Real Benefits. Operational Context. No shortcuts.

Each project documented here captures part of the client’s previous state — existing stack, specific bottleneck — and records what changed during the first 90 days after go-live.

Close-up overhead view of a logistics operations dashboard on a large monitor, showing route optimization data grids and throughput metrics under cool studio lighting, dark environment, teal accent highlights on the screen

Wide shot of a financial analyst workstation with multiple screens showing data pipeline architecture diagrams and credit scoring dashboards, cool window light from the left, cables and configuration hardware visible on desk, no people

Overhead view of a whiteboard in a manufacturing operations room covered with process flow diagrams and handwritten notes on quality control steps, natural window light from above, no people, technical annotations visible

Salt Life

Smart commercial support for fashion and silver accessories

Salt Life uses a personalized AI agent to enhance the brand’s digital service, supporting the customer journey from the first contact to the qualification of sales opportunities.
The agent answers questions about products, collections, payment methods, delivery times, product availability, and other relevant information related to the buying journey.
It also screens requests, identifies customer interests, captures leads, and strategically guides the conversation until the right moment to transfer it to a human sales representative.

Customization and Efficiency

Its communication style was designed according to the brand’s identity: light, friendly, approachable, and welcoming, while maintaining professionalism.
With the agent in operation, Salt Life now has faster, more standardized, and more available service, allowing the human team to focus on conversations with greater conversion potential.

University Consortium - CEDERJ

The AI agent developed for CEDERJ supports students by providing fast, organized answers aligned with the guidelines of each course.
The solution streamlines a service process that previously depended entirely on the availability of human teams, professors, tutors, or academic coordinators, especially during periods of high demand.
The agent is trained based on curriculum structures, course procedures, institutional documents, academic guidelines, and all relevant knowledge bases provided.

As a result, it can answer recurring questions about subjects, course structure, academic stages, institutional rules, and administrative procedures.
Its implementation contributes to more accessible, continuous, and standardized support, strengthening students’ academic follow-up and allowing human teams to focus on more complex and strategic demands.

Personalized agents, real results

The Salt Life and CEDERJ cases show that AI agents can generate value in different contexts, from commercial service to academic support. In both scenarios, personalization was essential for the agents to operate in alignment with each organization’s identity, processes, and needs. As a result, NexaAI delivers more than automation: it provides intelligent solutions capable of improving workflows, reducing overload, and increasing service quality.

Each agent is developed to understand the client’s reality, use its knowledge bases, and communicate in a way that fits the audience being served. This enables faster, more standardized, and more consistent responses without losing the human aspect of communication. Whether to capture leads, support sales, guide students, or organize recurring demands, NexaAI agents help companies and institutions turn service into efficiency, relationship, and results.

Fast and personalized academic support for students

— Project Aggregate

What the Results Show

Responsiveness

Customization

Student Support

More than answering questions, the agent understands the business context and operates according to its specific guidelines. This alignment strengthens the user journey, reduces rework, and expands operational results.

With AI agents, student support becomes faster and more accessible. Questions are answered based on the institution’s guidelines and procedures. As a result, the academic team gains efficiency, and students receive continuous support throughout their journey.

Considering that lead capture happens within the first minutes of service, the number of customers responded to and satisfied increased significantly.

Do any of these problems sound familiar?

Did you recognize any of these problems?

If the operational context of any case is similar to yours, it is worth discussing what is feasible in your environment — with no commitment and no generic proposal.