Your Apollo deployment is fully activated. All 30 seats are filled, every core feature is live, and the team is running active sequences and workflows. The Outbound Qualified workflow built for your demand gen team is in testing. That's a strong foundation — it took work to get here and it shows in the usage numbers.
This playbook is about what comes next. You've built the engine. This is about tuning it for the specific segments your AI Services Squads are going after — and scaling from 50 emails/day to 200.
Activation is strong across all 8 core features. The upside is in depth and scale — systematizing what's already working (AI Research, workflows) across the full team, getting every rep using Finder View for prospecting, and building the segment-specific infrastructure your four AI Services Squads need to run distinct outbound motions.
Five plays, sequenced by priority. The first two are foundation plays — get these live before building the segment-specific infrastructure. The last three scale the motion once the engine is running.
Your AI Services Model reorg created four distinct squads — specialty, FQHC, health system, federal. Each squad is going after a different buyer. Right now, reps are building lists manually or reusing generic searches. Segment-specific saved searches give every rep in every squad a clean, repeatable prospecting starting point the moment they open Finder View.
Specialty Care — DM Outreach saved search: Medical Practice / Physician Groups, 10–500 employees, titles (Practice Mgr, Director Ops, CMO), technologies (Epic, athenahealth, eClinicalWorks)FQHCs — Decision Makers search: Hospital & Health Care + Nonprofit, keywords (federally qualified, FQHC, 330 grant, community health), technologies (eClinicalWorks, OCHIN)Health Systems — Decision Makers search: 500+ employees, VP Patient Experience / CMIO / VP Revenue Cycle, technologies (Epic, Cerner, MEDITECH)Federal Health — Decision Makers search: keywords (VA, Veterans Affairs, DoD, Indian Health Service, IHS), Director+ seniority| Metric | Baseline | Target | Timeline |
|---|---|---|---|
| SDRs using Finder View | 4/10 | 10/10 | 2 weeks |
| Saved searches live | 0 | 4 | 1 week |
| Lists built/rep/week | Manual | 50+ contacts | 3 weeks |
Gil built a working AI pre-qualification workflow that automatically routes contacts based on ICP fit — saving hours of manual work every week. That workflow exists for one rep right now. Deploying it to all 12 SDRs, with segment-specific prompts tuned to each squad's buyer, is a force multiplier that doesn't require any new features — just replication.
Yes / No / UnknownYes → continue sequence + add to email sequence. No → remove from sequences + Do Not Contact (60 days). Unknown → route to 3-touch validation sequence.| Metric | Baseline | Target | Timeline |
|---|---|---|---|
| Reps with workflow | 1 (Gil) | 12 SDRs | 3 weeks |
| Contacts pre-qualified before sequence | Manual | 80%+ | 3 weeks |
| Unqualified contacts in sequences | Unknown | <10% | 4 weeks |
Artera has 1,000+ provider customers. Every time a champion at one of those accounts moves to a new organization, that's a warm introduction to a net-new logo — and they already know exactly what Artera does. Job Change Alerts surface these signals automatically. This is one of the highest-leverage, lowest-friction plays in the playbook.
| Metric | Baseline | Target | Timeline |
|---|---|---|---|
| Alerts configured | 0 | 1 (all prior contacts) | 1 week |
| Sequence live | 0 | 1 | 1 week |
| Meetings from reactivation | 0 | 2–4/month | 60 days |
The AI Services Model reorg is brand new. Each squad is going after a distinct buyer with different pain points, proof points, and buying timelines. One generic sequence won't work across all four. Getting segment-specific sequences live before the squads fully ramp gives each team a professional outbound engine from day one — and ensures the right proof point lands with the right buyer.
| Metric | Baseline | Target | Timeline |
|---|---|---|---|
| Segment sequences live | 0 | 4 | 2 weeks |
| Content Center populated | 0% | 100% | 1 week |
| Meetings/squad/month | TBD | +2–3 per squad | 60 days |
You have 1,000+ provider customers. Most of those accounts have multiple departments, locations, or service lines that aren't yet on the full Artera platform. Identifying the contacts at those organizations who influence patient communications or care coordination in a different department from your primary contact is net-new revenue with near-zero acquisition cost. Apollo makes this systematic.
Customer Base — Expansion Contacts| Metric | Baseline | Target | Timeline |
|---|---|---|---|
| Expansion contact list built | 0 | 500+ contacts | 2 weeks |
| Expansion sequence live | 0 | 1 | 2 weeks |
| Expansion meetings/month | 0 | 3–5 | 90 days |
| SDR Headcount | 12 |
| Target Emails/Day | 200 per SDR (confirm with Eric) |
| Domains to Purchase | 4 (not including artera.io) |
| Mailboxes per SDR | 4 |
| Total Mailboxes | 48 |
| Domain Aging | 30 days minimum |
| Warmup Period | 4–6 weeks |
| Total Timeline | ~2 months from purchase |
| Primary Domain Rule | artera.io = customer comms only. Never add to sending rotation. |
Production-ready sequence templates for each AI Services Squad vertical. Copy-paste ready — swap {{variables}} for contact-specific details.
| Metric | Current | 30-Day Target | 90-Day Target |
|---|---|---|---|
| Finder View adoption | 4/10 SDRs | 10/10 SDRs | 10/10 SDRs |
| Onboarding goals configured | 0% | 100% (all personas) | 100% |
| Email volume per SDR per day | ~50 | Building (domain aging) | 200 |
| Active workflows | 2 | 4 | 6+ |
| Segment-specific sequences live | 0 | 2 (Specialty + FQHC) | 4 (all verticals) |
| Meetings booked/month (platform) | Baseline TBD | +10% | +25% |
| AI Research prompts configured | 1 (manual) | 3 (per segment) | 5+ |
| Seat coverage vs. dept size | 30/62 | 30/62 | 40+/62 |
| Reactivation meetings/month | 0 | 0 (setting up) | 2–4 |
| Expansion contacts identified | 0 | 0 (setting up) | 500+ |