Scaling FIT Package Sales with Samyag AI

How AI transformed manual itinerary building into a guided, high-conversion experience

​Customer Snapshot (Anonymised)

• Industry: Leisure & FIT Travel
• Region: India & Middle East
• Business Model: Custom FIT packages (international & domestic)
• Channels: Website enquiries, email, WhatsApp
• Content Sources: Website, Electronic Package Brochures, CRM data

​​The Challenge

FIT travel companies typically have rich content such as destinations, hotels, sightseeing, sample itineraries spread across websites, CMS platforms, and internal systems like CRM and email platforms.

However, despite the availability of all this data, almost every enquiry required manual handling by an agent.​

Key challenges included:

• Travellers had highly personalised requirements (number of days, cities, pace, interests, budget)
• Online content was static and could not adapt to unique needs
• Agents spent significant time understanding requirements and assembling itineraries
• Each enquiry required multiple interactions before a relevant proposal could be shared
• Low conversion rates due to slow turnaround and information overload
• High cost of acquisition, as every enquiry consumed agent time

As volumes grew, scaling required adding more agents directly impacting margins.

Why Samyag AI

The customer needed a way to:

• Leverage existing content more effectively
• Reduce dependency on manual itinerary creation
• Help travellers discover the right itinerary faster
• Improve conversion without increasing headcount

The Solution

Samyag AI was introduced as an AI copilot for FIT discovery and itinerary creation.

Samyag initially consumed all the existing content sources and indexed it. It was then deployed to act as an intelligent interface for both travellers and agents.

Key Capabilities

• Unified content intelligence: Samyag ingested information from all the data sources and could combine the data to provide better recommendations.
• Guided traveller discovery: Travellers could express what they were looking for ranging from destinations, duration, interests, budget and any other requirements using natural language.
• Pre-built itinerary matching: The AI surfaced relevant pre-created itineraries that closely matched traveller preferences.
• Dynamic itinerary creation: When no exact match existed, Samyag generated a custom itinerary based on:
  • Number of days
  • Cities to visit
  • Travel pace and interests
  • Budget and accommodation preferences
• Agent copilot mode: Agents reviewed, refined, and finalised itineraries instead of building them from scratch.

Results

After deploying Samyag.ai, the customer observed:

• Significant reduction in manual effort per enquiry
• Faster turnaround for first itinerary proposals
• Improved traveller engagement due to personalised responses
• Higher enquiry-to-booking conversion
• Lower cost of acquisition per FIT booking

Agents were able to focus on high-value refinement and closing, rather than repetitive itinerary assembly.

Why It Worked

• FIT requirements were handled as structured intelligence, not static pages
• Existing content was finally activated, not just published
• Travellers felt guided instead of overwhelmed
• Agents became advisors, not data assemblers

Outcome

Samyag helped transform FIT package selling from a manual, agent-heavy process into a scalable, intelligent, and conversion-driven workflow thus unlocking growth without increasing operational costs.