Case studies
Twelve scenarios across SaaS, AI, e-commerce, FMCG, fintech, financial services, health and wellness, private equity, real estate, professional services and logistics — in Miami, New York, San Francisco, Dallas, Houston, Dubai, Sydney, Melbourne, Perth and Auckland. Each shows the situation, the pod we would build and the numbers it is built to move.
SaaS · New York
Situation. Three US SDRs had left inside a year; AEs were self-sourcing 45% of pipeline and the Q3 number was at risk.
Pod built. 4 SDRs + working team lead, Cape Town, US Eastern hours; AI research and drafting stack in HubSpot; inbound desk added in week 3.
| Qualified meetings / month | 12 → 46 |
|---|---|
| Speed-to-lead | Next day → under 5 min |
| AE self-sourced pipeline | 45% → 15% |
| Cost vs previous US team | −62% |
AI start-up · San Francisco
Situation. Hundreds of demo requests a month from a product launch; founders triaging by hand; conversion to opportunity under 8%.
Pod built. 3 technical SDRs who could demo-qualify + 1 solutions onboarding seat, Cape Town; AI classification of inbound by use case.
| Inbound → opportunity | 8% → 24% |
|---|---|
| Founder hours on triage / week | 20 → 2 |
| Time-to-first-value for new customers | 6 weeks → 11 days |
| Live | Day 6 |
E-commerce · Dallas
Situation. Support queue over 48 hours during peaks; CSAT at 78%; marketplace account at risk from late responses.
Pod built. 6 support agents (chat, email, social) split Manila day / Cape Town overnight + QA lead; AI-drafted replies from a knowledge base built as they worked.
| First response | 48 h → 38 min |
|---|---|
| CSAT | 78% → 93% |
| Cost per ticket | −54% |
| Marketplace late-response flags | Zero after week 4 |
FMCG · Houston
Situation. Six field reps couldn't reach the long tail of 1,400 independent stores; distributor queries went unanswered for days.
Pod built. 4 inside-sales reps + key-account support desk, Cape Town, US Central hours; call lists built by AI from distributor data.
| Independent accounts contacted / month | 180 → 1,100 |
|---|---|
| Field-rep appointments booked / week | 0 → 35 |
| Distributor query response | 3 days → same day |
| New active doors in pilot | +212 |
Fintech · Dubai
Situation. Onboarding completion stuck at 61%; support first-response over 24 hours; compliance worried about scripted outreach.
Pod built. 3 KYC/onboarding specialists + 4 support agents + team lead, Cape Town, Gulf hours; every call recorded and QA-scored.
| Onboarding completion | 61% → 88% |
|---|---|
| First response | 24 h → 52 min |
| QA pass rate on scripts | 99% |
| Live | Day 7 |
Financial services · Sydney
Situation. Partners turning away clients; month-end taking 14 days; two unfilled bookkeeper roles for five months.
Pod built. 4 bookkeepers / AR specialists + controller-support lead, Cape Town (SAICA pipeline), Xero and QuickBooks; overnight for Sydney.
| Days to close | 14 → 5 |
|---|---|
| Client capacity | +40 clients |
| DSO across client book | −11 days |
| Cost vs local hires | −58% |
Health & wellness · Miami
Situation. Front-desk staff doubling as the sales team; enquiries answered next day; no-shows at 30%.
Pod built. 3 enquiry-response and booking specialists + reminder operations, Manila and Cape Town for 7am–9pm coverage; HIPAA-aware handling.
| Enquiry response | Next day → 4 min |
|---|---|
| Enquiry → booked consult | 22% → 47% |
| No-show rate | 30% → 14% |
| Front-desk hours freed / week | 60+ |
PE portfolio · Chicago
Situation. Nine acquisitions, nine back offices; DSO over 60 days; support handled differently at every company.
Pod built. Shared-services pod: 5 AR/AP specialists + 4 support agents + lead, Cape Town; one playbook deployed company by company.
| Platform DSO | 63 → 41 days |
|---|---|
| Back-office cost | −47% |
| Support SLA met | 71% → 96% |
| Roll-out | All nine companies in 90 days |
Real estate · Dubai
Situation. 600+ portal leads a week; agents calling back hours later; conversion to viewings under 10%.
Pod built. 5 lead-qualification specialists + lead, Cape Town, Gulf hours; AI scoring of leads by intent; viewings booked straight into agents' calendars.
| Lead response | 4 h → 6 min |
|---|---|
| Lead → viewing | 9% → 27% |
| Agent hours prospecting / week | 25 → 5 |
| Live | Day 5 |
Professional services · Melbourne
Situation. Partner-led sales; feast-or-famine revenue; two clients at 55% of income.
Pod built. 1 BDM + 2 SDRs + research seat, Cape Town, Australian late shift + US hours for export; partner meetings booked weekly.
| Partner-level meetings / month | 3 → 18 |
|---|---|
| Top-two client concentration | 55% → 34% |
| Proposals issued / month | 4 → 14 |
| Cost of BD | Below one local hire |
Logistics · Auckland
Situation. Dispatchers answering tracking calls; customer queries lost at shift change; no after-hours coverage.
Pod built. 5 customer-service agents, Manila early shift + Cape Town evening, with tracking-system access and AI-drafted status replies.
| Dispatcher hours on queries / week | 40 → 4 |
|---|---|
| Query resolution same day | 58% → 94% |
| After-hours coverage | None → 24/7 |
| Cost vs local roster | −61% |
SaaS · Perth
Situation. Great product, no US presence, no one awake in US hours.
Pod built. 3 SDRs + lead, Cape Town, US Central hours; messaging built with AJ Smith; Virtual CRO cadence for the founder.
| US qualified meetings / month | 0 → 22 |
|---|---|
| US pipeline created in pilot | $1.1M |
| Founder time on sales / week | 30 h → 6 h |
| First US customer | Week 9 |
Scenarios are modelled on typical client economics and X77 benchmarking; figures are illustrative targets, not reported results of named clients.
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