Case Study
Autonomous Planning System with Constraint-Based Optimization
Developed an agentic planning system that combines web data extraction, contextual reasoning, and constraint-based optimization to generate end-to-end itineraries with minimal user input.
Challenge
AI Traveller needed to convert broad travel intent into realistic itineraries across dynamic availability, budget, and preference constraints. The system had to gather fresh web data, reason over conflicting options, and output practical plans while controlling latency and avoiding repetitive recomputation of intermediate planning steps.
Solution
Helios built a multi-step agentic planning pipeline with SERP-led discovery, structured extraction, and retrieval-augmented reasoning. Constraint-solving components evaluated budget, time windows, and user preferences to produce feasible itinerary options. Caching layers were introduced for repeated route and location computations, while orchestration policies controlled when deeper planning passes were required. The system delivered practical plans with transparent trade-offs and structured outputs for downstream UI rendering.
Technical Responsibilities
- Built a multi-stage agentic planning pipeline for data discovery, extraction, and reasoning
- Implemented retrieval and context assembly for location-aware recommendation quality
- Designed constraint-based optimization for budget, timing, and preference fit
- Integrated caching for repeated intermediate planning steps
- Developed failure-safe orchestration for noisy web sources and partial data
- Implemented structured itinerary output formats for product integration
Outcomes
- End-to-end itinerary generation with low manual intervention
- Improved plan feasibility through explicit constraint-aware optimization
- Lower recomputation overhead via stage-level caching strategies
- Scalable pipeline for continuous expansion of destinations and data sources