Skip to content

Differentiators and scale

The difference shows up in operations.

Integrated context, continuity between agents and people, a team working alongside you and evidence-informed improvement. See how these parts connect and what you can verify.

dr.consulta · Public account

Millions of interactions. A defined context.

Volume is one dimension of an operation. Understanding a result also requires knowing the process, period and source of the figure.

3+ millionmessages exchanged
276 thousandactive users

dr.consulta figures through March 2026. Data provided by the company to EXAME, in an article published on July 28, 2026.

Read the EXAME article ↗
How to interpret these figures

These are dr.consulta figures reported by the company, not a total for Starya’s operations. The article does not specify the user activity window or counting methodology. The figures are not presented as a funnel or conversion rate.

Explore the dr.consulta operation ↗

What supports the delivery

Five parts. One connected operation.

Connected context

The response meets the process.

AI needs to know the right data and what work it can do with it.

  1. 01Request · What the person needs
  2. 02Context · Process sources and data
  3. 03Action · A task with a defined scope
  4. 04Verification · The system's response
Sources, access and integrations defined for the task

Public case study · Sami

Samira connects qualification to quoting and the sales handoff, with the salesperson's involvement.

Explore the reference ↗

How to verify it in your operation

Choose a request and follow it from the conversation to the system. The required data and its origin need to remain identifiable.

For the technical team

Which sources were consulted, with which identity and version? Which integration received the action, and which response lets you verify the final state?

The design depends on the sources and integrations included in the deployment.

Read the five mechanisms and their questions

Connected context

AI needs to know the right data and what work it can do with it.

Request → Context → Action → Verification.

To verify: Choose a request and follow it from the conversation to the system. The required data and its origin need to remain identifiable.

Which sources were consulted, with which identity and version? Which integration received the action, and which response lets you verify the final state?

The design depends on the sources and integrations included in the deployment.

Public case study · Sami ↗

Team + product

Starya combines work alongside the team with its own technology foundation and operational workspace.

Your team → Starya / FDE → Works + OS → Operation.

To verify: Check who handles an issue, who can change the solution and how the company validates the change.

What is the scope of support? Who maintains the integrations? How is the change versioned, tested and rolled back when needed?

The team, responsibilities and ongoing support are defined in the contracted scope.

Learn how we work ↗

People + agents

The handoff should carry what the team needs to continue the work.

Agent → Handoff → Person → Continuity.

To verify: Follow a service interaction that was handed off. Check the reason, the history received and who took over the next step.

How is the queue selected? What happens when no service representative is available? How does the conversation state relate to the task that is still pending?

Ending a conversation does not prove that the task was completed.

Public case study · Unicall / Unimed Paraná ↗

Improvement with evidence

New needs emerge in interactions. The work is to turn these signals into hypotheses and verifiable changes.

Real use → Investigation → Change → Verification.

To verify: Start with an event, identify the records that explain it and define how the effect of an adjustment will be checked.

Which sample supports the hypothesis? Are the comparison conditions equivalent? How can you distinguish correlation, changes in demand and the effect of the change?

A result observed in one operation does not predict the result of another.

Public case study · dr.consulta ↗

Deployment and data

Environment, administration, models and data flows are part of the same deployment decision.

Requirements → Environment → Services → Operation.

To verify: Trace a piece of information: where it enters, where it is processed, who accesses it and which records retain it.

Which data leaves the environment? Which providers and regions are involved? How are retention, copies, permissions and external services managed?

A local model, on its own, does not establish sovereignty over the entire data path.

Explore deployment models ↗

Before moving forward

What is worth asking?

Can Starya work with agents that already exist?

Yes. The assessment starts with agents, systems and workflows already in use. The design needs to define the points to be integrated and the controls that apply to each. A model call through the gateway does not automatically cover actions taken through other paths.

Understand the role of NebulaOS ↗
Are the interactive examples customer results?

The examples are fictional scenarios explaining decisions and responsibilities. Cases identified by organization present public accounts, with their own scope and sources.

Explore examples ↗
How can we start from my company’s situation?

Map the operation, its actions, owners, limits and records. The preliminary assessment organizes these answers without requiring registration. Your team can then investigate further with Starya.

Assess my AI operation ↗