Tarmac — a travel LLM that runs the booking.
One model that delivers accuracy, orchestration, governance, and execution. Not a chatbot that talks about travel — an AI that runs it, correctly, end to end.
Tarmac knowledgeThe four things it does
One model. Four jobs.
Accuracy
Real fares, real availability, real fare rules. Not hallucinated guesses. The line between a chatbot and a system you can put a credit card into.
Orchestration
Coordinates the full lifecycle as one coherent flow — search, pricing, booking, ticketing, exchange, settlement, lodging.
Governance
Every action stays inside the rules — corporate policy, fare and ticketing rules, compliance. Nothing executes outside the lines.
Execution
Actually completes the transaction. Books, tickets, pays — not just suggests. Where a wrong answer costs real money.
What it delivers
From hallucination machine to infrastructure.
Turns any AI from a hallucination machine into an accurate one
The line between a chatbot and a system you can put a credit card into.
Transaction-grade reliability
Take payment. Issue tickets. End-to-end completion, not a suggestion to forward to a human.
Enterprise-safe by construction
Trained on owned synthetic and licensed data. Never on customer bookings or PII.
Any AI can talk about a trip. Tarmac is the model that can run one — accurately, within policy, all the way to booked, ticketed, and paid.
Takes travel AI from a demo to infrastructure.
Travel-tuned LLM bakeoff · v0.5
Tarmac vs. the frontier. On travel, it's not close.
FILL Mode (Schema Provided)
| Model | Task | Contract | Intent | Halluc. |
|---|---|---|---|---|
| Tarmac v0.5 | 93.1% | 94.1% | 96.7% | 0.47% |
| GPT-5.4 | 91.1% | 89.1% | 96.2% | 1.97% |
| Claude Opus 4.7 | 59.3% | 64.3% | 71.6% | 5.65% |
| Llama 3.3 70B | 71.6% | 75.6% | 95.8% | 9.4% |
| DeepSeek V4 Pro | 79.4% | 79.4% | 86.5% | 2.66% |
| Gemini 3.1 Pro | 14.5% | 14.5% | 15.5% | 1.97% |
When Otaip tells the model what to do
Lab compares apples to apples.
RAW Mode (Production Conditions)
| Model | Contract Correctness |
|---|---|
| Tarmac v0.5(NATIVE) | 94.1% |
| GPT-5.4 | 1.4% |
| Claude Opus 4.7 | 0.4% |
| Llama 3.3 70B | 0.0% |
| DeepSeek V4 Pro | 0.2% |
| Gemini 3.1 Pro | 0.0% |
When models try to figure it out themselves
Real world compares apples and oranges.
In the real world of travel, Tarmac works. Under 10B yet production-ready. Otaip prevents even 0.47% hallucination as the final gate. Other models return garbage — no domain expertise to guide them. That's the wall between LLMs and real travel bookings we must climb over.
No pricing page. Just a conversation.
Tarmac is sold to enterprises that need a travel-safe LLM with their own deployment, evals, and compliance posture. Tell us what you're building and we'll show you what fits.
Knowledge
Continue learning
Go deeper on Tarmac — evergreen product knowledge, not a blog.
- GuideWhat Tarmac isTelivity’s travel-tuned LLM — built for travel workflows, not generic chat.
- GuideWhy a travel-tuned modelTravel breaks general models on fares, IRROPS, and contracts. Tarmac is aimed at that failure mode.
- GuideReading the Tarmac bakeoffHow to use the on-page bakeoff tables — FILL/RAW metrics live on /tarmac; this guide explains what you are looking at.
- GuideHow to engage on TarmacEnterprise conversation — contact form or info@telivity.app. Bring workload and evaluation criteria.