Travel documents arrive in every format imaginable - agency PDFs, hotel confirmations, transfer vouchers, scanned itineraries. Your product needs clean, structured data from all of them. No hallucinations. No missing fields silently ignored.
PDF, DOCX, scanned images
Multi-layer with fallback
Constrained structured output
Cross-reference and flag
Clean JSON itinerary data
I built and operate a production RAG system that processes 710+ documents across 48 namespaces with Pinecone vector search, reranking, and OCR fallback - serving 10 daily users. The architecture translates directly to travel document structuring. I am not proposing theory - I am proposing patterns I have already shipped.
Five layers, each with a single responsibility. Designed for accuracy, not speed-of-demo.
Multi-format intake with intelligent routing.
Structured output with source tracing.
Semantic search with reranking for grounded answers.
Multi-layer checks before data leaves the pipeline.
Clean API surface for your dev team to consume structured itinerary data. Designed for easy integration into your existing product - RESTful endpoints returning validated JSON conforming to the agreed schema. Batch processing support for multi-document itineraries.
A starting point for structured itinerary data. We will refine this together based on your product's needs.
| Field | Type | Status | Notes |
|---|---|---|---|
| hotel_name | string | Required | Exact name from confirmation |
| check_in | ISO 8601 date | Required | Validated against check_out |
| check_out | ISO 8601 date | Required | Must be after check_in |
| city | string | Required | Can be inferred from transfer docs |
| confirmation_number | string | Optional | Null if not present in source |
| room_type | string | Optional | As stated in document |
| meal_plan | string | Optional | BB, HB, FB, AI, or null |
| price | object | Optional | {amount, currency, per_night} |
| Field | Type | Status | Notes |
|---|---|---|---|
| type | enum | Required | airport_pickup, airport_dropoff, inter_hotel, excursion |
| date | ISO 8601 date | Required | Cross-validated with itinerary |
| pickup_location | string | Required | As specific as source allows |
| dropoff_location | string | Required | Cross-referenced with hotel/activity |
| pickup_time | ISO 8601 time | Optional | Validated against connected events |
| vehicle_type | string | Optional | Private, shared, etc. |
| Field | Type | Status | Notes |
|---|---|---|---|
| name | string | Required | Activity name from booking |
| date | ISO 8601 date | Required | Must fall within trip window |
| location | string | Required | City or venue name |
| start_time | ISO 8601 time | Optional | Null if not specified |
| duration | string | Optional | ISO 8601 duration or natural language |
| booking_reference | string | Optional | Confirmation or voucher number |
| notes | string | Optional | Special instructions, dietary, etc. |
Required fields fail loudly when missing - flagged for review, never silently skipped. Optional fields accept null gracefully. Every field carries a confidence score and source reference. The schema is a contract, not a suggestion.
Structured delivery with your dev team involved at every stage.
Analyze your actual travel documents (sample set). Define the complete data schema for hotels, transfers, activities, flights. Identify document type variations and edge cases. Align with your dev team on the output contract.
Build the document ingestion and text extraction layer. Multi-format support (PDF native text, OCR for scanned docs, DOCX parsing). Implement schema-constrained extraction with source tracing and confidence scoring.
Stand up the vector database with namespace isolation per booking. Implement embedding, chunking by logical document sections, and two-stage retrieval with reranking. Build grounding enforcement so the model only answers from retrieved content.
Build the validation engine - date chronology, cross-document consistency, completeness scoring, conflict detection. Define human escalation rules for low-confidence extractions. Create evaluation test suites.
Work with your dev team to integrate the extraction pipeline into your product. API endpoints, batch processing support, error handling patterns. Documentation and knowledge transfer.
I have built and operate a production system with the same core challenges - document processing, RAG retrieval, structured extraction, and anti-hallucination controls.
Property governance documents (bylaws, declarations, amendments) processed, chunked, and embedded across 48 isolated namespaces. Includes scanned PDFs from the 1990s handled via OCR fallback.
Pinecone serverless with multilingual-e5-large embeddings (1024 dim). Two-stage retrieval: top 20 candidates reranked with bge-reranker-v2-m3 to top 5. Namespace isolation prevents cross-contamination.
Achieved after re-embedding all documents with full chunk text (up to 1,500 chars) instead of 200-char previews. Lesson learned: never truncate what the LLM needs to read for accurate quoting.
Account executives querying the system daily for property-specific document retrieval. PIN-based auth, conversation threading, and document export to .docx. Running since March 2026.
The same patterns that handle property governance documents - multi-format ingestion, OCR fallback, namespace isolation, reranking, source-traced extraction - apply directly to travel documents. Swap property bylaws for hotel confirmations and transfer vouchers, and the pipeline holds.
Fixed-price delivery. You pay for outcomes, not hours. I work fast because I have built this before.
I have built this exact architecture before. Fixed pricing means you get certainty on cost, and I am incentivized to deliver fast. You are paying for the system, not the clock.
I work between Hawaii and Tokyo. Available to start immediately. Send me a sample set of your travel documents and I will return a schema proposal within 48 hours.
Schedule a Call