Six concrete day-one sequences.

Day 1: connect the brand and configure 10 prompts. Sign up, paste the brand domain, add 10 buyer-questions in the shape your sophisticated AI buyers actually type into ChatGPT or Claude. Examples: "best agent framework for production rag", "ai agent infrastructure that handles scaling", "langchain alternative for enterprise". Name two competitors. The first tracker run starts within the hour.

Day 2: review the first forensics pass. The overnight forensics pass tags every score event with a cause code. Open the Citation-Lift suggestions sorted by AEO Value Score. The top three are the prompts where one specific content fix would lift you the most.

Day 3: ship the first Citation-Lift fix. Click the top suggestion. Copy the paste-ready block via the CMS Format Picker (Markdown for a static site, JSON-LD FAQ for the most engine-quotable shape). Paste into your blog or product page. Time investment: under 15 minutes.

Week 1: ship two more fixes and post the receipt. Run the loop twice more in week one. Then post a build-in-public tweet showing the score delta with screenshots of the engines now citing you. Use the Founder-led Motion persona on the Mentioned AI social tab to draft the post in your voice.

Day 30: open the Backlinks Initiative. By day 30 the Backlinks Initiative has 20+ opportunities ranked by AEO value. Run a batch of 10 pitch packages, review the per-row contact guesses, send the top 5 via the channel that fits (email, LinkedIn DM, X reply). Expected outcome: 1-2 backlinks convert from opportunity to active within 21 days.

Week 1: baseline against the SEO incumbent. Configure 20 prompts that match your existing SEO keyword set. Export the Mention Score per engine and lay it side-by-side with your Ahrefs or Semrush rankings. The asymmetry between Google rank and ChatGPT citation rate is the chart that convinces your CFO this is a different channel.

Weeks 2-4: ship the top 6 Citation-Lift fixes. Have the content team take six suggestions per week from the Content tab. Each shipped suggestion gets the Wave 41ff posted trail filled in with the live URL. By week 4 the audit trail of "what we shipped, when, what engine it lifted" is the artifact for the board deck.

Week 5: pull the per-engine CSV export. Export the per-engine per-prompt citation rate over the 30-day period. The CSV is the data-asset your CFO and board want. Drop it into the board deck with one slide: "AI answer engine citation rate, before and after."

Week 6: run the Backlinks Initiative against the top 10 competitors. Open the Backlinks tab, sort by AEO Value Score. Run a batch of 15 pitch packages. Have the content team review and send through their existing outreach tooling. Expected close rate: 12-18% of pitches convert to mentioned-in-page within 30 days.

Weeks 7-8: present to the board. Present the before/after CSV, the audit trail of shipped fixes, the backlink conversion rate, and the projected next-quarter Mention Score trajectory. The Five-Pillar Operating Score is the rubric for the next quarter's investment thesis.

Hour 1 per client: discovery call template. Run a 30-minute discovery to gather the client's top 15 buyer-questions, three named competitors, the brand voice (one short style guide), and the canonical domain. The Mentioned Context Studio takes all of this as paste-in inputs.

Hour 2 per client: configure and trigger first tracker run. Sign in to the agency workspace, click "Add brand", paste the discovery output into the Context Studio extract pipeline, configure the prompts, and trigger the tracker. The first run completes within the hour; the first forensics pass lands overnight.

Day 2 per client: review and queue the first 3 Citation-Lift fixes. For each client, sort the Content tab by AEO Value Score and select the top 3. The agency operator reviews, lightly edits to fit the client's voice (the Context Studio voice traits keep the draft close), and copies the JSON-LD FAQ format for the client's blog.

Week one delivery: ship + post + report. Ship the 3 fixes per client. Use the CMS Format Picker to match each client's CMS (most agencies have a Webflow client, a WordPress client, a Notion client, a Framer client in the same book). Post the receipt screenshot on the agency's X handle. Generate the first weekly KPI brief for each client.

Month one: pricing math. 6 brands × $49/mo per-brand pricing = $294/mo platform cost. Charge the client $400-$800/mo as a managed-AEO retainer. Margin per client: $100-$750/mo. Agency overhead per client: 2-4 hours per month at scale.

Step 1: configure the 30 prompts that map to revenue. Pick 30 prompts where the citation rate correlates with closed-won revenue (not vanity volume). Examples: "best [your category] for [ICP segment]", "[competitor] alternative for [use case]", "how to [your buyer's job-to-be-done]". The Mentioned Adaptive Query Framework can suggest the right shapes from your existing prompt set.

Step 2: pull 30 days of per-engine data. After 30 days of tracking, export per-engine per-prompt citation rate. Note the engines where the brand is strongest (use these as the positive narrative) and weakest (use these as the investment ask).

Step 3: build the share-of-voice slide. One slide: brand vs top three competitors, share of voice across the 30 prompts, broken out per engine. This is the same shape SEO teams used to show vs Ahrefs share-of-voice, but measured against AI answer engines. Visually identical to what the board has seen before; semantically different.

Step 4: build the lift-per-fix slide. Show the Citation-Lift suggestions shipped over the period, the verified Mention Score delta per fix, and the cost per fix in founder time. Average: under 30 minutes per fix, 1-3 point lift per fix. The unit economics of AEO are cleaner than paid acquisition.

Step 5: ask for the next quarter's budget. Ask for the Scale plan ($499/mo, 10 brands or 10x the current prompt set) plus a dedicated AEO operator role. The data on the prior two slides is the case. The Five-Pillar Operating Framework is the rubric for measuring quarterly progress.

Step 1: pick the 12 portfolio companies most ready for AEO. AEO benefit shows fastest for companies with a live product, real buyer-questions, and a content footprint of any size. Skip pre-launch and skip companies with no content at all. Twelve is the typical Seed or Series A portfolio sample for one cohort.

Step 2: sponsor 12 Starter seats and send the onboarding kit. Sponsor 12 × $99/mo Starter seats. Send each founder the onboarding kit: one Loom from the platform team, the 10-question prompt template, and a calendar link for a 15-minute setup nudge.

Step 3: configure each company's Context Studio in one batch. Have the platform team batch-configure the 12 Context Studios using the discovery output the founders fill in. 30 minutes per company × 12 = one focused day.

Step 4: monthly Mention Score review across the portfolio. The platform team pulls the monthly Mention Score across the 12 companies. The portfolio view is the asset: which companies are lifting, which are flat, which need a nudge. Use it as the agenda for the monthly portfolio sync.

Step 5: share the cohort lift back to LPs in the quarterly report. Average Mention Score lift across the cohort after 90 days is the LP-facing artifact. Founders ship fixes, the cohort lifts, the platform team has measurable proof the value-add is moving a real metric.

Morning · 07:30 ET · post the overnight score delta. Open the Mention Score dashboard. If the score moved, post the delta as a 280-character X tweet using the Founder-led Motion persona. Example: "Mention Score dipped 3 on ChatGPT overnight for the prompt aeo platform under 200. Profound surfaced on a long-tail variant. Drafting the fix now."

Mid-morning · 09:00 ET · post the Citation-Lift draft. Open the top Citation-Lift suggestion. Screenshot the paste-ready block and post the draft as a follow-up tweet. Founders share the WORK, not the announcement. The content IS the proof.

Late morning · 11:00 ET · ship and post the timestamp. Paste the fix into the live page via the CMS Format Picker. Post the third tweet: "Pasted at 09:14 via the JSON-LD FAQ format. 11 minutes of founder time." Use the Wave 41ff Mark Posted button to capture the live URL into the audit trail.

Next morning · verification. When the tracker re-runs and shows the lift, post the fourth tweet of the loop: "Last morning's fix recovered 2 of 3 points. Here is the cell that flipped." Screenshot the engine matrix showing the now-cited cell.

Weekly · Friday afternoon · publish the cohort comparison. Use the Brand-led Motion persona to draft a Friday post comparing the Mention Score across Mentioned AI, Profound, Jam, and the spreadsheet-DIY for the same 5 category prompts. Link to the public score page at /mentioned/score. Honest data, honest competitors named, no spin.

Day 1: confirm the citation engine has run for at least 7 days. The Demand pipeline draws from your last 30 days of citation answers. Open the brand workspace and check that the Track tab shows daily score deltas for at least the past week. If you just signed up, give the tracker 5-7 days to accumulate enough source URLs across your tracked prompts before running outbound. The richer the citation dataset, the more candidates the pipeline will surface.

Day 2: tighten the ICP context (this is the calibration that matters). Open Context tab. Confirm icp_summary names the exact buyer (eg "founders shipping LLM agent products at $500K-$5M ARR"), pain_points lists 4-8 verbatim phrases the buyer would say out loud, common_questions captures 5-10 buyer-question phrasings, and icp_titles enumerates the 4-6 job titles that own the buying decision. Mentioned uses ALL of this to filter sourced candidates via deep LLM grounding · weak context produces weak leads, sharp context produces sharp leads.

Day 3: click Find leads. Open Demand tab. Click the green Find leads button in the header. The pipeline runs: source ~25 citation-derived candidate domains, scrape each candidate's team and bio pages, extract decision-makers, run the deep-ICP grounder, draft citation-anchored outreach for the top 12 qualified people. Typical run time 30-60 seconds, $0.04-$0.08 LLM cost.

Day 4: triage the drafted lane. Open the Drafted lane in the kanban. Each card shows: company name + domain, decision-maker name + title + LinkedIn, deep-ICP fit verdict (strong / plausible / weak) with verbatim reasoning quoting your pain points and common questions, drafted email + LinkedIn DM referencing the EXACT prompt the company researched on the EXACT engine. Open each card, edit the draft to match your voice, click Send (or copy-paste into your sending tool).

Day 5-7: move replies through the kanban. When a reply lands, drag the card to the Replied lane. The status classifier surfaces the next action automatically: reply_now (the buyer asked a question), follow_up (the buyer engaged but did not commit), mark_outcome (the lead is qualified or disqualified). The kanban also surfaces awaiting_reply and send_now buckets so you always know what to do this hour.

Weekly cadence: rerun Find leads every Monday. The pipeline is bounded (25 candidates per run, 12 outreach drafts per run) to keep cost and quality calibrated. Run it weekly to keep the kanban fresh. As your citation tracking grows, more candidates surface. The compounding effect: month 1 you get 12 leads/week, by month 3 the corpus has matured and you see 25+/week with sharper ICP fit because the deep-grounder has more brand context to work with.