Customer service that cannot keep up
Customers and leads wait for answers about products, orders and lead times, while the team spends its time on repeated questions and follow-ups that get put off.
Customer service and team support with AI
AI agents for large companies
More capacity to serve customers. Information within reach for decisions.
AI agents help your company respond to customers faster and give teams access to the information they need. We connect business data and rules to support customer service, sales and management, with boundaries defined by your company.
At Beta Rede, an agent guides the team through the commercial brief. See the briefing agent.
Which business unit had the lowest margin in August, and why?
Query agentUnit B had the lowest margin in August: input costs and rework grew faster than revenue.
UnitMarginRevenue (R$k)Cost (R$k)
Unit A14.2%10085.8
Unit B7.8%10092.2
Unit C11.5%10088.5
Sources usedRevenue by unit · updated Sep 14, 6 a.m.Costs by unit · August
2 Unit B costs await posting · partial resultSend to controllership for reviewAwaiting reviewControllership · request logged Sep 14, 9:12 a.m.
This agent's limits
CanQuery revenue and costsExplain the calculation, citing sources
CannotChange prices, rate tables or recordsAccess payroll or HR data
Hands off to a personWhen data is missingWhen the question calls for a commercial decision
Outcome: answer with sources · open item logged · pricing decision stays with the team
Guidance for the team, one step at a time.
At Beta Rede, an agent in Gemini Enterprise guides the commercial brief through more than 50 required items. This excerpt illustrates how missing information can be surfaced.
See the Beta Rede projectCommercial brief
- Project objectiveProvided
- Requested deadlineProvided
- Approval ownerMissing
Who approves the brief before it moves forward?
Awaiting information from the team
How data and rules turn into completed work
We connect company information to the solution’s actions, with review points defined with your team.
Approved sources
Databases, documents and systems approved by the company.
Rules and integrations
Access, criteria and connections that fit the agreed scope.
Solution in use
An agent, an automation or a system built for the work.
Human review
Exceptions, missing information and sensitive decisions stay with the team.
When an agent makes sense
Scattered information
Costs, margins, indicators and documents sit across spreadsheets, systems and paper, and every management question requires manual consolidation or reading.
Quality depends on a few people
The quality of the work depends on following a checklist or a set of rules that only the most experienced people master.
An agent is not the best answer when the problem is simply moving data between systems. In that case, see AI process automation.
What Magical delivers
Agents that talk to customers
Customer service, lead qualification and follow-up, and order or status inquiries, in the channels defined in scope. The agent answers with the information and rules approved by the company and hands the conversation to a person when a case falls outside scope.
Agents for your team
Connection to the sources defined in scope, such as operational or financial data or business rules, to answer management questions and guide processes in natural language. Where it makes sense, the agent can be built on an off-the-shelf corporate tool.
Limits and pilot evaluation
Permitted questions and actions, the sources used when the workflow requires it, and criteria for handing a case to a person. The agent goes into use in a defined area and is evaluated against agreed criteria before its reach is expanded.
Agents and AI adoption in published projects
Logistics · Rodomilk
At Rodomilk, management can now query costs and margins for a fleet of 25 trucks in natural language.
The agent was integrated with the carrier's operational and financial databases. The 25 trucks describe the coverage of the queries, not measured savings.
Services · Beta Rede
At Beta Rede, an agent built on Gemini Enterprise guides the sales briefing through a checklist of more than 50 required items, with the same standard for new and experienced salespeople.
Assisted research and AI adoption · Grupo DND
At Grupo DND, the R&D director redid in two hours, with AI support, a literature review that originally took one month.
This is an assisted application in one specific review, not an autonomous agent or the full R&D cycle.
Define control before going live.
Four decisions the company and Magical agree on within the project scope.
Data and access
Which sources are included, who can query them and with what permissions.
Example: query costs and revenue; restrict HR data.
Action boundaries
What can be queried, prepared or executed with approval.
Example: prepare an analysis; the team decides pricing.
Validation
Which scenarios to test and what needs review before the pilot.
Example: flag an answer when information is missing.
Operations
Who handles exceptions and how to evaluate ongoing use.
Monitoring and maintenance depend on the contracted scope.
Integration and governance criteria in detail
Each agent is designed for one workflow and one set of sources defined in scope, with no ready-made platform or catalog of agents. Before it goes into use, the company and Magical define what it queries, what it executes and when a case is handed to a person.
| Published project | Sources and tools | Use |
|---|---|---|
| Rodomilk | The carrier's operational and financial databases | Cost and margin queries by route, truck and customer |
| Beta Rede | Gemini Enterprise, with a knowledge base of business rules | Sales briefing guided by required items |
| Grupo DND | NotebookLM and a custom AI assistant | Reading, cross-checking and synthesizing scientific literature; formulation and testing stay with the specialist |
Sources, access and the integration approach are verified before they enter the scope, including the service channels when the agent talks to customers. There is no universal integration catalog.
Data, sources and access
Which data sources the agent queries, how often they are updated and which permissions apply to each user profile. Sources, vendors, hosting and retention are defined in the scope before any sensitive data is shared. Data is used only for the contracted project, never to train models for other companies; access is limited to what the project needs and personal data is handled in accordance with the LGPD.
Approval levels and out of scope
Prices, discounts, negotiation, commercial terms and changes to master data follow the rules and approval levels set by the company; the agent does not decide outside them. The scope also defines which questions it does not answer and how that limit is shown to the person asking.
Handoff to a person
Complaints, exceptions, incomplete or inconsistent data and decisions that require approval go to someone on the team. Actions are logged, and the agent's reach only grows after the pilot is evaluated.
Data, decisions, evaluation and continuity are also covered on the AI implementation page.
How an agent project starts
Every service starts with a conversation about your operations. For an agent, the conversation looks for a recurring question, customer interaction or process, with available sources and someone accountable for the answer. The pilot is defined after that assessment.
To prepare the internal conversation, read what needs to be in place before the first agent pilot.
Questions about AI agents
Does Magical build agents that talk to customers?
Yes. The agent can serve customers, qualify and follow up on leads and answer questions about orders or status, within a defined scope. The project sets what it may answer, which systems it queries and when it hands the conversation to a person on the team. Discounts, negotiation and other sensitive commercial decisions follow the company's rules.
Can the agent change data in the company's systems?
Only if that is defined in scope. An agent can start by only querying and explaining information. When an action changes data, the project defines who may trigger it, which rules apply and when a person must approve it.
How is an AI agent different from a generic assistant?
The agent is designed for a task in your company, with connected sources, access rules and review criteria. The project defines what it can query, prepare and execute.
What happens when data is missing?
That behavior is defined in the project. The agent can flag that the answer is incomplete, point out the missing information or hand the question to a responsible person. The quality of the data sources is assessed before the pilot.
Let's assess where an agent helps
Tell us where delays, missing information or service volume are limiting your team. We’ll assess whether an agent could help.
Talk to a partner
Tell us your business priority. We’ll get in touch to understand the context and assess the opportunities.
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