In “Lead Reactivation Voice AI: Full Build Breakdown ($54K Project),” you get a clear, high-level walkthrough of a profitable Voice AI lead reactivation system built and sold by Henryk Brzozowski. You’ll see ROI calculations, the Vapi–Airtable–Make.com automation that replaced two years of manual work, and the exact blueprint used to scale a Voice AI agency.
The video and write-up are organized with concise sections covering offer breakdown, ROI & revenue, results, the high-level build, screenshots, and next steps so you can follow the deliverables step-by-step. Use the timestamps to jump to the parts most relevant to your agency or project planning.
Offer breakdown
Description of the lead reactivation service and deliverables
You get a done-for-you Voice AI lead reactivation system that automatically calls dormant leads, qualifies interest, and either reactivates them or routes warm prospects to humans. The $54K package delivers a full stack: Vapi-based call orchestration, natural-sounding TTS prompts and ASR transcription, Airtable as the central CRM and datastore, Make.com (with n8n as optional failover) workflows for orchestration and retries, dashboards and analytics, legal/TCPA review, 30–60 day pilot optimization, documentation, and training so your team can operate or hand off the system.
Target customer profiles and verticals best suited for the system
You’ll see the fastest wins in businesses with large dormant lead pools and high lifetime value per customer: home services, dental/medical practices, auto sales and service, B2B SaaS renewals, high-ticket e-commerce, and financial services. Organizations that need to re-engage leads at scale and have measurable AOVs or CLTV are ideal because the automation reduces manual dials and lifts revenue quickly.
Value propositions: conversion lift, time savings, and reduced CAC
You should expect three core value props: conversion lift (reactivating leads that would otherwise be waste), massive time savings (what would have taken a human two years of calling can be automated), and reduced CAC because you monetize existing lead assets rather than buying new ones. Typical conversion lifts range from low single digits to mid-single digits in reactivation rate, but when applied to large lists this becomes meaningful revenue with faster payback and lower incremental CAC.
What was sold in the $54K package and optional add-ons
The $54K package sold foundational deliverables: discovery and data audit, system architecture, Vapi dialer and voice AI flows, Airtable schema and lead prep, Make.com orchestration, transcription and analytics pipeline, QA and compliance checks, pilot run with optimization, training, and 60 days support. Optional add-ons you can offer include: ongoing managed service, premium TTS voices or multilingual support, enterprise-grade CRM integrations, live agent escalation packages, SLA-backed uptime, and advanced enrichment (paid API credits).
How the offer was positioned in sales conversations
You sold this as a high-ROI, low-risk pilot: a fixed-price build that turns dormant leads into revenue with measurable KPIs and a clear payback model. In conversation you emphasized case-study revenue lift, the time saved vs manual calling, TCPA compliance controls, and limited build slots. You used ROI projections to justify price, offered a short pilot and performance review window, and positioned optional managed services for ongoing optimization.
Project summary and scope
Overall project goal and success criteria
Your goal was to convert dormant leads into paying customers by automating outbound voice engagement. Success criteria were defined as a measurable reactivation rate, a quantifiable revenue uplift (e.g., rolling payback within 3 months), a stable call automation pipeline with >90% uptime, and clear handoff/training for operations.
Scope of work included in the $54K build
The scope included discovery and data audit, architecture and design, Vapi dialer configuration, TTS/ASR tuning, Airtable schema and data import, Make.com scenarios for orchestration and retries, transcription and analytics pipeline, QA and TCPA review, pilot execution and optimization, training, documentation, and 60 days post-launch support.
Assumptions and out-of-scope items
You assumed the client provided a clean-ish lead export, access to CRM/APIs, and permission to call leads under existing consent rules. Out-of-scope items: large-scale data enrichment credit costs, carrier fees above quoted thresholds, building a custom dashboard beyond Airtable views, in-person training, and long-term managed services unless contracted as add-ons.
Key stakeholders and decision makers
You engaged stakeholders from sales/BDR, marketing (lead sources), operations (data owners), legal/compliance (TCPA), and IT (integration/credentials). Final decisions on consent logic and escalation routing rested with the client’s compliance lead and head of sales.
High-level expected outcomes and timelines
You expected to deliver an initial working pilot in 4–6 weeks: week 1 discovery and data prep, weeks 2–3 architecture and integrations, week 4 voice tuning and QA, week 5 pilot launch, and week 6 optimization and handoff. Outcomes included measurable reactivation within the pilot window and a payback projection based on reactivated customers.
Detailed cost breakdown for the $54K project
Line-item costs: development, licenses, integrations, and configuration
A representative line-item breakdown for the $54K package looked like this:
- Project management & discovery: $4,500
- System architecture & design: $6,000
- Vapi integration & voice AI logic: $9,000
- Airtable schema & data prep: $4,000
- Make.com workflows & n8n failover wiring: $6,000
- TTS/ASR tuning and voice script development: $4,000
- Transcription pipeline & analytics (storage + dashboard): $5,000
- QA, compliance & TCPA review: $2,500
- Training, docs, and handoff: $3,000
- Pilot run & optimization (30 days): $4,000
- Contingency & 60-day post-launch support: $2,000
Subtotal: $50,000 - Agency margin/profit: $4,000
Total: $54,000
One-time vs recurring costs: infrastructure and third-party services
One-time costs include the build labor and initial configuration. Recurring costs you should budget for separately are platform usage and third-party services: Vapi (per-minute / per-call), ASR/transcription (per minute), TTS premium voices, Airtable Pro seats, Make.com operations units, storage for recordings/transcripts. Typical recurring baseline might be $2–3k/month depending on call volume; managed service add-on is typically $2–4k/month.
Labor allocation: internal team, contractors, and agency margins
Labor was allocated roughly by role: 15% PM, 45% dev/engineers, 15% voice engineer/IVR specialist, 10% QA, 5% documentation/training, 10% sales/admin. Contractors handled voice prompt actors/voice tuning and certain integrations; core engineering and QA were internal. Agency margin was modest (around 7–10%) to keep pricing competitive.
Contingency, testing, and post-launch support allowances
You included contingency and post-launch support to cover carrier hiccups, tuning, and compliance reviews — about 4–6% of the price. Testing cycles and the pilot budget allowed for iterative script changes, model threshold tuning, and up to 60 days of monitoring and adjustments.
How costs map to pricing and margins in the sales package
Costs covered direct labor, third-party credits for POCs, and operational overhead. The pricing left a healthy but realistic margin so you could quickly scale this offer to other clients. The sell price balanced a competitive entry price for clients and enough margin to fund ongoing R&D and support.
Business case and ROI calculations
Primary revenue uplift assumptions and reactivation rate projections
You base revenue uplift on three realistic scenarios for reactivation rates applied to the dormant lead universe: low (1%), medium (3%), and high (6%). Conversion of reactivated leads to paying customers is another lever — assume 10% (low), 20% (medium), 30% (high). Average order value (AOV) or deal size is another input.
Step-by-step ROI formula used in the video and deal deck
The core formula you used is:
- Reactivated leads = total leads * reactivation rate
- New customers = reactivated leads * conversion rate
- Revenue uplift = new customers * AOV
- Gross profit uplift = revenue uplift * gross margin
- ROI = (gross profit uplift – project cost) / project cost
Example: 10,000 dormant leads * 3% = 300 reactivated. If conversion is 20% -> 60 customers. If AOV = $1,200 -> revenue uplift $72,000. With a 40% gross margin, gross profit = $28,800. ROI = (28,800 – 54,000)/54,000 = -46.7% short-term, but you must consider recurring revenue, lifetime value, and reduced CAC to see true payback. If LTV is higher or AOV is larger, payback is faster.
Breakeven and payback period calculations
Breakeven is when cumulative gross profit equals the $54K build. Using the prior example, if gross profit per month after the pilot is $28,800, you’d reach breakeven in roughly 2 months if you count cumulative monthly gains (though in that example gross profit is the pilot outcome; you’d typically see recurring monthly incremental gross profit once the system runs). A simpler payback calc: Payback months = project cost / monthly incremental gross profit.
Sensitivity analysis: low/medium/high performance scenarios
- Low: 10,000 leads, 1% react (100), 10% conversion (10 customers), AOV $800 -> revenue $8,000 -> gross@40% $3,200. Payback ~ 17 months.
- Medium: 10,000 leads, 3% react (300), 20% conversion (60), AOV $1,200 -> revenue $72,000 -> gross@40% $28,800. Payback ~ 1.9 months.
- High: 10,000 leads, 6% react (600), 30% conversion (180), AOV $1,500 -> revenue $270,000 -> gross@40% $108,000. Payback ~ 0.5 months.
These show why client vertical, AOV, and list quality matter.
Real examples of revenue realized from pilot clients and expected LTV impact
Example 1 (dental chain): 4,500 dormant leads, 4% react -> 180. Conversion 15% -> 27 patients. AOV per patient $1,500 -> revenue $40,500 in the pilot month. Expected LTV uplift per patient (repeat visits) increased long-term revenue by 3x.
Example 2 (B2B SaaS): 2,000 churned trials, 5% react -> 100. Conversion 25% -> 25 re-subscribers. Annual contract value $6,000 -> first-year revenue $150,000. These pilot results justified immediate scale.
Technical architecture and system design
End-to-end diagram overview of components and data flow
You can visualize an architecture: lead sources -> Airtable (central datastore) -> Make.com orchestrator -> Vapi dialer (control + TTS streaming + call state webhooks) -> PSTN carrier -> call audio routed to ASR + storage -> transcripts to transcription service and S3 -> Make.com updates Airtable and triggers analytics / alerts -> dashboards and human agents (via CRM or warm transfer). n8n is configured as a backup orchestration path and for tasks that require custom code or advanced retries.
Role of Voice AI in calls: TTS, ASR, intent detection, and DTMF handling
You use TTS for prompts and natural-sounding dialogue, ASR for speech-to-text, intent detection (via LLMs or classical NLP) to parse responses and classify outcomes, and DTMF for secure or deterministic inputs (e.g., “press 1 to confirm”). These components let the system have conditional flows and escalate to human agents when intent indicates purchase or complexity.
How Vapi was used to manage voice calls and AI logic
Vapi manages call control, dialing, streamable audio, and real-time webhooks for call state. You use Vapi to initiate calls, play TTS, stream audio to ASR, collect DTMF, and pass call events back to Make.com. Vapi handles SIP/PSTN connectivity and provides the hooks to attach AI logic for intent detection.
Airtable as the centralized CRM/data store and its schema highlights
Airtable holds the lead records and orchestrates state: lead_id, name, phone_e164, source, last_contacted, status (new, queued, attempted, reactivated, failed), consent_flag, do_not_call, lead_score, enrichment fields (company, role), call_attempts, next_call_at, transcripts (attachments), recordings (attachments), owner. Airtable views drive queues for the dialer and provide dashboards for operations.
Make.com and n8n roles for orchestration, error handling, and retries
Make.com is your primary orchestration engine: it triggers calls from Airtable, calls Vapi APIs, handles webhooks, saves recordings/transcripts, updates status, and fires alerts. n8n acts as a fallback for complex custom logic or for teams preferring open-source automation; it’s also used for heavier retry strategies or custom connectors. Both systems handle error catching, retries, and rate limiting coordination.
Data model, lead list prep, and enrichment
Required lead fields and schema design in Airtable
Required fields: lead_id, full_name, phone_e164, email, source, opt_in_flag, do_not_call, last_contacted_at, call_attempts, status, owner, estimated_value, timezone, preferred_contact_hours. These fields support consent checks, pacing, and prioritization.
Cleaning and normalization steps for phone numbers and contact data
You normalize phone numbers to E.164, remove duplicates, validate using phone lookup APIs, normalize timezones, and standardize name fields. You apply rule-based cleaning (strip non-numeric characters, infer country codes) and flag bad numbers for exclusion.
Enrichment data sources and when to enrich leads
Enrichment sources include commercial APIs (company/role data), phone lookup services, and internal CRM history. Enrich prior to calling when you’re prioritizing high-value lists, or enrich post-interaction to fill CRM fields. Budget enrichment credits for the initial pilot on top of the build price.
Segmentation logic for prioritizing reactivation lists
You prioritize by expected value, recency, past engagement, and consent. Example segments: VIP leads (high AOV), recent losers (<90 days), high-intent historical leads, and low-value backfill. you call higher-priority segments with more aggressive cadence escalate to live agents faster.< />>
Handling opt-outs, DNC lists, and consent flags
You must enforce DNC/opt-out lists at ingestion and at each call attempt. Airtable has a hard suppression view that is checked before queueing calls. During calls you capture opt-outs and write them to the suppression list in real time. TCPA compliance is baked into the flows: consent checks, correct caller ID, and retention of call recordings/transcripts.
Voice AI call flow and scripts
Primary call flow blueprint: connect, qualify, reactivate, escalate
The primary flow: dial -> answer detection (machine vs human) -> greet and confirm identity and permission -> qualify interest with short questions -> offer a reactivation path (book, pay, demo) -> if interested, convert (collect minimal data or schedule) -> if complex or high-intent, warm-transfer to human -> update Airtable with outcome and transcript.
Designing natural-sounding TTS prompts and fallback phrases
You design brief, friendly TTS prompts: confirm name, permission to continue, one or two qualifying questions, and a clear CTA. Keep prompts concise, use fallback phrases like “I’m sorry, I didn’t catch that; can you please repeat?” and offer DTMF alternatives. TTS tone should match client brand.
Handling common call outcomes: no answer, voicemail, busy, human pickup
No answer -> log attempt, schedule retry with exponential backoff. Voicemail -> if allowed, leave a short, compliant message and log. Busy -> immediate short retry after small wait or schedule per cadence. Human pickup -> proceed with qualification; route to agent if requested or if intent score exceeds threshold.
Voicemail drop strategy and legal considerations
Voicemail drops can be effective but have legal constraints. In many jurisdictions prerecorded messages require prior express written consent; you must confirm permission before dropping recorded marketing content. Best practice: use a short, non-marketing compliance-friendly message and record consent logs.
Escalation paths to human agents and warm transfers
When intent or prospect requests human contact, the system schedules a warm transfer: the human agent receives a notification with lead context and transcript, and the system initiates a call bridge or callback. You also allow scheduling — if agents are offline, the system books a callback slot.
Automation orchestration and workflow details
Make.com scenario examples and key modules used
Typical Make.com scenarios: Airtable watch records -> filter for next_call_at -> HTTP module to call Vapi dial API -> webhook listener for call events -> save recording to S3 -> call ASR/transcription -> update Airtable record -> send Slack/Email alert on high-intent leads. Key modules: Airtable, HTTP, Webhook, S3, Email/Slack.
How Airtable records drive call queues and state transitions
Airtable views filter records ready to call; Make.com periodically queries that view and moves records into “in-progress.” On call completion, webhooks update status fields and next_call_at. State transitions are atomic so you won’t double-dial leads and you maintain clear attempt counts.
Retries, backoff strategies, and call pacing to maximize connect rates
Use exponential backoff with jitter (e.g., 1st retry after 4 hours, next after 24 hours, then 72 hours) and a max attempt cap (commonly 6 attempts). Pace calls within carrier limits and respect time-of-day windows per lead timezone to maximize connect rates.
Integration patterns for sending call recordings and transcripts to storage
You store raw recordings in S3 (or other blob storage) and push transcripts into Airtable as attachments or text fields. Metadata (confidence, start/end time, intent tags) is stored in the record for search and compliance.
Error handling, alerting, and automated remediation steps
Automated error handling includes webhook retry logic, alerting via Slack or email for failures, and automated remediation like requeuing records or toggling to a fallback orchestration path (n8n). Critical failures escalate to engineers.
AI, transcription, and analytics pipeline
Speech-to-text choices, quality tradeoffs, and cost impacts
You evaluate ASR options (e.g., provider A: high accuracy high cost; provider B: lower cost lower latency). Higher-quality ASR reduces manual review and improves intent detection but costs more per minute. Pick providers based on language, accent handling, and budget.
Using transcription for lead scoring, sentiment, and compliance checks
Transcripts feed NLP models that score intent, detect sentiment, and flag compliance issues (e.g., opt-outs). You surface these scores in Airtable to rank leads and prioritize human follow-up.
Real-time vs batch analytics design decisions
Real-time transcription and intent detection are used when immediate human transfer is needed. Batch processing suits analytics and trend detection. You typically run real-time pipelines for active calls and batch jobs overnight for large-scale tagging and model retraining.
How transcriptions feed dashboards and automated tagging in Airtable
Transcripts are parsed for keywords and phrases and tagged automatically in Airtable (e.g., “interested,” “pricing issue,” “no consent”). Dashboard views aggregate tag counts, conversion rates, and agent handoffs for monitoring.
Confidence thresholds and human review workflows for edge cases
Set confidence thresholds: if ASR or intent confidence