The real agent network a generic LLM cannot replicate
To measure visibility across generative AI with real results, you have to invest in infrastructure and track every model, tool use and web search update. naia operates this external and neutral layer, with autonomous agents in multiple regions simulating the behavior of the new user.
Rede de execução
Agentes ativos em múltiplas regiões
Why measurement has to happen from the outside
An LLM can ship its own dashboards, but the job of observing how each AI engine responds, in every region and with statistical consistency, requires an independent layer run by someone who is not the one being measured.
Rede de execução
Agentes ativos em múltiplas regiões
Real agents across different regions, simulating real users
Every query is executed by autonomous agents distributed across multiple regions, with distinct IPs and geolocations. It is the new user behavior replicated at scale, not a simulation inside a single provider.
- Execution from independent regions and IPs
- Sampling by engine, language and location
- Detection of regional variation and response bias
Pesquisa contínua
Atualização para cada novo release de modelo
Gemini 3 Flash
Release 2026
ChatGPT com Responses API
Release 2025
Claude Sonnet 4.6
Release 2025
Perplexity Sonar
Release 2025
Grok 4
Release 2025
Every new model, tool use and web search capability is shipped quickly
AI platforms release updates every week. For the result to be real, you must invest in R&D and track every change in model, orchestration and search behavior. This continuous update is part of the cost of playing the game, and it is already embedded in the naia product.
- Support for multiple models with feature parity
- Prompts and query strategies revised on every release
- New modalities such as web search and agentic tools covered end to end
Observação neutra
Mesma marca, cinco motores, resultados comparáveis
GEO Score consolidado
74,8/100
A neutral third party that is not the one being measured
No platform audits itself. To compare how ChatGPT, Gemini, Claude, Perplexity and Grok recommend brands, measurement must come from outside. naia is that layer, with open methodology and verifiable data.
- Comparable results across competing engines
- Public and reproducible methodology
- GEO Score 0 to 100 with 9 weighted components
MCP da naia
Dados reais de visibilidade dentro do assistente
Conectado a Claude Desktop, Claude Code e qualquer cliente compatível com Model Context Protocol.
naia.get_analysis
Análise atual
naia.list_competitors
Concorrentes
naia.get_recommendations
Recomendações
If your team uses Claude, naia becomes even more effective
naia ships an official MCP server that connects visibility data, competitors, recommendations and plans directly inside Claude and any MCP client. The same applies to other engines. The naia network becomes the observation layer of your favorite assistant.
- Official MCP server compatible with Claude Desktop and Claude Code
- Access analyses, competitors, recommendations and plans
- Turns the assistant into a GEO execution agent
Features can be copied. Infrastructure, methodology and continuous R&D cannot.
naia is not a feature. It is the network, the methodology and the research pace that measure, in a neutral way, how the AI engine world is recommending your business. The stronger the LLM ecosystem becomes, the more relevant this layer gets.
- Operating a distributed network with real infrastructure cost
- Continuous updates in response to new models and behaviors
- Third-party neutrality to compare competing engines
- MCP integration amplifies teams already using AI assistants
Frequently asked questions
A platform can deliver internal dashboards, but it is not neutral when evaluating itself and cannot compare competing engines. The observation layer must be external, distributed and independent. That is naia's role, and the stronger the AI ecosystem, the more valuable this independent measurement becomes.
Every AI engine responds differently depending on language, region, history and context. naia replicates the behavior of the new user with autonomous agents in several locations, which guarantees statistically reliable samples and surfaces regional variations that a single test would hide.
Continuous investment in research and development is part of the product. With every model release, new tool use, web search mode or citation format, the team reviews prompts, execution strategies and parsing rules. That is the real cost of delivering a result that reflects reality.
naia provides an official MCP server, compatible with Claude Desktop, Claude Code and any MCP client. It exposes analyses, competitors, recommendations and plans. Teams already using Claude get the naia external data layer integrated, turning the assistant into a GEO execution agent.
The moat is made of three layers that are hard to replicate: a distributed agent network with ongoing infrastructure cost, an open and auditable GEO Score methodology, and the speed of integrating new models and capabilities. Features can be copied, but infrastructure, methodology and R&D pace require sustained investment.