We already have a chatbot.

The chatbot answers. Without memory, it always answers the same, with the policy from when it was configured. With context, the answer follows the policy in force and the customer's history.

What about when volume spikes, like Black Friday?

The mechanism doesn't change with the peak: capture happens in the flow and memory is the same for all agents. Human escalation arrives with context ready.

What about customer data?

Data in Brazil, in compliance with the LGPD. Permissions inherited per person and per source, tenant isolation, and an audit trail for every read.

Industry · Retail & E-commerce · schema_pack: 6 types

The exchange policy in force reaches the right agent.

Customer history retrieved in service, the exchange policy in force in every answer, and campaign learning becoming a rule.

Scenario

How context circulates when ValorBrain is in the flow.

01
Service retrieves the decisions and exceptions already granted to the customer, with source.
02
Similar cases appear with what worked before.
03
At escalation, the person in charge receives the context ready, and the customer doesn't repeat themselves.
04
The policy stays versioned, with validity and owner.
05
The approved exception is recorded, with who approved it and until when it holds.
06
The agent answers by today's rule, not by the March version stuck in a PDF.
07
What worked in one campaign becomes a recorded learning, with the originating campaign as source.
08
The next brief is born from that accumulation, not from zero.
09
Briefs, content, and support start from the same memory, each with their role's permission.

Schema pack

Schema pack types: cliente + campanha + política + pedido + exceção + aprendizado

The same mechanism, applied here

Next step

Less starting over. More continuity

Bring a real project. In one conversation we map where context gets lost between the people and agents on your team, and where to start.