

Chapter 1 · ICPRun 30 Sep

Write your ICP as fields a provider can filter on

Your ICP is a sentence in a deck (“mid-market B2B companies that value data”), so every list is built from a different reading of it.

An agent cannot search for “values data”. It can search for country, headcount band, category, funding and technology. Anything you cannot turn into one of those becomes a check the agent runs on the rows afterwards (chapter 5), not a filter.

1. Split the ICP into filters (fields providers index) and checks (judgement on each row).
2. Write the buyer as a function (“marketing or growth”), not a title, so chapter 6 can search for it.
3. Keep the exclusions explicit: agencies, consumer apps, companies that were acquired.

ICP: the one we ran this playbook on
filters   country = US · employees = 51-200 · category = B2B and SaaS
checks    sells to sales, marketing, revenue or growth teams, or runs outbound itself
          would buy lead data, enrichment or prospecting APIs
buyer     function = marketing or growth (Head of Marketing, Head of Growth)
exclude   agencies, services firms, consumer apps, hardware, acquired companies

Using 鹦鹉AI, turn the ICP in icp.md into filters for two company-search providers.
Use their free count endpoints only. For each provider show the exact filter
object and the count it returns. Do not return any rows yet.

The rule If a part of the ICP cannot be a filter, it is a check, and it runs before any expensive step.

• Company research for agents
• Company data providers

Chapter 2 · Market sizeRun 30 Sep · free counts

Count the market for free before you pay for a single row

You buy a list, and only later learn the filter meant something different to the provider.

Several providers will count matches for free. Use that before anything else, and use two of them: the same ICP, expressed in each provider’s vocabulary, returns very different markets.

1. Count on two providers. A gap of 2× is normal; 100× means one filter is not doing what you think.
2. Pull 10 sample rows from each (cheap) and read them before you trust either number.
3. Size the adjacent bands too. Your real market is often the band next door.

Using 鹦鹉AI's free company-count endpoints, count my ICP on two providers and in the
headcount bands either side of it. Show every filter you used. If two counts differ by
more than 3x, tell me which filter is likely wrong before we pay for anything.

The rule No list is bought until two free counts roughly agree and ten sample rows look right.

• Company data providers
• Company research

Chapter 3 · RulesFrom the 23 Sep run

Write down the rules the agent follows, and who can stop it

The agent did something at volume that nobody would have approved one by one. “What makes a lead good enough? What evidence moves it forward? When can Claude act?” — LinkedIn

An agent acts at volume on whatever its instructions leave open. A written rule is still an instruction, not a guarantee, so it needs a number, a log and an owner.

icp_check      pass if fit >= 0.5, judged on the fields the row already has (one cheap enrichment first if it has too few)
people, email and news steps   only on rows that passed icp_check
drops          log every dropped row with its reason
email          send only to verifier = deliverable; unknown and catch-all go to a separate list
first_line     a person approves every first line before a sequence starts
spend_cap      stop and ask if a run will cost more than 3500 credits
owner          <name> can pause every sequence with one command

The rule Every threshold is a number in the prompt, every drop has a reason, and one person owns the stop.

• The run the thresholds came from

Chapter 4 · LookalikesRun 30 Sep · 140 credits

Lookalikes of your best accounts are candidates, not leads

A lookalike list feels on-target because the seeds were your best customers, so it skips the qualification step.

We took the three best-fit accounts from the 23 Sep run as seeds, asked for 25 lookalikes each in the same country, enriched every one and ran the same ICP check as chapter 5.

1. Seed with 3 to 5 accounts that actually closed, not the logos you wish you had.
2. Constrain the lookalike by country and size if the provider allows it; we only constrained country, and most results were smaller companies.
3. Enrich once, cheaply, so there are fields to judge; then enforce your size filter and run the same check as any other list.

Using 鹦鹉AI, find 25 lookalikes for each of these customer domains, same country and
same size band. Enrich each lookalike, then run my ICP check from rules.md on it.
Return only the ones that pass, with the reason each failed one was dropped.

The rule A lookalike goes through the same size filter and ICP check as any other row.

• Company research
• Company data providers

Chapter 5 · CostRun 23 Sep · 1631.01 credits

Qualify on the fields you already have before any expensive lookup

Credits burn faster than planned, because every finder, verifier and news call runs on every row. “I rarely enrich everything.” — r/gtmengineering

1. Judge every row on the fields the list already carries: description, industry, size, funding.
2. Drop the rows that fail and keep the reason next to them.
3. Run finders, verifiers and news only on what passed; use your own provider keys where you already pay.

Using 鹦鹉AI, list 50 US software companies with 51 to 200 staff that raised a Series A.
Before you find anyone, judge each company against my ICP from the list fields alone,
drop anything below 50%, and show me the price of every paid step before you run it.

The rule No expensive lookup (people, emails, news) runs on a row that has not passed the check. If a row lacks the fields to judge, one cheap enrichment comes first.

• The full lead-list run
• Lead enrichment
• The catalog

Chapter 6 · Buying committeeRun 30 Sep · 0 credits

Which people search APIs work inside an AI agent? Search by role, not “decision makers”

You email one senior person per account, and they are rarely the one who buys what you sell.

On 8 accounts that passed chapter 5, we asked a “decision makers” endpoint for the committee, then asked a routed people search for the buyer’s function directly.

1. Map the committee yourself: who pays, who champions, who uses it. Write each as a function.
2. Search each account for those functions, cheapest rows first, then pick the most senior per function.
3. Reveal contact details only for the people you will actually write to.

Using 鹦鹉AI, for each passed account search people by company domain with title
"marketing" and then "growth", at most 3 rows each and $0.20 per call. Return name,
title and a seniority guess. Do not reveal emails yet.

The rule Search for the buyer’s function. A “decision makers” list is a map of the company, not your committee.

• 鹦鹉AI for people search
• People data providers

Chapter 7 · Coverage and costBench 16 Sep

The cheapest way to run waterfall email enrichment from an AI agent

A vendor that works for someone else misses half of your market, and its per-row price hides it. “Half the emails bounce now.” — r/gtmengineering

Coverage depends on the segment, so the only honest test is your own rows. And the number that matters is cost per correct result, because a cheap provider that misses is expensive.

1. Take 20 to 50 rows of your real list and run them through a routed finder, which tries providers cheapest first; misses on per-success providers are not billed.
2. Record which provider found each email, and compare cost per correct, not price per row.
3. Widen adjacent titles before switching vendors; thin results are often a title filter.

Using 鹦鹉AI, find work emails for these 30 people with the routed email finder, then
verify each one. Report found, verified and cost per verified email, and which
provider found each address.

The rule Pick providers per segment from a test on your own rows, and re-test when the segment changes.

• The 292-person bench
• Email providers

Chapter 8 · DeliverabilityRun 23 SepStudy 7 Oct

Find, then verify, and keep the unknowns apart

“Valid” from a finder turns out to mean the domain accepts everything, and the bounces land on your sending domain. “Half our ‘valid’ contacts were catch-alls.” — r/gtmengineering

Those companies had passed a fit check first. In a separate sample built from title searches alone, the gap between found and deliverable was much wider. We asked the routed finder for each person’s email and verified every address it returned.

1. Verify as its own step and store the verdict next to the address. “Found” only means a finder returned an address; in the title-search sample above, 53% of found addresses verified deliverable.
2. Send only to deliverable. Unknown and catch-all go to a smaller, slower list, or nowhere. A blank catch-all field means unknown, not safe.
3. Watch bounces and complaints per sending domain.

The rule Only a verified-deliverable address goes into the main sequence.

• Lead enrichment
• Verifier and finder providers

Chapter 9 · SignalsRun 30 SepStudy 7 Oct

How to set up signal-based outbound: use signals you can open and date

The team has a signal feed and nobody acts on it. “Another alert feed everyone ignores?” — r/sales

An intent score cannot be checked, so reps discount it. A job post, a hire, a funding announcement or a post about the problem can be opened, dated and quoted in the first line.

person    Head of Growth, <company>
signal    posted 3 SDR roles in the last 30 days
why_now   building outbound now; the list will need data
source    https://…/careers/sdr
email     found and verified, or blank

Job changes: most contact records do not show the new job yet

A champion who moves to a new company is one of the best reasons to write. The trap is the data: in the first weeks after a move, most of the stored records we checked did not show the new company, so the email can go to an inbox they no longer read and the first line names the wrong company.

1. Keep only signals with a source link and a date, and drop closed job postings: more than half of those returned were closed.
2. Write one line of “why now” per signal. If you cannot, it is noise.
3. For a job change, confirm the new company with a live profile read before anything else. A second database helps little: when two both had the person, both missed the new job 64% of the time.
4. Give the feed an owner who triages it on a fixed day.

Using 鹦鹉AI, for each person in job-changes.csv read their live LinkedIn profile and
return current company, title and start date. Compare with the company in our CRM and
flag every mismatch. For mismatches only, find and verify an email at the new company.
Show the cost before you start.

The rule No source link, no signal. No “why now”, no outreach.

• 鹦鹉AI for lead signals
• Live profile reads
• Signal providers

Chapter 10 · PriorityRun 30 SepStudy 6 Oct

Score fit and timing together, then work the top tier first

Every account on the list gets the same sequence on the same day, whether or not anything is happening there.

Timing signals deserve suspicion before they get a weight. We tested a popular one: can you see a startup’s next round coming in its hiring or its news? Hiring could not tell them apart, and news ran the wrong way.

Study run 6 Oct 2026. Method: 57 US startups that announced a seed to Series B round between mid-August and early October 2026 (reference date: the announcement), against similar startups whose last round was in 2025 and for which our source showed no 2026 round (reference date: the median announcement date). 54 of those 61 had any job or news data. Job postings by first-seen date (posted date if missing) and news by found date, from 90 to 7 days before the reference date; the final week is left out so the round itself does not count. GTM roles cover sales, marketing, growth, revenue operations, business development, partnerships and customer success. “Hiring sped up” means two or more postings and more than in the 90 days before that. One source, with incomplete coverage, and an unmatched comparison: gaps of a few points are noise, and differences in company age and coverage could explain the news gap; we did not establish the cause. This is no evidence of a hiring signal, not proof that none exists.

1. Keep fit and timing as two numbers. Multiply them only to sort, never to decide.
2. Define tiers with numbers, and write them in rules.md.
3. Re-score weekly; timing decays, fit barely moves.
4. Before a timing signal gets a weight, check it on your own past deals: the share of wins that showed it against the share of losses. If it does not separate them, use it as a reason to write, not as a score. For funding, act on the announcement itself.

Using 鹦鹉AI, for each passed account pull open jobs and funding rounds. Count sales,
marketing and growth roles that are still open and were posted or first seen in the last
60 days. Tier A if fit >= 0.6 and 2+ such roles, B if 1+, else C. Show the tier, the
roles and their links.

Using 鹦鹉AI, take wins.csv and losses.csv (domain and close date). For each account pull
job postings and news from the 90 days before its close date. For each signal, report
the share of wins and the share of losses that showed it, and tell me which signals
separate them by more than 15 points. Show the cost before you start.

The rule Work tier A this week. Tier C gets nothing until a signal moves it.

• Lead signals
• Funding and jobs data

Chapter 11 · First lineRun 23 Sep

Let the agent research. Let a person write, or at least approve.

Thousands of emails go out and the replies do not come. “Cold outreaches always come off super robotic.” — r/sales

1. The agent gathers the reason to write, with its source.
2. Score the reason before anyone writes. Drop weak reasons; do not polish them.
3. A person writes or approves the first line. Read ten out loud before a sequence goes live.

The rule Nothing is sent that a person has not read.

• How the opener scoring works
• Company research

Chapter 12 · Sending and repliesMethod · not ours

Sending infrastructure and replies: what practitioners recommend

More time goes into inboxes and reply triage than into selling. “I was spending more time on infrastructure than on selling.” — r/b2bmarketing

Sending

Inboxes, domains and warm-up belong to your sending tool. The advice that held up across threads: conservative volume, and watching bounces and complaints per domain. Absolute rules (“never use links”) did not.

Replies

Classify every reply as interested, objection, referral or out-of-office; route it to the rep with the context the agent gathered; let a person approve the answer; and write the outcome back to the row (chapter 14).

The rule Replies are routed within a working day, and every outcome is written back to the row.

Chapter 13 · Inbound routingRun 30 Sep · 37.1 credits

Enrich an inbound lead from its email domain and route it in seconds

A demo request sits in a queue while someone looks the company up by hand.

A work email gives you a domain, and a domain gives you most of what routing needs. We enriched 20 company domains the way a form handler would.

1. On submit, enrich the company from the email domain, and store the result so a repeat never pays full price.
2. Route on two or three fields you trust: headcount band, country, industry. Send gaps to a person, not to nurture.
3. Answer tier-A inbound within the hour; it already told you the timing.

Using 鹦鹉AI, enrich acme.com from this inbound email domain. Return employee
count, location, industry and description, then route it by our rules: 51-200 staff in
the US and a software category goes to tier A. Show the cost first.

The rule Every inbound lead is enriched and routed before a person opens it.

• Company research
• Person enrichment
• Enrichment providers

Chapter 14 · AttributionRun 23 Sep

Tag every row, so you can tell which list, signal and provider paid off

Nobody can say which source produced last quarter’s pipeline. “Which positive replies became opportunities? Which meetings went nowhere, and why?” — LinkedIn

1. Store per row: source list, signal, the provider that found the email, the verifier’s verdict.
2. Monthly, join rows to replies, meetings and closed-won in the CRM.
3. Cut the sources and providers that never appear on the winning side of that join.

The rule A row without its source and provider does not go into a sequence.

• Download the tagged CSV

Chapter 15 · MetricsMethod, with our numbers

The metric stack: system health, performance, efficiency

The only number anyone tracks is “emails sent”.

The rule Review the three layers weekly, and change one rule at a time.

• All workflows with receipts

Chapter 16 · RolloutLessons from our runs

Roll automation out like software: shadow, small segment, human gate, then autonomy

An automation quietly stops, or quietly does the wrong thing, and nobody notices for days. “We usually find out days later.” — r/RevOps

1. Shadow. Run the play next to the manual process for a week and compare outputs row by row.
2. Small segment. Turn it on for one segment or one rep.
3. Human gate. A person approves each send until the error rate is known.
4. Autonomy only for the steps that passed; keep the gate on first lines.

What broke in our own runs

1. Alert on expected counts per stage per run, not on errors.
2. Store raw provider responses for a week, so a bad parse can be fixed without paying twice.
3. Set a spend cap per run and have the agent stop and ask above it.

The rule Nothing goes autonomous until it has run in shadow, and every stage reports its row count.

• All workflows with receipts
• Provider success rates in the catalog