In “Learn this NEW AI Agent, WIN $300,000 (2026),” Liam Tietjens from AI for Hospitality guides you through a practical roadmap to build and monetize an AI voice agent that could position you for the 2026 prize. You’ll see real-world examples and ROI thinking so you can picture how this tech fits your hospitality or service business.
The short video is organized with timestamps so you can jump to what matters: 00:00 quick start, 00:14 Work With Me, 00:32 AI demo, 03:55 walkthrough + ROI calculation, and 10:42 explanation. By following the demo and walkthrough, you’ll be able to replicate the setup, estimate returns, and decide if this agent belongs in your toolkit (#aileadreactivation #n8n #aiagent #aivoiceagent).
Overview of the Contest and Prize
Summary of the $300,000 (2026) competition and objectives
You’re looking at a high-stakes competition with a $300,000 prize in 2026 that rewards practical, measurable AI solutions for hospitality. The objective is to build an AI agent that demonstrably improves guest engagement and revenue metrics—most likely focused on lead reactivation, booking conversion, or operational automation. The contest favors entrants who show a working system, clear metrics, reproducible methods, and real-world ROI that judges can validate quickly.
Eligibility, timelines, and official rules to check
Before you invest time, verify eligibility requirements, submission windows, and required deliverables from the official rules. Typical restrictions include team size, company stage, previous winners, intellectual property declarations, and required documentation like a demo video, reproducible steps, or access to a staging environment. Confirm submission deadlines, format constraints, and any regional or data-privacy conditions that could affect testing or demos.
Evaluation criteria likely used by judges
Judges will usually weigh feasibility, impact, innovation, reproducibility, and clarity of ROI. Expect scoring on technical soundness, quality of the demo, robustness of integrations, data security and privacy compliance, and how convincingly you quantify benefits like conversion lift, revenue per booking, or cost savings. Presentation matters: clear metrics, a reproducible deployment plan, and a tested workflow can distinguish your entry.
Why hospitality-focused AI agents are in demand
You should know that hospitality relies heavily on timely, personalized guest interactions across many touchpoints—reservations, cancellations, upsells, and re-engagement. Labor shortages, high guest expectations, and thin margins make automation compelling. AI voice agents and orchestration platforms can revive cold leads, fill cancellations, and automate routine tasks while keeping the guest experience personal and immediate.
How winning can impact a startup or hospitality operation
Winning a $300,000 prize can accelerate product development, validation, and go-to-market activities. You will gain credibility, press attention, and customer trust—especially if you can demonstrate live ROI. For an operation, adopting the winning approach can reduce acquisition costs, increase booking rates, and free staff from repetitive tasks so they can focus on higher-value guest experiences.
Understand the AI Agent Demonstrated by Liam Tietjens
High-level description of the agent shown in the video
The agent demonstrated by Liam Tietjens is a hospitality-focused AI voice agent integrated into an automation flow (n8n) that proactively re-engages dormant leads and converts them into bookings. It uses natural-sounding voice interaction, integrates with booking systems and messaging channels, and orchestrates follow-ups to move leads through the conversion funnel.
Primary capabilities: voice interaction, automation, lead reactivation
You’ll notice three core capabilities: voice-driven conversations for human-like outreach, automated orchestration to manage follow-up channels and business logic, and lead reactivation workflows designed to resurrect dormant leads and convert them into confirmed bookings or meaningful actions.
How the agent fits into hospitality workflows
The agent plugs into standard hospitality workflows: it can call or message guests, confirm or suggest alternate dates, offer incentives, and update the property management system (PMS). It reduces manual outreach, shortens response time, and ensures every lead is touched consistently using scripted but natural conversations tailored by segmentation.
Unique features highlighted in the demo worth replicating
Replicable features include real-time voice synthesis and recognition, contextual follow-up based on prior interactions, ROI calculation displayed alongside demo outcomes, and an n8n-driven orchestration layer that sequences voice calls, SMS, and booking updates. You’ll want to replicate the transparent ROI reporting and the ability to hand-off to human staff when needed.
Key takeaways for adapting the agent to contest requirements
Focus on reproducibility, measurable outcomes, and clear documentation. Demonstrate how your agent integrates with common hospitality systems, capture pre/post metrics, and provide a clean replayable demo. Emphasize data handling, privacy, and fallback strategies—these aspects often determine a judge’s confidence in a submission.
Video Walkthrough and Key Timestamps
How to use timestamps: 00:00 Intro, 00:14 Work With Me, 00:32 AI Demo, 03:55 Walkthrough + ROI Calculation, 10:42 Explanation
Use the timestamps as a roadmap to extract reproducible elements. Start at 00:00 for context and goals, skip quickly to 00:32 for the live demo, and then scrub through 03:55 to 10:42 for detailed walkthroughs and the ROI math. Treat the timestamps as anchors to capture the specific components, configuration choices, and metrics Liam emphasizes.
What to focus on during the AI Demo at 00:32
At 00:32 pay attention to the flow: how the agent opens the conversation, what prompts are used, how it handles objections, and the latency of responses. Note specific phrases that trigger bookings or confirmations, the transition to human agents, and any visual cues showing system updates (bookings marked as confirmed, CRM entries, etc.).
Elements explained during the Walkthrough and ROI Calculation at 03:55
During the walkthrough at 03:55, listen for how lead lists are fed into the system, the trigger conditions, pricing assumptions, and conversion lift estimates. Capture how costs are broken down—development, voice/SMS fees, and platform costs—and how those costs compare to incremental revenue from reactivated leads.
How the closing Explanation at 10:42 ties features to results
At 10:42 the explanation should connect feature behavior to measurable business results: which conversational patterns produced the highest lift, how orchestration reduced drop-off, and which integrations unlocked automation. Use this section to map each feature to the KPI it impacts—reactivation rate, conversion speed, or average booking value.
Notes to capture while watching for reproducible steps
Make a checklist while watching: endpoints called, authentication used, message templates, error handling, and any configuration values (time windows, call cadence, incentive amounts). Note how demo data was injected and any mock vs live integrations. Those details are essential to reproduce the demo faithfully.
Core Concepts: AI Voice Agents and n8n Automation
Definition and roles of an AI voice agent in hospitality
An AI voice agent is a conversational system that uses speech recognition and synthesis plus an underlying language model to interact with guests by voice. In hospitality it handles outreach, bookings, cancellations, confirmations, and simple requests—operating as an always-available assistant that scales human-like engagement.
Overview of n8n as a low-code automation/orchestration tool
n8n is a low-code workflow automation platform that lets you visually build sequences of triggers, actions, and integrations. It’s ideal for orchestrating multi-step processes—like calling a guest, sending an SMS, updating a CRM, and kicking off follow-ups—without a ton of custom glue code.
How voice agents and n8n interact: triggers, webhooks, APIs
You connect the voice agent and n8n via triggers and webhooks. n8n can trigger outbound calls or messages through an API, receive callbacks for call outcomes, run decision logic, and call LLM endpoints for conversational context. Webhooks act as the glue between real-time voice events and your orchestration logic.
Importance of conversational design and prompt engineering
Good conversational design makes interactions feel natural and purposeful; prompt engineering ensures the LLM produces consistent, contextual responses. You’ll design prompts that enforce brand tone, constrain offers to available inventory, and include fallback responses. The clarity of prompts directly affects conversion rates and error handling.
Tradeoffs: latency, accuracy, costs, and maintainability
You must balance response latency (fast replies vs. deeper reasoning), accuracy (avoiding hallucinations vs. flexible dialogue), and costs (per-call and model usage). Maintainability matters too—complex prompts or brittle integrations increase operational burden. Choose architectures and providers that fit your operational tolerance and cost model.
Step-by-Step Setup: Recreating the Demo
Environment prep: required accounts, dev tools, and security keys
Prepare accounts for your chosen ASR/TTS provider, LLM provider, n8n instance, and any telephony/SMS provider. Set up a staging environment that mirrors production, provision API keys in a secrets manager, and configure role-based access. Have developer tools ready: a REST client, logging tools, and a way to record calls for QA while respecting privacy rules.
Building the voice interface: tools, TTS/ASR choices, and examples
Choose an ASR that balances accuracy and cost for typical hospitality accents and background noise, and a TTS voice that sounds warm and human. Test a few voice options for clarity and empathy. Build the interaction handler to capture intents and entities, and craft canned responses for common flows like rescheduling or confirming a booking.
Creating n8n workflows to manage lead flows and automations
In n8n, model the workflow: ingest lead batches, run a segmentation node, pass leads to a call-scheduling node, invoke the voice agent API, handle callbacks, and update your CRM/database. Use conditional branches for different call outcomes (no answer, voicemail, confirmed) and add retrial or escalation nodes to hand off to humans when required.
Connecting AI model endpoints to n8n via webhooks and API calls
Use webhook nodes in n8n to receive real-time events from your voice provider, and API nodes to call your LLM for dynamic responses. Keep request and response schemas consistent: send context, lead info, and recent interaction history to the model, and parse structured JSON responses for automation decisions.
Testing locally and in a staging environment before live runs
Test call flows end-to-end in staging with realistic data. Validate ASR transcripts, TTS quality, webhook reliability, and the orchestration logic. Run edge-case tests—partial responses, ambiguous intents, and failed calls—to ensure graceful fallbacks and accurate logging before you touch production leads.
Designing an Effective Lead Reactivation Strategy
Defining the target audience and segmentation approach
Start by segmenting leads by recency, booking intent, prior spend, and reason for dormancy. Prioritize high-value, recently active, or previously responsive segments for initial outreach. A targeted approach increases your chances of conversion and reduces wasted spend on low-probability contacts.
Crafting reactivation conversation flows and value propositions
Design flows that open with relevance—remind the guest of prior interest, offer a compelling reason to return, and provide a clear call to action. Test different value props: limited-time discounts, room upgrades, or personalized recommendations. Keep scripts concise and let the agent handle common objections with empathetic, outcome-oriented responses.
Multichannel orchestration: voice, SMS, email, and webhooks
Orchestrate across channels: use voice for immediacy, SMS for quick confirmations and links, and email for richer content or receipts. Use webhooks to synchronize outcomes across channels and ensure a consistent customer state. Channel mixing helps you reach guests on their preferred medium and improves conversion probabilities.
Scheduling, frequency, and cadence to avoid customer fatigue
Respect timing and frequency: start with a gentle outreach window, then back off after a set number of attempts. Use time-of-day and day-of-week patterns informed by your audience. Too frequent outreach can harm brand perception; thoughtful cadence preserves trust while maximizing reach.
Measuring reactivation success: KPIs and short-term goals
Track reactivation rate, conversion rate to booking, average booking value, response time, and cost per reactivated booking. Set short-term goals (e.g., reactivating X% of a segment within Y weeks) and ensure you can report both absolute monetary impact and uplift relative to control groups.
ROI Calculation Deep Dive
Key inputs: conversion lift, average booking value, contact volume
Your ROI depends on three inputs: the lift in conversion rate the agent achieves, the average booking value for reactivated customers, and the number of contacts you attempt. Accurate inputs come from pilot runs or conservative industry benchmarks.
Calculating costs: development, infrastructure, voice/SMS fees, operations
Costs include one-time development, ongoing infrastructure and hosting, per-minute voice fees and SMS costs, LLM inference costs, and operational oversight. Include human-in-the-loop costs for escalations and monitoring. Account for incremental customer support costs from any new bookings.
Sample ROI formula and worked example using demo numbers
A simple ROI formula: Incremental Revenue = Contact Volume × Conversion Lift × Average Booking Value. Net Profit = Incremental Revenue − Total Costs. ROI = Net Profit / Total Costs.
Worked example: if you contact 10,000 dormant leads, achieve a conversion lift of 2% (0.02), and the average booking value is $150, Incremental Revenue = 10,000 × 0.02 × $150 = $30,000. If total costs (dev amortized, infrastructure, voice/SMS, operations) are $8,000, Net Profit = $30,000 − $8,000 = $22,000, and ROI = $22,000 / $8,000 = 275%. Use sensitivity analysis to show outcomes at different lifts and cost levels.
Break-even analysis and sensitivity to conversion rates
Calculate the conversion lift required to break even: Break-even Lift = Total Costs / (Contact Volume × Average Booking Value). Using the example costs of $8,000, contact volume 10,000, and booking value $150, Break-even Lift = 8,000 / (10,000 × 150) ≈ 0.53%. Small changes in conversion lift have large effects on ROI, so demonstrate conservative and optimistic scenarios.
How to present ROI clearly in an entry or pitch deck
Show clear inputs, assumptions, and sensitivity ranges. Present base, conservative, and aggressive cases, and include timelines for payback and scalability. Visualize the pipeline from lead to booking and annotate where the agent contributes to each increment so judges can easily validate your claims.
Technical Stack and Integration Details
Recommended stack components: ASR, TTS, LLM backend, n8n, database
Your stack should include a reliable ASR engine for speech-to-text, a natural-sounding TTS for the agent voice, an LLM backend for dynamic responses and reasoning, n8n for orchestration, and a database (or CRM) to store lead states and outcomes. Add monitoring and secrets management as infrastructure essentials.
Suggested providers and tradeoffs (open-source vs managed)
Managed services offer reliability and lower ops burden but higher per-use costs; open-source components lower costs but increase maintenance. For early experiments, managed ASR/TTS and LLM endpoints accelerate development. If you scale massively, evaluate self-hosted or hybrid approaches to control recurring costs.
Authentication, API rate limits, and retry patterns in n8n
Implement secure API authentication (tokens or OAuth), account for rate limits by queuing or batching requests, and configure exponential backoff with jitter for retries. n8n has retry and error handling nodes—use them to handle transient failures and make workflows idempotent where possible.
Data schema for leads, interactions, and outcome tracking
Design a simple schema: leads table with contact info, segmentation flags, and consent; interactions table with timestamped events, channel, transcript, and outcome; bookings table with booking metadata and revenue. Ensure each interaction is linked to a lead ID and store the model context used for reproducibility.
Monitoring, logging, and observability best practices
Log request/response pairs (redacting sensitive PII), track call latencies, ASR confidence scores, and LLM output quality indicators. Implement alerts for failed workflows, abnormal drop-off rates, or spikes in costs. Use dashboards to correlate agent activity with revenue and operational metrics.
Testing, Evaluation, and Metrics
Functional tests for conversational flows and edge cases
Run functional tests that validate successful booking flows, rescheduling, no-answer handling, and escalation paths. Simulate edge cases like partial transcripts, ambiguous intents, and interruptions. Automate these tests where possible to prevent regressions.
A/B testing experiments to validate messages and timing
Set up controlled A/B tests to compare variations in script wording, incentive levels, call timing, and frequency. Measure statistical significance for small lifts and run tests long enough to capture stable behavior across segments.
Quantitative metrics: reactivation rate, conversion rate, response time
Track core quantitative KPIs: reactivation rate (percentage of contacted leads that become active), conversion rate to booking, average response time, and cost per reactivated booking. Monitor these metrics by segment and channel.
Qualitative evaluation: transcript review and customer sentiment
Regularly review transcripts and recordings to validate tone, correct misrecognitions, and detect customer sentiment. Use sentiment scoring and human audits to catch issues that raw metrics miss and to tune prompts and flows.
How to iterate quickly based on test outcomes
Set short experiment cycles: hypothesize, implement, measure, and iterate. Prioritize changes that target the largest friction points revealed by data and customer feedback. Use canary releases to test changes on a small fraction of traffic before full rollout.
Conclusion
Recap of critical actions to learn and build the AI agent effectively
To compete, you should learn the demo’s voice-agent patterns, replicate the n8n orchestration, and build a reproducible pipeline that demonstrates measurable reactivation lift. Focus on conversational quality, robust integrations, and clean metrics.
Final checklist to prepare a competitive $300,000 contest entry
Your checklist: confirm eligibility and rules, build a working demo with staging data, document reproducible steps and APIs, run pilots to produce ROI numbers, prepare sensitivity analyses, and ensure privacy and security compliance.
Encouragement to iterate quickly and validate with real data
Iterate quickly—small real-data pilots will reveal what really works. Validate assumptions with actual leads, measure outcomes, and refine prompts and cadence. Rapid learning beats perfect theory.
Reminder to document reproducible steps and demonstrate clear ROI
Document every endpoint, prompt, workflow, and dataset you use so judges can reproduce results or validate your claims. Clear ROI math and reproducible steps will make your entry stand out.
Call to action: start building, test, submit, and iterate toward winning
Start building today: assemble your stack, recreate the demo flows from the timestamps, run a pilot, and prepare a submission that highlights reproducibility and demonstrable ROI. Test, refine, and submit—your agent could be the one that wins the $300,000 prize.
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