Grounded AI. Discoverable services.
AI models can reason, summarise, and generate. But they can't tell you which location is closest, whether it's open, or how long the drive takes. That requires live data from authoritative sources. We connect AI to real-world location intelligence - reliably, at enterprise scale.
One protocol. Every AI platform.
MCP - Model Context Protocol - is the open standard for connecting AI to real-world data and services. Think of it as REST for AI: a common language that lets AI systems discover and use tools safely and reliably.
MCP originated with Anthropic and was donated to the Linux Foundation in December 2025. It is now governed as an open standard, not a single-vendor protocol.
Open standard
Governed by the Linux Foundation. Vendor-neutral by design.
Cross-platform
Works with Claude, ChatGPT, Copilot, VS Code, and any compliant client. Same server, every platform.
Production-ready
Deployed at enterprise scale. Google provides managed MCP servers for its own products.
One connection. Automatic discovery.
An MCP endpoint makes your location services available to any AI platform in a standardised, self-describing way.
AI-powered application connects and receives a menu of available tools
Each tool declaration includes its name, description, what it's useful for and how to use it
The AI incorporates these into its working context
During its workflow, the AI calls appropriate tools based on what it needs
The endpoint returns structured data; the AI takes it from there
Adding new capabilities - fuel prices, live charger status, promotional offers - means adding entries to the menu. The AI discovers them automatically. No integration work or code changes on the AI application side - it's smart enough to know what to do!
Real-world data. Verified answers.
Without connection to the outside world, AI models hallucinate. They generate plausible but incorrect information - wrong addresses, outdated business hours, places that don't exist. For enterprise applications, this isn't a quirk. It's a liability.
Grounding solves this by anchoring AI responses in trusted, current data sources. Instead of generating answers from training data that may be months or years old, the AI queries authoritative systems and returns verified information.
This matters more over time, not less. As AI training data ages, the gap between what models "know" and what's actually true widens. Every business that changes its hours, every new location that opens, every road that closes - these create errors that compound. Real-time data access through grounding is the corrective.
Google recognises this. Their Grounding with Google Maps product in Vertex AI ensures AI agents return factually accurate, current location information. It's the first Google Maps Platform product built natively on MCP - a signal of where the industry is heading.
Move from experiment to production
Most organisations have explored AI. Few have deployed it into production workflows. The gap isn't capability - it's infrastructure. AI systems need structured access to enterprise data, governed workflows, and reliable execution paths.
MCP provides the data access layer. Skills provide the workflow layer.
Skills are pre-built workflows - written instructions that explain the best way to perform complex tasks by combining multiple tools and resources. Instead of an AI agent reasoning through every step, it executes a verified workflow. This makes AI faster, more reliable, and consistent - especially for critical operations where process matters.
Together, MCP servers and Skills form the scaffolding that enterprises need: tools for data access, workflows for reliable execution, and a standard protocol that works across AI platforms.
Why Geo.me for AI and location
Pre-integrated intelligence
Our location services already integrate customer master data, Google Maps Platform capabilities, real-time feeds, and complex geospatial logic. MCP exposes years of accumulated capability through a single connection.
Operational maturity
We already operate location services at enterprise scale. The MCP layer inherits high availability infrastructure, security controls, usage monitoring, and SLA coverage.
Extensibility without development
When a customer wants to add new capabilities, we add them to the menu. Their AI application discovers them automatically. No changes to AI infrastructure required.
Vendor independence
MCP is platform-agnostic. It works with OpenAI, Anthropic, Google, Azure, or custom AI systems. Linux Foundation governance ensures the standard evolves independently of any single vendor.
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location services AI-ready?
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