Three plays, one system, one number.
The number is submission volume: more of the carrier's 40,000 brokerage offices sending risk, and the brokers who already do sending more of it. Three broker-facing plays, built to feed each other, each with a working tool behind it. Click around instead of taking my word for it.
AI visibility
Clips and cited answers reach a broker who has never heard of the carrier.
Targeting
The same named offices, made familiar on LinkedIn and by email before the underwriter's touch.
Signal Engine
Brokers, signals, drafts, briefs: the moment a signal fires.
How the three are orchestrated
They are one system, not three campaigns, and the order is the argument.
Play 3 earns the attention
A clip or a cited answer reaches a broker who has never heard of the carrier. Cheap, slow, compounding.
Play 2 makes it familiar
The same named offices see paid placement and a marketing email flight, and site visitors get retargeted. Days, not months.
Play 1 converts the moment
A signal fires at that firm and the underwriter's email lands into recognition instead of cold. This is where a submission actually happens.
Everything feeds back
Every touch from any play is logged as first-party intent on the broker record, which raises their priority and re-orders tomorrow's work.
Signal-led broker outbound
Score the broker universe, watch for the moments a broker is about to place risk, and have agents write the first email for the underwriter.
New submitting offices, and first submissions from brokers who have never sent one.
A broker replies to an underwriter, not a marketer, when the email lands the week something changed at their firm and names it. If reply rate clears 20%, the thesis holds.
Third-party: project awards, permits, practice hires, carrier pullbacks. First-party: logged engagement. Lifecycle and outbound email, sent from the underwriter's own mailbox.
Acquisition, then first-to-repeat: open offices that never submitted, then walk them toward the 7-plus pace where quote rate jumps ~20 points. The agent drafts, the underwriter approves and sends. Marketing owns the signal book.
Every construction and energy practice title at the 18 appointed brokerages, pulled from Apollo and scored on four arguable axes: fit, timing, access, intent. A sweep reads the trade press for moments that mean risk is about to move. When a broker at a flagged firm floats to the top, the agent writes the first touch in the underwriter's voice and waits for approval. The one-pager is here too: type a name, get the brief.
The universe finds offices that never sent anything; the signals tell the underwriter which week to call. I ran the same signal-led outbound for my own job search, and it is how this project started.
Running today: 450 real brokers and 89 real signals, clickable in the tool. Full-strength permit and project data is a licensed upgrade, priced in the budget.
The signal selection itself is an argument, not a feed: nine types ranked into act, aim, and context tiers, each with its sourcing, criteria, and confidence written down, and loss events deliberately excluded from outreach. Read the signal thesis →
Open the funnel math: 21 submissions a month at 60 emails a day, assumptions editable +
Adjust the assumptions +Adjust the assumptions −
Per month, from this play alone, before any existing broker sends more. Change any input; the math is live. The reply and meeting rates are from my own outbound; the submission and indication rates are assumptions I want to replace with your numbers on the call.
Apollo about $200 a month at 2,000 brokers. Claude under $50. Mail goes through whatever you already use.
Kill rule. Under 20% reply rate on signal-led email after 300 sends, I change the copy, not the target.
Targeting
The scored universe is already an audience. One list, two channels: LinkedIn makes the office familiar, and a marketing email flight through AgentMail reaches the brokers with a verified address, so the underwriter's touch lands into recognition instead of cold.
Be already familiar at the offices Play 1 is about to email, and capture the brokers who visit the site without ever replying.
Almost no carrier here runs a modern paid stack, so a small spend against named offices is cheap and uncrowded. If targeted offices don't out-reply the control, it stops.
First-party: site visitors and content viewers, retargetable. Third-party: Play 1's signals, used to time a flight. Paid media plus marketing email. LinkedIn company and title targeting inside named brokerages, and an email flight through AgentMail at the 271 brokers with a verified address.
Acquisition support: makes the office familiar before Play 1's email lands. Claims no stage of its own. Marketing end to end; underwriters only approve the target list.
A targeting cockpit over the scored universe. Filter by practice, state, brokerage, title, fresh signal in the last 30 days, or email on file, or describe the audience in a sentence and the model sets the filters for you. The result feeds two channels from one list: LinkedIn company and title exports, and an email flight through AgentMail at its reachable half, every send behind the same approval gate as Play 1. Spend and outcomes land in the Spend tab, so cost per submitting office is a real number.
Indirectly, and I'll say so: brokers do not click an ad and send a risk. Paid makes the underwriter's email the second time an office has seen the name that week. Smallest bet on the board, pointed at a list you can name.
Feasible this week: the cockpit runs on data already here, no credits. LinkedIn needs an ads account and $500 to $1,500 a month; the AgentMail flight is a day of wiring.
$500 to $1,500 a month to start, which is small enough to kill without a meeting. No agency.
Kill rule. 90 days. If reply rate at targeted offices does not beat the control, the budget moves into Play 1's data.
Broker-facing content and AI visibility
One recorded hour a month with the founders becomes a month of clips, and the same material becomes the pages an LLM cites when a broker asks it who writes this risk.
Be the market a broker recognizes before the email lands, and the market an LLM names when a broker asks where to place a risk.
Brokers research markets the way everyone now does: by asking a model. Legacy carriers carry twenty years of sentiment, the carrier carries none, and nobody else is competing for the answers.
First-party. Engagement with a clip or a page is a signal that raises that broker's priority in Play 1. Video and social, plus targeted answer-engine optimization.
Deepening: engagement feeds the Intent axis and climbs brokers toward the 7-plus pace. Also the front door for brokers who discover markets by asking a model. Marketing produces; the founders give one recorded hour a month; the audit runs itself. Underwriters spend zero time here.
Targeted AEO: an audit asks every answer engine the questions brokers actually ask, records who gets named and cited, and drafts the page that closes each gap, so the before and after of paying to be seen is measured on the same audit. It runs today on five engines; six more are written and start with a key. The clip side turns one recorded founder hour into a month of posts.
Slowest of the three and I'd fund it last, but it compounds: a clip costs nothing to distribute again, and a page that earns a citation keeps earning it. I ran this playbook once and got 1.5 million views on under $1,000. The mechanics translate; the content changes.
The audit is live on five engines today, so the baseline is real. Clips need one founder hour a month and an editor.
Prompts vary infinitely, so the program is ranked, not sprayed: comparison prompts first, objection prompts second, shortlist prompts last, argued by who is asking, how close they are to a submission, and how winnable the answer is. Read the prompt ranking →
The five clips I'd cut first, the posting cadence, and the tracking live on the Social page →
One recorded founder hour becomes a month of posts. About $30 a month in model calls across engines. An editor at $1,500 a month, or me. No paid distribution until a clip earns it.
Kill rule. Six pages live, 90 days, under 15 points of share-of-voice movement. Stop.
Ninety days, one number
The budget below is the full amount if every optional line turns on. The realistic first month is under $400, because the AEO tool and paid video wait for proof.
| Apollo (seats + credits for ~2,000 brokers)Search is free; credits only on reveal. | 01 | $200 |
| Claude and other model callsScoring, drafts, briefs, and the audit across engines. Nothing runs on a timer. | 01 03 | $80 |
| Email sending and trackingWhatever the underwriters already use, plus tracking. | 01 | $100 |
| LinkedIn ABM against the exported audienceStarts small with a held-out control so the lift is measurable. Kill at 90 days if it does not beat the control. | 02 | $750 |
| AEO measurement (Profound), optionalOnly if the first 30 days show movement. Otherwise $0. The audit already uses Profound's metric definitions, so the baseline carries over with zero lost history. | 03 | $500 |
| Video editor (contractor) or my timeHalf a day a month. Captions and scheduling under $100. | 03 | $1,500 |
| Paid distribution on the two best clipsStarts day 61, targeted at the same broker audience Play 2 builds. | 03 | $500 |
| Everything on | $3,630 | |
| Realistic month one | $380 |
No agency retainers. No new headcount. Tooling and one contractor I can manage.
New submitting offices
Offices at appointed firms sending a first risk.
First submissions from never-submitted brokers
From the funnel math at 60 emails a day.
Brokers on a 7+ submissions-a-year pace
The carrier's own stat: at 7+ a year, quote rate jumps ~20 points and hit rate 10-15. The deeper half's whole target.
Reply rate, signal-led email
Below 20% and the copy changes, not the target.
Share of voice on comparison prompts
Claude, Llama now; ChatGPT, Gemini, Perplexity with keys.
Brokers engaging with content, per month
Counted against the Play 1 universe, and logged as first-party intent on the broker.
Hours of the marketing lead's week this costs
Review the draft queue, approve pages, say yes to clips.
| Metric | 30 | 60 | 90 |
|---|---|---|---|
| New submitting officesOffices at appointed firms sending a first risk. | baseline | +5 | +12 |
| First submissions from never-submitted brokersFrom the funnel math at 60 emails a day. | baseline | 10 | 25 |
| Brokers on a 7+ submissions-a-year paceThe carrier's own stat: at 7+ a year, quote rate jumps ~20 points and hit rate 10-15. The deeper half's whole target. | baseline | +5 | +15 |
| Reply rate, signal-led emailBelow 20% and the copy changes, not the target. | 20% | 22% | 25% |
| Share of voice on comparison promptsClaude, Llama now; ChatGPT, Gemini, Perplexity with keys. | baseline by engine | +8 pts | +15 pts or kill |
| Brokers engaging with content, per monthCounted against the Play 1 universe, and logged as first-party intent on the broker. | first clips live | 100 | 250 |
| Hours of the marketing lead's week this costsReview the draft queue, approve pages, say yes to clips. | 3 | 2 | 2 |
Targets are mine, built from the funnel math above. They become real against the carrier's own baseline.
What I would ask first
I guessed at a lot of numbers to build this. These four answers would change the plan the most.
What's the submission baseline? Monthly count, active offices, and brokers who have sent at least one. Where does it live today?
What does the broker data look like right now: appointments by office, a CRM, spreadsheets? How is underwriter outreach tracked, if at all?
Is there any paid budget today, and what would you need to see in 90 days to keep funding it?
Which risk classes do you want more of this half, and which are you fine losing on price?
How this was built: Next.js on Vercel, Supabase, Claude for the agents, a VPS for the scheduled sweeps, all in a week of evenings. The stack details live on the stack page, because the argument here is the plays, not the plumbing.
What this is, honestly: an architecture MVP. The shape of the system works end to end with real data, real drafts, and real audits, but the production build is a longer road. The biggest piece is the always-on half: cloud-hosted agents that run the sweeps, watch the monitors, and queue drafts on their own schedule instead of when someone clicks, plus the CRM and mailbox wiring and the hardening an underwriter-facing approval gate deserves. That is weeks of focused work, and this site is the argument for doing it.