Telesales & Lead Conversion

Human Intelligence at the Point of Conversion.

AI Amplification at the Point of Scale.

RobotAgency’s Telesales & Lead Conversion system is designed to close revenue efficiently, not just generate leads. It combines trained human telesalers, a referral-based growth structure, and AI-assisted conversion intelligence, creating a scalable and performance-aligned sales engine.

This is not a call center.
It is a conversion architecture.

Core Philosophy

Most digital systems fail after lead generation.
RobotAgency is engineered specifically to win at the last mile.

Humans close complex decisions
AI optimizes flow, timing, and prioritization
Commissions align incentives across the entire network
Conversion intelligence compounds over time

How the System Works

1. Lead Intake (Multi-Source)

Leads enter the system from:

Web platforms & e-commerce
Content & media (GlobalNews ecosystem)
Community partners & LinkedIn groups
Paid campaigns & organic traffic
Direct referrals

All leads are unified into a single conversion pipeline.

2. Human Telesales Core

Professional telesalers:

Qualify leads in real time
Handle objections and negotiations
Adapt messaging based on context
Close sales directly or escalate if needed

Humans remain the final authority in the sale.

3. Referral-Driven Growth Structure

The telesales network operates with a multi-tier referral logic, similar to proven models (e.g. Avon-style structures), but optimized for B2B and services.

Commission model (baseline):

15% commission on direct sales
+5% override on sales generated by referred telesalers
Regional COOs / Agencies: +5% regional override

This creates:

Natural recruitment incentives
Organic scaling without fixed payroll expansion
Performance-based cost structure

4. AI-Assisted Conversion Layer

AI operates as a co-pilot, not a replacement.

Functions include:

Lead scoring & prioritization
Call outcome analysis
Objection pattern detection
Offer recommendation
Best-time-to-call optimization
Channel attribution

AI continuously learns from real human conversions.

Conversion Intelligence Loop

Lead enters system
Human telesaler engages
AI analyzes outcome
Conversion data feeds back into scoring & scripts
Next lead is handled smarter

This loop increases:

Close rates
Deal velocity
Revenue per lead
Operator efficiency

Scale Path: Chatbots Layer (Phase 2)

Once commercial traction is proven, the system unlocks the next layer.

AI Chatbot Expansion

Up to 1 million AI chatbots
Covering pre-qualification, basic objections, scheduling
24/7 coverage across markets and time zones

Economic Threshold

This layer is only activated once:

Annual sales exceed ~$1B
Chatbot operating cost ($1–3M/year) is fully justified
Human sales performance data is sufficient to train AI safely

Result:
Humans focus on high-value closes.
AI absorbs volume.

KPIs Tracked

Conversion KPIs

Lead → Contact rate
Contact → Qualified lead
Close rate per telesaler
Average deal size
Sales cycle duration

Cost & Efficiency

CAC per channel
Revenue per telesaler
Commission efficiency ratio
Cost per closed deal

Network Health

Referral productivity
Telesaler activation rate
Retention & churn of operators

Why This Model Wins

Compared to Traditional Call Centers

No fixed payroll burden
Higher motivation through commissions
Better quality conversations
Faster learning cycles

Compared to AI-Only Sales

No trust gap
No hallucinations
No compliance risk
Better handling of complex deals

Strategic Value

Short term: immediate revenue generation
Mid term: declining CAC through AI assistance
Long term: seamless transition to AI-orchestrated sales

Human where judgment matters.
AI where scale matters.
Economics that improve over time.

Enterprise & Investor Takeaway

RobotAgency’s Telesales & Lead Conversion system:

Converts demand into revenue
Scales without linear cost growth
Protects quality, ethics, and compliance
Creates a defensible sales moat

This is not an experiment.
It is a controlled, phased, economically rational system.

1) Commission Calculator UI (WordPress-ready)

What it calculates

Telesaler earnings = Direct Sales × Direct Commission %
Leader override = Team Sales × Referral Override %
Regional COO/Agency override = Regional Sales × Regional Override %
Total payout = sum of the above
Optional: platform take / gross margin check (if you want to show “commissions as % of revenue”)

Embed option A (fastest): WordPress Custom HTML block

Paste this into a Custom HTML block (or Elementor HTML widget):

<div id="ra-calculator" style="max-width:820px;padding:18px;border:1px solid #222;border-radius:14px;">
  <h3 style="margin:0 0 8px;">Commission Calculator</h3>
  <p style="margin:0 0 16px;opacity:.85;">
    Estimate earnings for telesalers, leaders, and regional COOs/agencies (performance-based only).
  </p>

  <div style="display:grid;grid-template-columns:1fr 1fr;gap:12px;">
    <label>
      Direct Sales (USD / period)
      <input id="directSales" type="number" min="0" value="25000" style="width:100%;padding:10px;border-radius:10px;border:1px solid #333;">
    </label>

    <label>
      Direct Commission %
      <input id="directPct" type="number" min="0" max="100" step="0.1" value="15" style="width:100%;padding:10px;border-radius:10px;border:1px solid #333;">
    </label>

    <label>
      Team Sales from Referred Telesalers (USD / period)
      <input id="teamSales" type="number" min="0" value="100000" style="width:100%;padding:10px;border-radius:10px;border:1px solid #333;">
    </label>

    <label>
      Referral Override %
      <input id="refPct" type="number" min="0" max="100" step="0.1" value="5" style="width:100%;padding:10px;border-radius:10px;border:1px solid #333;">
    </label>

    <label>
      Regional Sales (USD / period)
      <input id="regionalSales" type="number" min="0" value="250000" style="width:100%;padding:10px;border-radius:10px;border:1px solid #333;">
    </label>

    <label>
      Regional COO/Agency Override %
      <input id="regionalPct" type="number" min="0" max="100" step="0.1" value="5" style="width:100%;padding:10px;border-radius:10px;border:1px solid #333;">
    </label>

    <label>
      Platform/Network Share % (optional)
      <input id="platformPct" type="number" min="0" max="100" step="0.1" value="0" style="width:100%;padding:10px;border-radius:10px;border:1px solid #333;">
    </label>

    <label>
      Period Label
      <input id="periodLabel" type="text" value="Monthly" style="width:100%;padding:10px;border-radius:10px;border:1px solid #333;">
    </label>
  </div>

  <div style="display:flex;gap:10px;margin-top:14px;flex-wrap:wrap;">
    <button id="calcBtn" style="padding:10px 14px;border-radius:10px;border:1px solid #333;cursor:pointer;">
      Calculate
    </button>
    <button id="resetBtn" style="padding:10px 14px;border-radius:10px;border:1px solid #333;cursor:pointer;opacity:.85;">
      Reset
    </button>
  </div>

  <div id="results" style="margin-top:16px;padding:14px;border-radius:12px;background:rgba(0,0,0,.06);border:1px solid #333;">
    <div style="display:grid;grid-template-columns:1fr 1fr;gap:10px;">
      <div><strong>Direct Commission</strong><div id="rDirect">$0</div></div>
      <div><strong>Referral Override</strong><div id="rRef">$0</div></div>
      <div><strong>Regional Override</strong><div id="rRegional">$0</div></div>
      <div><strong>Total Payout</strong><div id="rTotal">$0</div></div>
      <div><strong>Commissions as % of Total Sales</strong><div id="rPct">0%</div></div>
      <div><strong>Platform/Network Share (optional)</strong><div id="rPlatform">$0</div></div>
    </div>
    <p style="margin:10px 0 0;opacity:.8;font-size:13px;">
      Note: This is an estimator. Actual payouts depend on collected revenue, settlement cycles, and validation rules.
    </p>
  </div>
</div>

<script>
(function(){
  const $ = (id)=>document.getElementById(id);
  const fmt = (n)=> new Intl.NumberFormat('en-US',{style:'currency',currency:'USD'}).format(n||0);
  const fmtPct = (n)=> (Math.round((n||0)*10)/10).toFixed(1) + "%";

  function calc(){
    const directSales = parseFloat($("directSales").value)||0;
    const directPct   = (parseFloat($("directPct").value)||0)/100;

    const teamSales   = parseFloat($("teamSales").value)||0;
    const refPct      = (parseFloat($("refPct").value)||0)/100;

    const regionalSales = parseFloat($("regionalSales").value)||0;
    const regionalPct   = (parseFloat($("regionalPct").value)||0)/100;

    const platformPct   = (parseFloat($("platformPct").value)||0)/100;

    const direct = directSales * directPct;
    const refOv  = teamSales * refPct;
    const regOv  = regionalSales * regionalPct;

    const totalSales = directSales + teamSales + regionalSales;
    const payout = direct + refOv + regOv;

    const payoutPct = totalSales > 0 ? (payout/totalSales*100) : 0;
    const platformShare = totalSales * platformPct;

    $("rDirect").textContent = fmt(direct);
    $("rRef").textContent = fmt(refOv);
    $("rRegional").textContent = fmt(regOv);
    $("rTotal").textContent = fmt(payout);
    $("rPct").textContent = fmtPct(payoutPct);
    $("rPlatform").textContent = fmt(platformShare);
  }

  function reset(){
    $("directSales").value="25000";
    $("directPct").value="15";
    $("teamSales").value="100000";
    $("refPct").value="5";
    $("regionalSales").value="250000";
    $("regionalPct").value="5";
    $("platformPct").value="0";
    $("periodLabel").value="Monthly";
    calc();
  }

  $("calcBtn").addEventListener("click", calc);
  $("resetBtn").addEventListener("click", reset);
  calc();
})();
</script>

Optional UI upgrades (quick wins)

Add a “Monthly / Quarterly / Annual” toggle (multiplies inputs).
Add “Net revenue collected %” (e.g., refunds/chargebacks).
Add “Commission caps / tiers” (if you introduce them later).

2) VC-Style Sensitivity Model (CAC → LTV curves)

This is a boardroom framing: the key is to show how unit economics improve as (a) conversion rises and (b) CAC falls with hybrid execution + learning loops.

A) Inputs (assumptions you can disclose)

Use ranges, not single-point promises:

Traffic to Leads rate: 2%–6%
Lead-to-close conversion (classic): 5%–12%
Lead-to-close conversion (assisted): 8%–20%
AOV: $80–$250 (depends on vertical)
Gross margin: 55%–70%
Monthly retention (or repeat rate): varies by vertical
CAC: $40–$160 (blended)
Commission payout % of revenue: 10%–22% (blended by mix)

B) Sensitivity Table (Investor-ready)

Pick a standard cohort size: 1,000 leads.

Assume:

AOV = $150
Gross margin = 65%
Classic close rate = 10%
Assisted close rate = 14% / 18% (two scenarios)

Now compute:
Revenue = Leads × Close Rate × AOV
Gross Profit = Revenue × GM
Sales Payout (commissions) = Revenue × Payout%
Contribution = Gross Profit – Sales Payout – CAC total

Here’s a clean sensitivity matrix focusing on two levers VCs care about: conversion and CAC.

Scenario grid (Contribution per 1,000 leads)

Assume payout% = 15%, GM = 65%, AOV = $150

CAC per Lead	Close 10% (classic)	Close 14% (assisted)	Close 18% (assisted+)
$40	$3,250	$6,650	$10,050
$80	$-36,750?	wait fix

Need accurate math; I’ll provide a correct table with explicit formula:

Let’s calculate contribution:

Revenue = 1000 * close * 150
Gross Profit = Revenue * 0.65
Sales Payout = Revenue * 0.15
CAC total = 1000 * CAC
Contribution = GP – Payout – CAC

Now the results:

Close 10%: Revenue=150,000; GP=97,500; Payout=22,500 → GP-Payout=75,000
Contribution = 75,000 – CAC_total
Close 14%: Revenue=210,000; GP=136,500; Payout=31,500 → GP-Payout=105,000
Contribution = 105,000 – CAC_total
Close 18%: Revenue=270,000; GP=175,500; Payout=40,500 → GP-Payout=135,000
Contribution = 135,000 – CAC_total

So:

CAC per Lead	Close 10%	Close 14%	Close 18%
$40 (CAC=$40k)	$35,000	$65,000	$95,000
$80 (CAC=$80k)	-$5,000	$25,000	$55,000
$120 (CAC=$120k)	-$45,000	-$15,000	$15,000
$160 (CAC=$160k)	-$85,000	-$55,000	-$25,000

Investor narrative: Assisted conversion can move you from negative to positive contribution without changing AOV, purely by improving close rate and efficiency.

C) CAC → LTV curve framing (how to present it)

VCs expect a curve story:

Early phase: CAC high, LTV uncertain → focus on proving conversion lift via telesales.
Learning phase: AI assistance improves routing + scripts → CAC declines, conversion rises.
Scale phase: AI agents absorb volume → CAC stabilizes, margins expand.

Use the ratio:

LTV/CAC > 3x = scalable
Payback period < 6 months (or 3 months in aggressive verticals) = strong

D) Sensitivity “break-even” formula (simple, powerful)

Break-even close rate:close_rate≥CAC_per_leadAOV×(GM−payout%)close\_rate \ge \frac{CAC\_per\_lead}{AOV \times (GM – payout\%)}close_rate≥AOV×(GM−payout%)CAC_per_lead

With AOV=150, GM=65%, payout=15% → (GM – payout)=50%
So denominator = 150 * 0.50 = 75

Break-even close rate = CAC/75

CAC $40 → 53.3%?? Wait, CAC per lead not per customer. We used CAC per lead; leads already acquired. That formula should use CAC per lead only if leads are the spend unit. Correct: yes, then close_rate must cover lead-level CAC. For $40, close_rate 0.533 is wrong vs table. Because CAC total used per lead, not per customer; close_rate is fraction of leads turning to customers. Revenue per lead = close_rate * AOV. Profit per lead = close_rateAOV(GM-payout). Set equal to CAC per lead. So close_rate = CAC/(AOV*(GM-payout)) = 40/75 = 53.3%. That contradicts the earlier table because earlier CAC was per lead but also we treated 1000 leads already acquired; yes consistent: if CAC is $40 per lead, that is $40k for 1000 leads; to cover $40 per lead, each lead must on average generate $40 contribution. With close 10%, contribution per lead is $35,000/1000=$35 which is less than $40, hence negative? But table says positive $35k at CAC=40. Wait check: contribution at close 10% and CAC=40 was $35k positive. Contribution per lead = 35. That’s <40, but still positive? Can’t. Let’s recompute: For close 10%: GP-Payout=75k. Subtract CAC_total 40k => 35k positive. Per lead contribution 35. CAC per lead 40 but we already subtracted it; net 35 means contribution after CAC. That implies per lead profit 35, so before CAC it was 75. Yes CAC per lead 40, so net 35. That means break-even CAC per lead at close 10% is 75. Right: CAC_break_even = close_rateAOV(GM-payout)=0.101500.50=7.5. Oops because close_rate is 10%, not 1. So 0.101500.50=7.5. So if CAC per lead is $40, you’d be deeply negative. But our earlier calculation used CAC per lead times 1000; correct. Then close 10% gave net 35k. That means the profit before CAC per lead was 75, not 7.5. because I mistakenly used GM-payout 50% on revenue per customer, revenue per lead is close_rateAOV=15, not 1500.10? Actually 1500.10=15 yes. Profit per lead before CAC = 150.50=7.5 indeed. Times 1000 = 7,500. Not 75,000. Aha error: I used revenue 150,000 for 1000 leads close 10% AOV 150 => customers=100, revenue=15,000 not 150,000. Right: 1000 leads, close 10% => 100 customers. Revenue 100*150=15,000. Big mistake factor 10. Let’s correct quickly and present correct table.

Compute: revenue = 10000.10150=15,000. GP 9,750. Payout 2,250. GP-Payout 7,500. Subtract CAC_total: if CAC per lead 40 => 40,000 negative -32,500. So table changes.

We need provide corrected sensitivity with plausible CAC per lead maybe $2-$10 rather than 40. Better to define CAC per customer perhaps. For investors, CAC usually per customer, not per lead. Let’s reframe: Use 1,000 website visits -> leads etc. Too complex. We’ll provide model in terms of CAC per acquisition (customer). Simpler.

Define: Customers acquired N, revenue = NAOV, gross profit = revenueGM, payouts= revenuepayout%, contribution = GP – payouts – NCAC_customer.

Then sensitivity with CAC per customer 50/100/150. Use close rates on traffic irrelevant. We’ll just use per customer.

Let’s craft quickly:

Assume AOV=150, GM=65%, payout=15% => margin after payout = 50% => contribution before CAC per customer = 150*0.50=75. Then if CAC per customer is 50 => contribution 25. If CAC 80 => -5. Need LTV to offset; include repeat purchases: LTV = AOV * purchases_per_customer. So LTV contribution = 75 * purchases. For purchases 2 => 150. subtract CAC.

Create table: purchases 1/2/3 vs CAC 50/100/150.

Ok.

Provide guidance on plotting CAC→LTV curve: x CAC, y LTV; break-even line LTV=CAC/(GM – payout). Actually LTV contribution. We’ll write formulas.

a Commission Calculator UI you can paste into WordPress today, and
a VC-style sensitivity model that’s numerically consistent and easy to defend in diligence (CAC → LTV curves + break-even math).

1) Commission Calculator UI (WordPress-ready)

Embed (Custom HTML block)

Paste this into a WordPress “Custom HTML” block (or Elementor HTML widget). It calculates:

Direct telesaler earnings
Referral override earnings
Regional COO/Agency override
Total payout
Payout as % of total sales

<div id="ra-calculator" style="max-width:860px;padding:18px;border:1px solid #222;border-radius:14px;">
  <h3 style="margin:0 0 8px;">RobotAgency Commission Calculator</h3>
  <p style="margin:0 0 16px;opacity:.85;">
    Estimate commissions for telesalers, team leaders, and regional COO/agency partners (performance-based).
  </p>

  <div style="display:grid;grid-template-columns:1fr 1fr;gap:12px;">
    <label>
      Direct Sales (USD / period)
      <input id="directSales" type="number" min="0" value="25000" style="width:100%;padding:10px;border-radius:10px;border:1px solid #333;">
    </label>

    <label>
      Direct Commission %
      <input id="directPct" type="number" min="0" max="100" step="0.1" value="15" style="width:100%;padding:10px;border-radius:10px;border:1px solid #333;">
    </label>

    <label>
      Team Sales (from referred telesalers) (USD / period)
      <input id="teamSales" type="number" min="0" value="100000" style="width:100%;padding:10px;border-radius:10px;border:1px solid #333;">
    </label>

    <label>
      Referral Override %
      <input id="refPct" type="number" min="0" max="100" step="0.1" value="5" style="width:100%;padding:10px;border-radius:10px;border:1px solid #333;">
    </label>

    <label>
      Regional Sales (USD / period)
      <input id="regionalSales" type="number" min="0" value="250000" style="width:100%;padding:10px;border-radius:10px;border:1px solid #333;">
    </label>

    <label>
      Regional COO/Agency Override %
      <input id="regionalPct" type="number" min="0" max="100" step="0.1" value="5" style="width:100%;padding:10px;border-radius:10px;border:1px solid #333;">
    </label>

    <label>
      Period Label
      <input id="periodLabel" type="text" value="Monthly" style="width:100%;padding:10px;border-radius:10px;border:1px solid #333;">
    </label>

    <label>
      Notes (optional)
      <input id="notes" type="text" value="Estimator only. Paid on collected revenue." style="width:100%;padding:10px;border-radius:10px;border:1px solid #333;">
    </label>
  </div>

  <div style="display:flex;gap:10px;margin-top:14px;flex-wrap:wrap;">
    <button id="calcBtn" style="padding:10px 14px;border-radius:10px;border:1px solid #333;cursor:pointer;">
      Calculate
    </button>
    <button id="resetBtn" style="padding:10px 14px;border-radius:10px;border:1px solid #333;cursor:pointer;opacity:.85;">
      Reset
    </button>
  </div>

  <div id="results" style="margin-top:16px;padding:14px;border-radius:12px;background:rgba(0,0,0,.06);border:1px solid #333;">
    <div style="display:grid;grid-template-columns:1fr 1fr;gap:10px;">
      <div><strong>Direct Commission</strong><div id="rDirect">$0</div></div>
      <div><strong>Referral Override</strong><div id="rRef">$0</div></div>
      <div><strong>Regional Override</strong><div id="rRegional">$0</div></div>
      <div><strong>Total Payout</strong><div id="rTotal">$0</div></div>
      <div><strong>Payout as % of Total Sales</strong><div id="rPct">0%</div></div>
      <div><strong>Notes</strong><div id="rNotes" style="opacity:.85"></div></div>
    </div>
  </div>
</div>

<script>
(function(){
  const $ = (id)=>document.getElementById(id);
  const fmt = (n)=> new Intl.NumberFormat('en-US',{style:'currency',currency:'USD'}).format(n||0);
  const pct = (n)=> (Math.round((n||0)*10)/10).toFixed(1) + "%";

  function calc(){
    const directSales   = parseFloat($("directSales").value)||0;
    const directPct     = (parseFloat($("directPct").value)||0)/100;

    const teamSales     = parseFloat($("teamSales").value)||0;
    const refPct        = (parseFloat($("refPct").value)||0)/100;

    const regionalSales = parseFloat($("regionalSales").value)||0;
    const regionalPct   = (parseFloat($("regionalPct").value)||0)/100;

    const direct = directSales * directPct;
    const refOv  = teamSales * refPct;
    const regOv  = regionalSales * regionalPct;

    const totalSales = directSales + teamSales + regionalSales;
    const payout = direct + refOv + regOv;

    $("rDirect").textContent   = fmt(direct);
    $("rRef").textContent      = fmt(refOv);
    $("rRegional").textContent = fmt(regOv);
    $("rTotal").textContent    = fmt(payout);
    $("rPct").textContent      = pct(totalSales > 0 ? (payout/totalSales*100) : 0);
    $("rNotes").textContent    = $("notes").value || "";
  }

  function reset(){
    $("directSales").value="25000";
    $("directPct").value="15";
    $("teamSales").value="100000";
    $("refPct").value="5";
    $("regionalSales").value="250000";
    $("regionalPct").value="5";
    $("periodLabel").value="Monthly";
    $("notes").value="Estimator only. Paid on collected revenue.";
    calc();
  }

  $("calcBtn").addEventListener("click", calc);
  $("resetBtn").addEventListener("click", reset);
  calc();
})();
</script>

Optional upgrade (recommended): add a toggle for Monthly / Annual that multiplies sales numbers, plus a “Collected Revenue %” (refund/chargeback factor). Easy to add later.

2) VC-Style Sensitivity Model (CAC → LTV Curves)

Investor framing: the only curve that matters

You want to show investors:

CAC is controlled (human-assisted conversion + routing reduces waste)
LTV grows (retention + upsell + repeat purchase)
Payback improves as the system learns

To keep it defensible, express CAC as CAC per acquired customer (not per lead).

A) Model Inputs (simple + standard)

Define:

AOV = Average Order Value
GM = Gross Margin (after COGS)
Payout% = total commissions as % of revenue (blended)
Contribution Margin per Order = AOV × (GM − Payout%)
Orders per Customer (annual) = purchase frequency
LTV (contribution) = Contribution per Order × Orders per Customer × Retention Multiplier
Payback = CAC / (Contribution per Customer per period)

Base case example (illustrative):

AOV = $150
GM = 65%
Payout% = 15%
Contribution per order = 150 × (0.65 − 0.15) = 150 × 0.50 = $75

So every order yields $75 contribution (before CAC + overhead).

B) Sensitivity Table (LTV contribution vs CAC)

Assume orders per customer per year = 1 / 2 / 3
(you can swap these for your verticals).

Contribution LTV = $75 × orders/year

CAC per Customer	1 order/year (LTV=$75)	2 orders/year (LTV=$150)	3 orders/year (LTV=$225)
$50	+$25	+$100	+$175
$100	-$25	+$50	+$125
$150	-$75	$0	+$75
$200	-$125	-$50	+$25

How to read it (VC language):

With repeat purchases, the model tolerates higher CAC.
The platform’s job is to raise orders/customer and push CAC down over time.

C) CAC → LTV Curves (what to show on a slide)

Plot two curves (conceptually or with a chart later):

LTV contribution (y-axis) vs CAC (x-axis)
Break-even line: LTV = CAC

Then show how RobotAgency shifts the curve:

Higher conversion → lower CAC
Better retention + upsell → higher LTV

Story:
Classic stacks fight CAC. RobotAgency improves both sides of the ratio.

D) Break-even Formula (simple, diligence-safe)

Break-even orders per customer needed to justify CAC:orders≥CACAOV×(GM−payout%)orders \ge \frac{CAC}{AOV \times (GM – payout\%)}orders≥AOV×(GM−payout%)CAC

Using the base case: AOV=150, GM=65%, payout=15% → denominator = 75

So:

If CAC = $150 → orders ≥ 150/75 = 2 orders
If CAC = $100 → orders ≥ 1.33 orders (needs upsell/retention)
If CAC = $50 → orders ≥ 0.67 (profitable even with one purchase)

This is an extremely clean slide for investors.

E) “AI-Assisted” Sensitivity (what changes when AI improves ops)

AI-Assisted Sales primarily impacts:

CAC decreases (better routing, prioritization, scripts)
Orders/customer increases (better follow-up, retention triggers)

You can present a simple 2×2:

CAC: high → low
Orders/customer: low → high

RobotAgency roadmap pushes you toward low CAC + high repeat, where margins expand structurally.

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