Travelgram.

The problem

Travelers collect inspiration everywhere — saved Instagram reels, screenshots, blog links, notes — but that raw material is messy and unstructured with mixed intent, and turning it into an actual plan means searching multiple sites, manually organizing ideas, comparing destinations, and building an itinerary from scratch, then repeating it all next trip.

The most severe pains are translating saved inspiration into meaningful intent, getting generic recommendations instead of personalized ones, itineraries that are jam-packed and over-optimized, and a broken post-trip learning loop where systems never remember nuanced preferences. Booking, privacy concerns around linking Instagram, and group coordination add further friction.

The solution

Travelgram is an AI travel planning assistant that turns saved inspiration into a travel taste profile, personalized destination suggestions, and a day-by-day itinerary the user can refine by chat. It infers travel style, interests, pacing, and aesthetic preferences from behavior rather than typed questions — shifting over time from an active planning tool toward a passive discovery engine.

The MVP deliberately narrows to the highest-value step: moving from scattered inspiration to a plan, rather than solving booking or live pricing. It relies on structured prompting, a curated knowledge pack, and conversational refinement so users can say "make it slower-paced" or "add more cafés" and update only what's necessary.

How it works

The assistant is orchestrated with n8n and runs on ChatGPT 5.5 / 5.4, chosen for strong visual reasoning on uploaded images and memory for learning a user's style over time. For the MVP, users manually upload screenshots or paste links; a webhook scrapes link text or feeds images to an OpenAI agent node, grounded by a Maps/Places API and web search.

Because the model can hallucinate on unknowns, the master prompt enforces strict factuality rules — never invent prices, hours, availability, or "hidden gems" — and separates inferred preferences from general knowledge from information that needs verification, with required "What to verify" sections. This is a RAG-lite approach: the AI extracts structured preferences and generates from the prompt to validate the concept before investing in full RAG.

Who it's for

Travelgram is a B2C startup targeting external users — influencers and millennials — Instagram-inspired travelers who save content constantly but lose the ideas before acting on them.

Over time the customer base is designed to expand into B2B and B2B2C as the model evolves toward commission-based booking revenue, a supplier marketplace, and white-labeled licensing to enterprises. But the initial focus is the individual traveler who wants the assistant to "get" their taste and reduce planning effort compared with searching manually.

Why it matters

The global AI-in-travel market is estimated at roughly $5.38B in 2026, growing at about 26.7% CAGR, with travel planning and itinerary generation a meaningful and expanding slice. Most travel AI tools rely on typed prompts and fail to infer intent from unstructured input — the gap Travelgram targets.

Evaluation used a hybrid of script checks, an LLM-as-judge grader, and human review against a golden test set. After prompt refinement the pass rate rose from 80% to 92.5% (37 of 40 cases) with no critical failures remaining, scoring 4.2–4.7 across relevance, personalization, accuracy, and hallucination avoidance. Launch is staged from an internal pilot to a 10–20% A/B test to a region-by-region rollout gated on quality and safety thresholds.

At a glance

Project

Travelgram

Built by

Apery Kira

One-liner

Travelgram is an AI travel planning assistant that turns saved inspiration like Instagram posts, screenshots, and notes into a travel taste profile, destination suggestions, and a…