Meta Llama AI
Integration
Connect the CRM to Meta's Llama language models for structured travel planning. Generate itinerary content, create day-wise trip plans, and run AI workflows using a configurable Llama-based AI path instead of limiting AI features to a single provider.
Editable CRM Itinerary
Part of the CRM's AI layer. Sends trip requirements to Meta Llama in a structured format, receives machine-readable output, validates it, and saves it directly into the Itinerary Builder as an editable draft.
API key, endpoint, and default model stored centrally through integration settings. When configured, Llama models appear in the AI model selector.
Destination, dates, travelers, budget, hotel type, meals, cab type, interests, and notes. CRM validates all inputs before sending anything to Meta AI.
Trip requirement converted into a travel-planning instruction. Llama asked to return strict JSON with realistic routing, practical timings, and professional content.
CRM checks whether the response can be parsed. Model fallback available across Llama options. Automatic repair step for malformed output. Clear error handling for auth, quota, and model issues.
Output mapped into editable sections: day-wise rows, route, inclusions, exclusions. Quote number generated. AI metadata stored. Team continues in the regular Itinerary Builder.
Same Workflow.
The business can match model choice to its needs for speed, output depth, and preference while keeping the workflow consistent across all AI providers.
If the preferred Meta model is unavailable or rate-limited, the CRM tries alternate Llama model options. The integration detects invalid credentials, unauthorized access, model-not-found conditions, and quota issues, then returns clear workflow errors instead of silent failures.
Dependable Output.
The CRM expects Llama to return strict structured format rather than loose text. Standardized request and response flow for direct CRM storage.
Llama is told to act like a professional travel planner with practical routing, realistic timings, and clean structured responses. Reduces manual rewriting.
Checks source record, destination, dates, traveler counts, budget values, and trip duration limits. Prevents poor input quality from reaching the AI.
Model fallback across Llama options. Clear error detection for auth, quota, and missing models. Automatic repair step for malformed structured output.
Same CRM Workflow.
Meta Llama works inside a CRM that supports multiple AI providers. The business can use Llama as one of its supported AI paths alongside OpenAI and Gemini.
The CRM stores the provider, model, prompt, and raw response with every AI-generated itinerary. Useful for review, quality checks, and provider comparisons over time.
API key, endpoint, and default model managed from one administrative place through the integration and API-vault layer. Controlled, not user-level.
Generated itineraries saved as drafts. The team opens them in the regular builder, revises content, completes pricing, and continues the quotation process.
Through Central Settings
Key, and Default Model
Llama 3.1 8B Support
Based Generation
All Travel Fields
Response Handling
Highlights, Notes
(AM, PM, Evening)
Before API Call
and Retry
(Auth, Quota, Model)
Response Repair
Itinerary Builder
and Audit Trail
for Team Review
See Meta Llama Integration in Action
Watch how Triplide uses Meta Llama models to generate structured itinerary drafts and saves them directly into the CRM for editing, pricing, and sharing.
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