AI Integration for FareHarbor Custom Quotes

AI integration targets the Quote Request and Quote objects in FareHarbor's data model. When a new request is created via the web form or API, an AI agent is triggered via webhook to ingest the raw customer requirements, group size, dates, and any special requests. The agent then accesses FareHarbor's product catalog via the GET /products API to retrieve real-time availability, pricing tiers, and optional add-ons (like equipment rentals or photography). Using this context, it applies business logic—such as group discounts, seasonal promotions, and minimum participant rules—to construct a line-item proposal.

The core implementation involves a middleware service that sits between FareHarbor and the AI model. This service handles: 1) Data Enrichment, pulling in customer history from a connected CRM to personalize the offer; 2) Pricing Calculation, executing discount logic and tax rules; and 3) Proposal Drafting, using an LLM to generate a narrative cover letter and terms. The final structured quote is posted back to FareHarbor via the POST /quotes endpoint, and a PDF version is generated and attached, ready for sales rep review or automated delivery. This reduces quote creation from 30-45 minutes of manual work to under 2 minutes of automated processing.

Rollout is typically phased, starting with a pilot for a specific tour product or sales team. Governance is critical: all AI-generated quotes should be flagged in the quote.notes field, and a human-in-the-loop approval step can be enforced via FareHarbor's user roles for quotes above a certain value. The system logs all decisions (e.g., discount applied, products selected) for auditability. This pattern not only accelerates sales cycles but ensures consistency and reduces errors in complex, multi-activity quotes. For related integration patterns, see our guides on FareHarbor API Workflows and connecting to CRM systems like Salesforce.

The integration is triggered by a new Lead or Custom Quote Request in FareHarbor. A webhook sends the core inquiry data—including contact details, group size, preferred dates, and selected activities—to a secure orchestration layer. This layer enriches the payload with real-time data from connected systems, such as CRM contact history or ERP pricing rules, before passing it to the AI agent. The agent's core task is to interpret the request, apply the correct discount tiers and optional extras (e.g., transportation, photography, meals) based on business logic, and structure a compliant, professional quote.

The AI agent generates the quote narrative and calculates pricing, but does not write directly to FareHarbor. Instead, it returns a structured JSON payload to a review queue. This queue can be configured for human-in-the-loop approval for quotes above a certain value or containing non-standard terms, or for fully automated posting. Once approved, the system calls the FareHarbor API to create the official Quote record, attaches a generated PDF proposal, and triggers the next step in the configured workflow, such as an automated email to the client or a task for a sales rep.

Key guardrails are built into each step: Input validation sanitizes inquiry data to prevent prompt injection. Pricing logic is executed in a separate, version-controlled service, ensuring the LLM narrates but does not calculate final numbers. All actions are logged to an immutable audit trail linked to the FareHarbor quote ID, detailing the AI's reasoning, the human approver, and the final API call. This architecture ensures the integration is scalable, auditable, and maintains FareHarbor as the single source of truth for all booking and financial data.

A production-grade integration treats the AI as a governed service within your existing FareHarbor operations. This means connecting via FareHarbor's REST API and webhooks to listen for events like quote_requested or booking_modified. The AI agent, hosted in your secure cloud environment (e.g., AWS, GCP), ingests the request payload—containing customer details, product IDs, and group size—and calls your configured LLM (like GPT-4 or Claude) with a structured prompt. This prompt includes your business rules, discount matrices, and optional extras catalog. The generated quote text, pricing breakdown, and PDF proposal are then posted back to FareHarbor via the API, creating or updating the Quote record. All actions are logged with a correlation ID for full auditability.

Security is paramount when handling PII and payment data. The architecture should enforce role-based access control (RBAC) so the AI only sees necessary fields, implement data masking for sensitive information before prompt assembly, and use private endpoints for all LLM calls. All data in transit and at rest should be encrypted. For PCI compliance, ensure the AI never logs or stores full credit card numbers; instead, integrate with Stripe or Braintree tokens already in FareHarbor. A human-in-the-loop approval step can be configured for quotes exceeding a certain value or containing non-standard terms before they are finalized and sent to the client.

A phased rollout mitigates risk and proves value. Start with a pilot workflow, such as automating quotes for a single, high-volume product type (e.g., corporate team-building packages). Run the AI-generated quotes in parallel with manual processes for a defined period, comparing accuracy, discount application, and client acceptance rates. Use this data to refine prompts and business logic. Phase two expands to more product categories and introduces automated PDF generation. The final phase integrates the system with your CRM (like Salesforce or HubSpot) via the same middleware layer, enabling the AI to pull in historical customer data for personalized upselling, completing a closed-loop from inquiry to booked revenue with minimal manual intervention.