You’re about to follow Liam Tietjens’ full build showing how an AI agent converts dead leads into $300,000, with clear steps and a live demo that makes the process easy to follow. The video is framed for hospitality professionals and shows practical setup, voice and phone automation, and recruitment AI ideas you can adapt to your business.
Timestamps let you jump straight to what matters: the live demo at 0:52, cost breakdown and ROI at 4:11, and the in-depth explanation at 7:20 before the final summary at 12:06. Use those sections to replicate the workflow, estimate costs for your market, and test the lead reactivation process on your own lists.
Video Structure and Timestamps
Breakdown of timestamps from the original video by Liam Tietjens
You get a clear timeline in the video that helps you jump to the exact segments you care about. Liam structures the recording so you can quickly find the intro, the offer pitch, the live demonstration, the cost and ROI discussion, and a deeper technical breakdown. Those timestamps act like a roadmap so you don’t waste time watching parts that are less relevant to your current goal.
What to expect at each timestamp: Intro, Work with Me, Live Demo
At 0:00 Liam sets the stage and explains the problem space: dead leads costing revenue. At 0:36 he transitions to a “Work with Me” pitch where he outlines consulting and execution services. At 0:52 you’ll see the live demo where the AI agent actively re-engages leads. Later segments cover cost/ROI around 4:11 and an in-depth technical explanation beginning at 7:20. Expect a mix of marketing, hands-on proof, and technical transparency.
How the timestamps map to the full build walkthrough
The timestamps map sequentially to a full build walkthrough: introduction and motivation, offer and services, demonstration of functionality, financial justification, and then technical architecture. If you’re following the build, treating the video as a linear tutorial helps — each segment builds on the last, from concept to demo to architecture and implementation details.
Where to find the in-depth explanation and cost breakdown
The bulk of the nitty-gritty lives in the segments at 4:11 (cost breakdown and ROI) and 7:20 (in-depth explanation). Those are the parts you’ll revisit if you want the economics of the project and the system’s design. The video separates practical proof-of-concept (demo) from the modeling of costs and technical choices, so you can focus on the part that matters most to your role.
Suggested viewing order to follow the tutorial effectively
If you’re new, watch straight through to understand the problem, the demo, and the economics. If you’re technically focused, skip to 7:20 for architecture and return to the demo to see the pieces in action. If you’re evaluating the business case, start with 0:52 and 4:11 to see results and ROI, then dive into 7:20 for implementation specifics. Tailor your viewing order to either learn, implement, or evaluate ROI.
Work with Me Offer and Consulting
Overview of the ‘Work with Me’ pitch at 0:36
You’ll hear Liam pitch a “Work with Me” consulting option that packages his experience and the build into an engagement. The offer is framed as an accelerated path to deploy an AI lead reactivation agent without you having to figure out every detail. It’s positioned for business owners or operators who want results quickly and prefer a done-with-you or done-for-you approach.
What consulting or done-for-you services include
Consulting typically includes strategy sessions, data audit and cleaning, agent script design, prompt engineering, telephony setup, integration with your CRM, pilot execution, and performance tuning. Done-for-you services extend to full implementation, testing, and handoff, often with a performance review period and ongoing optimization.
How to prepare your business for agency or consultant collaboration
Before you engage, prepare your CRM exports, access to telephony accounts or the ability to create them, key performance indicators (KPIs) you care about, sample lead lists, and brand voice guidelines. Clear internal decision rights, a single point of contact, and a prioritized list of business outcomes will make collaboration smoother and faster.
Pricing models and engagement timelines described in the video
Liam outlines a mix of pricing models: fixed-fee pilots, retainer-based optimization, or revenue-share/performance incentives. Timelines vary with scope — simple pilots can run a few weeks, while full rollouts are several months. Expect discovery, setup, testing, and iterative tuning phases with milestones tied to deliverables.
Expectations, deliverables, and milestones for a typical engagement
Deliverables typically include a cleaned lead dataset, agent scripts and prompts, telephony and CRM integrations, a working pilot, reporting dashboards, and a plan for scale. Milestones are discovery complete, integration complete, first pilot calls, conversion evaluation, and scale decision. You should expect regular check-ins and transparent reporting during the engagement.
Live Demo Walkthrough
Summary of the live demo segment starting at 0:52
The live demo shows the AI voice agent calling and interacting with previously unresponsive leads in real time. It’s a proof-of-concept to illustrate how automated outreach can recreate natural conversations, qualify leads, and either schedule a follow-up or hand the lead to a salesperson. The demo is designed to reassure you the system works in realistic scenarios.
Demonstration of the AI agent re-engaging dead leads in real time
You see the agent initiate calls, greet recipients with contextual information, handle short back-and-forths, and nudge leads toward booking or next steps. The agent leverages data such as prior interaction history so conversations feel personalized rather than robotic. The live aspect shows latency, tone, and decision-making under realistic constraints.
Examples of lead responses and conversion flows shown
In the demo you observe a range of responses: quick re-engagements where leads confirm interest, partial interest where scheduling is deferred, and refusals. Conversion flows include booking appointments, capturing updated contact preferences, and escalating interested leads to human agents. The demo highlights how different responses route to different downstream actions.
What parts are automated versus manual in the demo
Automation covers dialing, conversational handling, qualification scripts, basic scheduling, and CRM updates. Manual intervention occurs when the lead requests a live human, when complex negotiation is required, or when legal/compliance confirmations are needed. The demo is explicit about the handoff points where a human takes over.
How to replicate the demo environment for testing
To replicate, you’ll need a sandbox telephony account, a set of anonymized dead-lead records, a voice and language model, a small orchestration layer to handle call logic and CRM sync, and a staging CRM. Start with a narrow scope — a few hundred leads — and test call flows, edge cases, and handoffs before scaling.
In-depth Explanation of How the Agent Works
High-level architecture explained during the 7:20 segment
At a high level the agent is an orchestration of model-driven conversation, voice synthesis/recognition, telephony routing, and CRM state management. Requests flow from a scheduler that initiates calls to a conversational engine that decides on responses, to a voice layer that speaks and transcribes, and back into the CRM for state updates. Monitoring and retraining form the feedback loop.
Core components: AI model, voice engine, phone integration, CRM
The AI model handles intent and dialog, the voice engine converts text to speech and speech to text, phone integration manages call setup and DTMF, and the CRM stores lead state and histories. Each component is modular so you can swap providers or scale independently.
Lead lifecycle and state transitions driven by the agent
Leads move through states like new, attempted, engaged, qualified, scheduled, uninterested, or do-not-contact. The agent updates these states based on conversation outcomes, which then triggers follow-up sequences, reminders, or human agent escalations. State transitions ensure you don’t re-contact uninterested leads and that engaged leads are nurtured efficiently.
Decision-making logic and fallback behavior
Decision logic uses a combination of deterministic rules (e.g., do-not-call lists, business hours) and model-driven inference (intent, sentiment). If confidence is low or the lead asks for complex changes, the system falls back to routing the call to a human or scheduling a callback. Fallbacks prevent awkward or noncompliant interactions.
How personalization and context are maintained across interactions
Personalization comes from CRM fields, prior conversation transcripts, and enrichment data. The agent references prior touches, remembers preferences, and uses short-term memory during a call to maintain context. Longer-term context is stored in the CRM for future outreach, ensuring continuity across sessions.
Agent Architecture and Tech Stack
Recommended AI models and providers for conversational reasoning
For conversational reasoning you’ll want a model optimized for dialogue and contextual understanding. Choose providers that offer strong few-shot performance, customizable prompts, and low-latency APIs. You can also use embeddings for retrieval-augmented responses where the agent references past interactions or product details.
Voice synthesis and recognition options for a phone-based agent
Choose a voice synthesis provider with natural prosody and support for SSML to control intonation and pauses. For recognition, pick a speech-to-text engine with high accuracy on the accents and languages of your region, and consider real-time transcription for immediate decision-making. Test models for latency and error rates in noisy environments.
Telephony integrations: SIP, Twilio, and alternative providers
Telephony can be implemented via SIP trunks, Twilio, or other cloud voice providers. Twilio is convenient with APIs for calls, webhooks for events, and easy number provisioning, but alternative providers may offer cost or compliance advantages. Ensure your chosen provider supports call recording, transfers, and regional compliance.
CRM and database choices for storing dead lead data
Use a CRM that allows API access and custom fields for agent state and conversation logs. If you need more flexibility, pair the CRM with a secondary database (SQL or NoSQL) to store transcripts, model outputs, and training labels. Ensure data retention policies comply with privacy and industry regulations.
Orchestration layer and serverless vs containerized deployment
The orchestration layer manages scheduling, retries, call-state, and model calls. Serverless functions can simplify scalability for event-driven tasks, while containerized microservices suit complex, long-lived processes like streaming audio handling. Choose based on expected load, latency needs, and operational expertise.
Data Preparation and Lead Segmentation
How to extract and clean dead lead lists from CRMs
Export leads with fields like last contact date, source, status, and notes. Clean records by removing duplicates, normalizing phone formats, and filtering out do-not-contact entries. Use scripts or ETL tools to standardize data and ensure you don’t inadvertently re-contact customers who opted out.
Important fields to include: last contact, tags, conversion history
Include last contact date, number of contact attempts, tags or campaign identifiers, conversion history, lead score, and any notes that give context. These fields let the agent personalize outreach, prioritize higher-value leads, and avoid repeating failed approaches.
Segmentation strategies based on lead source, recency, and intent
Segment by source (e.g., web leads, events), recency (how long since last contact), prior intent signals (pages viewed, forms submitted), and lead value. Prioritize warmest segments first — recent leads or those who showed high intent — while testing different scripts on colder segments.
Enrichment techniques: append phone verification, demographics
Enrich lists with phone validation to reduce wasted calls, append basic demographics where useful, and add public data such as company size for B2B. Enrichment reduces friction and increases the probability of a successful connection and relevant conversation.
Labeling and training datasets for supervised components
Collect labeled transcripts that classify intents, outcomes, and objection types. Use these labels to fine-tune classifiers or build supervised components for routing and intent detection. Keep labeling consistent and iteratively expand your dataset with edge cases observed during pilot runs.
Conversation Scripts, Prompts, and Tone
Designing cold reactivation scripts that convert without spam
Create concise, respectful scripts that acknowledge prior contact, remind recipients of value, and offer a clear next step. Avoid aggressive frequency or salesy language. Position the outreach as helpful and relevant, and give an easy opt-out option to maintain trust.
Prompt engineering strategies for consistent, goal‑oriented replies
Design prompts that include intent instructions, response length limits, and required data capture points. Use few-shot examples in prompts to guide tone and behavior. Regularly test prompts against real conversations and refine them to reduce hallucination and keep replies on-script.
Handling objections, scheduling, and qualification with branching scripts
Build branching logic for common objections — price, timing, not interested — with short rebuttals and an option to schedule a human. Provide the agent with qualification questions and rules for when to book appointments or escalate. Branching ensures the agent can handle variability without derailing the conversation.
Maintaining brand voice and compliance language in calls
Encode brand voice guidelines into prompts and templates so the agent speaks consistently. Include mandatory compliance language (disclosures, consent statements) in the script and enforce playback where regulations require it. Consistency protects brand reputation and legal standing.
Fallback prompts and escalation paths to human agents
Design fallback prompts that gracefully transfer to a human when confidence is low or when the lead requests complex assistance. Ensure the transfer includes context and transcript so the human agent has the full conversation history and can pick up smoothly.
Voice Agent and Phone Integration
How AI voice agents simulate natural-sounding conversations
Use prosody control, natural pauses, and varied utterances to avoid robotic cadence. Incorporate short filler phrases and confirmations, and tune timing so the agent listens and responds like a human. High-quality TTS and carefully designed prompts make conversations sound authentic.
Configuring call flows, DTMF options, and voicemail handling
Map out call flows for initial greeting, qualification, offers, and transfers. Use DTMF for simple inputs like selecting options or confirming times. Build voicemail handlers that leave concise messages and log attempted contact in your CRM for future outreach.
Warm transfer and live agent takeover procedures
Implement warm transfers that play a short summary to the live agent and route the call after a brief confirmation. Ensure that when the live agent connects they receive the lead’s context and transcript to avoid repeating questions. Smooth handoffs improve conversion and customer experience.
Managing call frequency, pacing, and retry logic
Respect contact windows and implement exponential backoff for retries. Limit daily attempt frequency and set maximum attempts per lead. Pacing prevents harassment complaints, reduces opt-outs, and keeps your calling reputation healthy.
Testing and QA for various carrier and handset behaviors
Test across carriers, handset models, and network conditions to uncover audio clipping, latency issues, or transcription errors. QA includes volume checks, silence detection, and call failure modes. Real-world testing ensures reliability at scale.
Cost Breakdown and ROI Analysis
Detailed cost components: model usage, telephony, hosting, engineering
Costs include model API usage, telephony minutes and number provisioning, hosting and orchestration infrastructure, engineering time for build and maintenance, and possibly third-party integrations or compliance services. Each component scales differently and should be tracked separately.
How Liam estimated costs leading to $300,000 in revenue
Liam breaks down the cost per call, conversion rates, and deal sizes to project revenue. By estimating calls needed to convert a customer and multiplying by conversion rate and average deal value, he extrapolates total revenue potential. The video shows that modest per-call costs can scale into significant revenue when conversion rates and deal values are favorable.
Calculating per-lead cost and break-even point
Calculate per-lead cost by summing telephony cost, model cost per minute, and amortized engineering/hosting per call, then dividing by number of calls. The break-even point is reached when the lifetime value or deal margin of converted leads exceeds this per-lead cost. Use conservative conversion assumptions for planning.
Example ROI scenarios with conversion rate assumptions
Model scenarios with low, medium, and high conversion rates to see sensitivity. Even with conservative conversion assumptions, high average deal values can produce attractive ROI. The video demonstrates that improving conversion by small absolute percentages or increasing average deal size dramatically improves ROI.
Ongoing operational costs and budget planning for scale
Ongoing costs include model consumption as volume grows, telephony fees, monitoring, and staffing for escalations and optimization. Plan budgets for continuous A/B testing, retraining prompts, and compliance updates. Budgeting for scale means forecasting monthly minute usage and API calls and building in margin for experimentation.
Conclusion
Recap of the end-to-end approach to turning dead leads into revenue
You’ve seen how an AI voice agent can systematically re-engage dead leads by combining data preparation, conversational AI, telephony, and CRM orchestration. The approach turns neglected contacts into measurable revenue through targeted, personalized outreach and clear escalation paths.
Key takeaways for building, launching, and scaling the AI agent
Start small with a focused pilot, prioritize high-value segments, and instrument everything for measurement. Use modular components so you can swap providers, and keep human fallback paths in place. Iterate on scripts and prompts, and scale only after validating conversion and compliance.
Risk vs reward considerations and how to get started safely
Risks include regulatory compliance, brand reputation, and wasted spend on poor-quality lists. Mitigate these by validating numbers, respecting do-not-contact lists, limiting frequency, and starting with conservative budgets. The reward is substantial if conversion and deal sizes align with your projections.
Next steps: pilot plan, budget allocation, and success metrics
Create a pilot plan with a few hundred leads, allocate budget for telephony and model usage, and define success metrics like conversion rate, cost per conversion, and revenue per lead. Run the pilot long enough to see statistically significant results and iterate based on findings.
Final encouragement to iterate and adapt the system for your business
You can’t perfect the system in one go — treat the agent as a living system that improves with data and testing. Iterate on scripts, tune models, and adapt segmentation to your market. With careful testing and respectful outreach, you can turn dormant leads into a meaningful revenue channel for your business.
If you want to implement Chat and Voice Agents into your business to reduce missed calls, book more appointments, save time, and make more revenue, book a discovery call here: https://brand.eliteaienterprises.com/widget/bookings/elite-ai-30-min-demo-call
