At Nexariza, we’re currently deep into building out robust multi-agent AI frameworks and scalable...

At Nexariza, we’re currently deep into building out robust multi-agent AI frameworks and scalable...At Nexariza, we’re currently deep into building out robust multi-agent AI frameworks and scalable...

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Nexariza AI

• Aug 5

At Nexariza, we’re currently deep into building out robust multi-agent AI frameworks and scalable applications. Our main focus right now is Findify—an intelligent, multi-agent smart product finder and semantic search engine. It’s been a great challenge architecting the orchestration engine to handle complex user queries, search through massive catalogs, and deliver hyper-personalized recommendations.

Alongside that, we recently rolled out AI Job Factory and we are continuously crafting seamless mobile experiences utilizing Flutter and Riverpod in Android Studio.

I'm curious to hear from other builders in the community—if you are working on multi-agent systems or RAG applications, what are your go-to tools for managing agent routing and memory? Let’s chat in the comments!

#AIApplicationDeveloper #genai #machinelearningprojects #creativeaiflow #Flutter

OpenAI Flutter AI Application Development

Miguel Queirós

max

• Jul 31

Hero section for Semantics AI agents

Web Design Framer Website Development Framer

Juice Lab

pro

• Aug 3

Proof that consistency pays off. Congratulations on the $50k milestone!

Gabriel Gabriel

• Aug 1

Many companies are building AI applications with LLMs but struggle to understand what is happening behind the scenes. Without proper monitoring and tracing, teams cannot identify slow responses, expensive API calls, failed generations, prompt issues, or areas where AI performance can be improved.

I will integrate professional LLM observability solutions like Langfuse and Helicone to help you monitor, trace, debug, and optimize your AI applications. I will set up complete visibility into your LLM workflows, including prompt tracking, token usage analysis, latency monitoring, cost optimization, evaluation pipelines, and production performance insights.

I use modern AI monitoring and development tools including Langfuse, Helicone, LangSmith, Arize Phoenix, OpenLLMetry, OpenTelemetry, Weights & Biases, PromptLayer, Traceloop, and Grafana to build a complete LLM observability system.

My process includes SDK integration, API configuration, trace setup, prompt management, cost tracking, dashboard creation, evaluation workflows, error monitoring, and performance optimization. Whether you are building AI agents, RAG systems, chatbots, or enterprise LLM applications, I will help you reduce AI costs, improve reliability, and gain full visibility into your AI operations.