inHotel vs. General-purpose AI Tools
Need help deciding whether inHotel is the right AI toolset for your hotel operation?
The table below compares inHotel AI and generic ChatGPT-style tools across key dimensions so you can match capabilities and trade-offs with your property’s goals.
| Dimension | ChatGPT / Claude / Gemini / Perplexity | inHotel |
|---|---|---|
| Owner-first | Not optimized for property value or cost control. | Explicit focus on owner value: connects fragmented knowledge and surfaces open source tech options when paid software is overpriced to maximize ROI. |
| Industry knowledge | Trained on broad internet text. Hospitality nuance is hit-or-miss. | Trained by industry experts, hotel schools, and hospitality consultants. Advice is tailored to your role and highly applicable. |
| Workforce alignment | No shared company-wide memory. Users and projects create separate silos. | Knows your hotel and business plan, and aligns the entire workforce around shared objectives. Interprets every query as “what this means for us.” |
| Operational knowledge hierarchy | Gives equal weight to all document sources. | Policies are enforced, SOPs outrank other internal content, curated advisors outrank generic best practices, which outrank public data. |
| Access to consultants | None built in. | Tap trusted specialist consultants 24/7 through their AI agents, selected by your hotel. |
| Hotel tech tool integrations | None. Requires manual copy-paste of PMS/POS/RMS/CRM reports. | Building API and MCP connectors to PMS, POS, RMS, CRM so assistants can use live data. |
| Skills (custom capabilities) | Only tech-savvy users build agents (GPTs) capable of automating workloads. | Teachable agent skills, backed by inHotel technical assistance and a marketplace, let non‑technical hospitality pros build and resell capabilities, automate their own operation, and turn cost into revenue. |
| Social | No social aspect. | AI personas help colleagues connect, reveal hidden strengths, and build trust. Hotels form social profiles to attract communities, talent, and guests. |
| Job protection | No built-in mechanism to reward industry professionals. Big Tech business model extracts know-how from industry experts. | Hoteliers own their insights and rent them through personal AI agents, so tech augments rather than replaces jobs. |
| File upload at search | Supports image and document uploads for analysis and extraction. | Not yet. Planned and can be prioritized if demand is strong. |
| Canvas editing | Provides a document-style canvas where you can edit, reorganize, and co-develop content directly inside the chat workspace. | No support and not planned. |
| Memory | Supports long-term memory to recall past context across conversations. | Not yet. Planned for both individual and team AIs. |
| Data privacy | Consumer-facing tools are designed to train models on your data. | Your data is private, secured, and never used to train public LLMs. |
| Pricing model and cost | Typically €20 per user per month. A 100‑employee hotel pays €2,000 each month, so only a subset of staff gets access and collaboration suffers. | Pay for assistants (roles), not seats. A 100‑employee hotel can enable everyone for €100 per month. Lifetime plan available: pay once, keep the teammate for life. |