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Application·Revenue Operations Lead·May 2026

Eight systems. Ninety days.
One force of nature.

A live spec of the operations layer I would build inside LEADR's SDR function in my first ninety days. Three systems in week one. Eight across the quarter.

Prepared for Nick Ahrens & the LEADR growth team.

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Systems
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AI Layers
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Day Rollout
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Week-One Ships
Author
Nick Nuaym · London, UK
Stack
HubSpot · Aloware · Slack · Airtable · Asana · Claude / LLMs · Clay · n8n
Contact
nuaymcloses@gmail.com
Section I · AI Architecture

The AI layer.

The role spec says: you do not just use AI, you architect with it. Seven layers. Each one maps to a real data flow inside the tools this team already runs. HubSpot sits at the center. The LLM connects via API: Claude today, swap the key for any model tomorrow. n8n runs the event bus. Clay handles enrichment in parallel.

How the stack connects
HubSpot CRM · System of record Leads · Deals · Contacts · Properties Claude / LLMs Intelligence layer API-agnostic · Any model Clay Parallel enrichment · List ops Aloware Dialer · Call data → Dialer.io migration Airtable ATS · Recruiting ops Slack Visibility · Notifications Asana Tasks · Workflows n8n Event bus · Orchestration API read/write enrichment write-back native sync recruiting pipeline reports · alerts SOPs · tasks event triggers
Layer 01
Data Ingestion & Sync
Everything ends up in HubSpot. Aloware pushes calls, recordings, and dispositions. Forms and Calendly sync on their own. n8n bridges the gaps.
Zero data silos
HubSpotAlowaren8nCalendly
Layer 02
Waterfall Enrichment
Meta leads hit a setter in under 30 seconds. Zero gates. Clay runs in parallel, filling in firmographics and ICP data while the call's already happening. 7-provider waterfall: Prospeo, Apollo, Findymail, Dropcontact, Datagma, Hunter.io, Fullenrich. Stale contacts refresh nightly. Cold call lists built from scratch when needed.
< 30s speed to lead
ClayApolloHubSpotClearbitLinkedIn
Layer 03
AI Scoring & Routing
LLM scores every enriched contact by conversion probability. Tags tiers into custom HubSpot properties and routes the best leads first. Model retrains weekly from closed-won data.
Weekly model retraining
Claude / LLMsHubSpotAloware
Layer 04
Workflow Orchestration
n8n watches for HubSpot events: new contact, deal stage change, missed SLA. Kicks the LLM when something needs thinking. Routes outputs back. Bridges HubSpot to Airtable, Clay to HubSpot, Slack to everything.
18 triggers · 31 actions
n8nHubSpotSlackAirtableAsanaClay
Layer 05
Engagement Intelligence
Pre-call briefs from enrichment data. Post-call summaries auto-logged. Trellus coaches in real time during live calls. Gong transcripts show what top performers actually do. Built to survive the Aloware to Dialer.io swap.
Real-time coaching
Claude / LLMsAlowareDialer.ioHubSpotGongTrellus
Layer 06
Reporting & Truth System
Written EOD/EOW reports with trend analysis. CRM-vs-dialer audits flag mismatches before management sees them. Pipeline forecasts catch risk early. Pushed to Slack via n8n.
Zero manual entry
Claude / LLMsHubSpotAlowareSlackn8n
Layer 07
Onboarding & Enablement
Slack bot for process questions and SOPs on demand. Hyperbound runs AI roleplay with ICP personas that fight back on weak openers. SOPs tracked in Airtable. Walkthroughs via Loom.
Weeks, not months
Claude / LLMsSlackAirtableLoomHyperbound
Already Built
10 production AI agents on a live CRM
Automated lead scoring and pipeline routing
AI-assisted call review systems
Custom reporting automations end to end
Daily Tools
Claude Code + MCP for production AI
n8n / Make.com workflow orchestration
CRM architecture (Close, HubSpot-class)
Slack & Airtable automation design
Context
$1.2M cash collected, $1.7M in revenue
Master's in AI, currently pursuing
London based, working US hours
Extreme ownership, zero hand-holding
Section II · Week One

What ships in week one.

Three systems chosen because each one removes friction from the SDR function immediately. Not planning documents. Operational systems running against HubSpot, Aloware, and Slack from day one.

System 01Week One
CRM Audit Engine.
Scans the full HubSpot architecture: lead statuses, list structure, pipeline data, property usage. Produces a written gap report with every inconsistency flagged, prioritized by revenue impact.
Expand details

How it runs

Direct HubSpot access from day one. Cross-references lead statuses against actual pipeline stages. Identifies orphaned contacts, duplicate lists, stale properties, broken automations. Maps the gap between how the CRM should route leads and how it actually does.

What this returns to LEADR

A single document that tells the management team exactly where the CRM is bleeding data quality and what to fix first. The roadmap for days 31 through 90 is built on observed reality, not assumptions.

System 02Week One
Ticket Pattern Killer.
Takes full ownership of the SDR tech support ticket queue. Establishes SLA tracking, but the real value: pattern detection. Same issue twice triggers root-cause investigation, not another Band-Aid.
Expand details

How it runs

Existing ticket channels consolidated into a single queue with status tracking. Each resolution logged with category tags. Weekly pattern report surfaces the top three recurring issues with proposed permanent fixes.

What this returns to LEADR

Zack stops being the single point of failure for every broken tool. SDRs get faster resolution. The same fire stops getting put out every week.

System 03Week One
Pod Shadow Protocol.
Observes all three SDR management pods. Documents where operational friction actually lives, not what people report. Maps the gap between SOPs and reality on the floor.
Expand details

How it runs

One week of structured observation across all three pods. Daily notes: tools used, time on admin versus selling, blockers hit, workarounds invented, questions that should be self-serve. Produces a friction map for the entire 90-day roadmap.

What this returns to LEADR

Taylor gets the view he has been too deep in the weeds to see. Nick gets a data-backed picture of where the operational leverage actually lives.

Section III · The Full Eight

The full eight.

Three ship in week one. The remaining five build out across ninety days, sequenced against where the friction map says the next fire is burning. Each one maps to an ownership area in the role spec.

01
CRM Audit Engine
Full HubSpot scan. Gap report with prioritized fix list.
CRM & Data
02
Ticket Pattern Killer
Owned queue. SLA. Root-cause pattern detection.
Tech Stack
03
Pod Shadow Protocol
Friction map across all three SDR pods.
Process & Scale
04
AI Workflow Layer
Lead scoring, enrichment, prompt libraries, automation architecture.
AI & Automation
05
Slack Nerve Center
Automated EOD/EOW. Workflow health. Zero manual reporting.
Slack Ops
06
Reporting Truth System
Self-reported vs CRM vs dialer. Auto-reconciled.
Sales Reporting
07
Recruiting Pipeline
ATS for 16 hires/month. Automated dispatch.
Recruiting Ops
08
Onboarding Zero-Delay
Template-driven SOPs. Checklist-verified. Zero delays.
Onboarding Ops
Section IV · The Math

$1.6M → $2.4M

On assumed numbers · starting point $1.6M setter-closed revenue

▶ Change any input — everything recalculates live

Defaults pulled from Taylor's application video: $2.5M-$3M/month total revenue, 60-person team across three pods. A 5-point connect-rate lift on the setter function adds $803K in new monthly cash from one operational fix.

Setter-Closed Revenue
$1.6M
CURRENT / MO
→
$2.4M
TARGET / MO
+$803,250 (+50%)
+$9,639,000 annualized · Total company: $2.15M → $2.96M

Inputs

Team
Connect Rates
Setter → Closer Funnel
Economics

Funnel

StageCurrent (10%)Target (15%)Delta
Dials / month16,80016,8000
Quality connects (65s+)1,6802,520+840
Strategy calls booked504756+252
Strategy calls taken378567+189
Deals closed94.5141.8+47.3
Closed revenue$1,606,500$2,409,750+$803,250
Gross profit$1,124,550$1,686,825+$562,275
+50%
Revenue Lift
187x
GP ROI
<1 day
Payback
$3,000
Dialer / mo
Δ revenue = ΔCR × dials × (book × show × close) × deal
0.05 × 16,800 × (0.30 × 0.75 × 0.25) × $17,000
= $803,250 new cash / month
Section V · The Bet

What you measure me on.

Three checkpoints. Each with measurable deliverables. By day ninety you have evidence the hire was right. No ambiguity.

Day 0
Day 30
Day 60
Day 90
Day 1 — 30
Audit delivered.
  • CRM gap report written and presented
  • Tech ticket queue owned with SLA operational
  • All three pods shadowed, friction map produced
  • Written roadmap for days 31-90 on Nick's desk
  • First optimizations and automations live
Day 31 — 60
Gaps closing.
  • CRM actively tightening: clean lists, corrected statuses
  • Biggest onboarding bottleneck identified and fixed
  • First onboarding cycle run cleanly end to end
  • AI workflow layer: first two components live
  • Reporting truth system catching discrepancies
Day 61 — 90
Taylor steps back.
  • Taylor is out of day-to-day ops. Systems hold.
  • Zack has a real partner, not another person to manage
  • Data clean. SOPs current and actually followed
  • AI embedded as standard practice
  • All three pods: clean data, reliable reporting, zero blockers
  • Next-quarter improvement roadmap presented

The end state is simple. You gain an operator who runs like a force of nature. The SDR function scales without the operational debt that slows most teams down. Every system built is documented, auditable, and transferable.

"This page is the spec. The conversation is where it gets real. I am not here to maintain what exists. I am here to build what is missing."

Nick Nuaym
nuaymcloses@gmail.com · +44 7769 282942