Frequently Asked Questions

This is ultimately a data play and not a domain expertise play. We believe that there isnt a skill set with someone from the hospitality space that has the ability to do what we are doing. We both have the technical (Paul - commerical and data, Nikhil - technical and strateigc capability) to be able to execute on this which is why we believe you do not need to have domain expertise to create Otel ai.

Right now, we're first focused on making the behind-the-scenes work better through the revenue manager agent and then the HR agent and eventually a rostering agent. But our plan includes a special Front Desk Agent. This agent will handle common questions and tasks automatically, letting hotel staff focus on giving guests special, personal attention. Looking ahead five years, we see AI making guest stays extremely personalized, guessing what guests need before they ask, allowing smooth communication everywhere (phone, email, app), and overall, leading to a faster and better stay because of smart automation working in the background.

Our Otel AI agents are like "digital workers," not just extra software features or basic chatbots.

Our main differences are:

Existing big companies find it hard to copy this because of their old systems, the huge difficulty of connecting deeply with many different vendors, separate departments not working together, and focusing on keeping their current software running instead of building something totally new and AI-focused from the start. We are building for the future of agents right from the beginning.

• Autonomous Action & Workflow Execution: Unlike tools that just suggest things, our agents can plan, think, and do complex series of tasks across different systems all by themselves (within limits). This directly cuts down on work done by hand and allows optimization 24/7.
• Deep Cross-System Integration: We are building agents to work across the different, separate hotel computer systems (PMS, RMS, HR, etc.). They bring data together and automate tasks that current systems can't handle well because they don't talk to each other.
• Hybrid Integration Capability: Our planned "Otel Operator" technology aims to let agents work even with older systems that don't have modern APIs. This gets around a big problem competitors face if they only rely on APIs being available.
• Outcome-Focused Specialization: Each agent is specialized for a main job (like Revenue, Rostering, HR) but they are designed to work together intelligently. This drives overall business results, not just improving one small thing.

Our Ideal Customer Profile (ICP) is multi-property hotel groups in city locations intially. We are focused on groups with more than two hotels to build credibility before engaging with larger international groups. We are currently working with the Aloft hotel in Dublin which is part of the Marriot Group - once we prove the product works, we will then be introduced to the group decision makers as per the GM locally. The problems of handling complexity, separate systems, and big teams are much bigger for hotel groups, so they are the best match for what we offer. The first groups we're working with have hotels ranging from upper-mid-scale to luxury, showing it works for different types of hotels, but the main thing is that they manage more than one hotel.

While we watch AI developments closely, including agent platforms in other areas like 11x in sales, we haven't seen any direct competitors in the hotel world using agentic AI in the same way we envision: working across different departments and acting on their own to handle behind-the-scenes tasks. A company called Riviera provides a front desk solution and is currently in the YC. As mentioned above, we plan to offer this solution but the revenue management agent is much more complicated and value add given the direct impact it will have on increasing revenue. Our focus on making specialized "digital workers" that connect deeply with systems and act on their own for Revenue, HR, Rostering, and Front Desk tasks makes us different. We see interesting agent platforms popping up in other specific industries. You have examples like Harvey for legal work, 11x helping sales teams, maybe Truewind automating finance tasks for startups, or Abridge assisting doctors with documentation.

Our moat is built on several key pillars:

• Raw Product Momentum: We believe in the gingerbread man analogy - you can't catch us. We're both proactive and reactive, unlike incumbents who are just reactive. We're building on an AI-native platform while competitors sit on legacy SaaS products.

• Data Moat: Through deep integration across PMS, RMS, HR, Finance, etc., we aggregate unique, rich operational data (anonymized and aggregated). This data feeds into our custom Otel1 model, creating a continuous learning loop that's hard to replicate.

• Technology & IP: Our agentic AI architecture and 'Otel Operator' for legacy systems create significant technical barriers. We're building specialized algorithms and a secure, compliant platform aligned with ISO standards.

• Integration Moat: The effort required to build and maintain robust integrations across dozens of hotel systems is substantial. Our unified intelligence layer understands the interplay between systems, creating a complex barrier to entry.

• High Switching Costs: Once our agents are embedded in core workflows, removing them becomes highly disruptive. Hotels would need to revert to manual processes or find alternative solutions for multiple functions.

• Network Effects: More customers lead to richer data, improving our AI models and outcomes, which in turn attracts more customers. This creates a virtuous cycle that strengthens our moat over time.

Revenue management is an everyday problem, unlike HR or rostering which are weekly or monthly tasks. We're focused on maximizing our first agent's potential before expanding. When adding new agents, we consider the multiplicative effect - one agent plus a second agent creates more value than just the sum of their parts. We need to ensure our product releases match our brand posture and don't just add features/agents without proper consideration. We won't start a new agent until we have conviction that its ROI exceeds focusing on the current revenue manager agent, and we're ready to hire a dedicated team for it.

We target multi-property groups with two key personas:

• Owners/General Managers: These personas care about two numbers - revenue and profit. We solve directly for this.

• Revenue Managers: These personas care about career advancement. We provide a co-pilot solution that helps them make better decisions, get more time for strategic work, and avoid manual tasks that consume half their day. Our tool amplifies their throughput, helping them perform better in the eyes of their superiors.

We'll move up market when we have comfort around four key areas:

• Adaptability: Ready to handle large enterprise contracts, MSAs, and compliance requirements
• Scalability: Able to support the throughput demanded by large enterprises
• Justificability: Can surface data proving our product delivers value
• Interoperability: Seen as a good citizen in their tech stack, able to both hydrate their ecosystem and extract data to support their stack

Sales cycles vary by segment:

• Multi-property groups: 4-8 weeks
• Larger enterprises: 3-4 months

This timing needs to be factored into pipeline creation and conversion strategies.

For our revenue manager agent, we're considering two main approaches:

1. Subscription with Overage Model:

• Initial onboarding and subscription fee
• Overage charges for usage outside subscription limits
• Leading to renegotiations and multi-year extensions at reduced unit costs
• Based on Jevons paradox - becoming an essential daily tool for revenue managers

2. Value-Based Incentive Model:

• Initial onboarding and subscription fee
• Plus percentage of revenue increase from our recommendations
• Requires robust audit trail and testing to justify the model