How can you make your AI agents location-aware?
Build reliable AI systems
Enable consistent AI decisions with deterministic spatial computations that produce the same answers for the same inputs and constraints.
Lower AI operational costs
Reduce the total cost of running location-aware AI agents. Cut API calls, reduce token usage, and lower latency to reach the right answer faster.
Access trusted intelligence
Ground your AI with location intelligence that goes beyond consumer maps, such as truck restrictions, automotive-grade road data, real-time traffic and more.
How does HERE Location Reasoning unlock new value for AI agents?
Dedicated spatial execution layer
Turn complex queries into structured geospatial execution steps and optimize how and when location services are used. For example, enable an AI agent to identify the route to the airport with the least traffic at a specific time, factoring in arrival time and route constraints.
Deterministic spatial computation
Resolve queries using real spatial logic against HERE’s authoritative data, not the guesswork that most AI systems rely on. Get the same answer across models and workflows, removing the approximations that make AI unreliable in the real world.
System-level optimization
Gain a competitive edge in resolving spatial queries and optimize how and when location services are used. Reduce latency, unnecessary tool calls, API usage and runtime cost by resolving multi-step spatial queries through a structured execution path.
Scaled applications with trusted outcomes
Return validated answers with context and alternatives to make decisions actionable, unlocking new agentic use cases without rebuilding orchestration or prompting logic. For example, HERE Location Reasoning can recommend the best option and explain the alternatives, tradeoffs and constraints behind it.
Deployment flexibility
Deploy where the use case demands. Run HERE Location Reasoning in the cloud, natively in your environment, or on the edge (including in-vehicle) so latency-critical and connectivity-constrained applications get the same deterministic spatial reasoning.
Visuals are conceptual illustrations of AI agent workflows using HERE Location Reasoning and are provided for demonstration purposes only. They may not represent the exact final product. Actual implementation may vary by use case.
Related resources
Why LLMs understand language but not space
True spatial reasoning requires computation over real-world data, something large language models (LLMs) were never designed to do.
How to help LLMs understand the physical world
A concise look at why reliable real-world AI depends on combining language models with deterministic spatial reasoning and real-world location intelligence.
Enabling spatial reasoning for LLMs
Why enabling spatial reasoning in LLMs requires an execution layer that computes location outcomes accurately, deterministically and at scale.
Why LLMs understand language but not space
True spatial reasoning requires computation over real-world data, something large language models (LLMs) were never designed to do.
How to help LLMs understand the physical world
A concise look at why reliable real-world AI depends on combining language models with deterministic spatial reasoning and real-world location intelligence.
Enabling spatial reasoning for LLMs
Why enabling spatial reasoning in LLMs requires an execution layer that computes location outcomes accurately, deterministically and at scale.
Ready to build a location-aware AI use case?
Talk to HERE experts about how location reasoning can help your agents make more accurate, consistent and cost-efficient decisions in the physical world.