Generative AI, especially that based on Large-Scale Language Models (LLMs), is already on the radar of companies seeking to accelerate service, productivity, and personalization. In operation, it brings technical challenges that affect accuracy, safety, and compliance.
At Plusoft, the focus is on transforming generative AI into controlled operational capacity, based on private knowledge per client, data governance, and generation parameters adjusted to reduce risks.
What changes when your company uses generative AI with LLMs
LLMs are able to produce text in natural language, summarize documents, extract intentions, and classify feelings. In corporate contexts, reliability is often the main requirement: the response must be aligned with internal policies, business rules, and updated customer information.
When generative AI operates without controls, risk grows on three fronts: distortion due to data biases, invented answers, and undue exposure of information.
Key challenges of generative AI
Information bias
Models trained with large volumes of public data can reflect social patterns present in those data. In corporate environments, this affects tone, prioritization of recommendations, and even interpretations of user requests, with a direct impact on service and decision-making.
Practical implication: without governance, the model may respond inappropriately to a customer segment, generate language inconsistencies between channels, and create reputational liabilities.
hallucinations
Generative AI can produce plausible but imprecise answers, especially when context is lacking, when the question is ambiguous, or when the parameters favor greater textual variation.
Practical implication: in service, a hallucination can guide the customer into a wrong flow; in internal operations, it can generate incorrect document syntheses and decisions based on unvalidated information.
Strategies used to reduce risk and increase reliability
1) Personalized data and customer context
Generative AI delivers value when working with customer data: processes, products, policies, consumer characteristics, history of interactions, and operational information. This context allows:
- answers aligned with the catalog and business rules;
- summary and analysis of large texts (e.g., calls, emails, documents);
- sentiment and intent analysis based on user content;
- content curation and language standardization across channels.
2) Information control and isolation by client
To reduce contamination by public content and avoid mixing data between companies, the central strategy is to maintain unique knowledge bases per customer.
- each customer operates on a controlled basis;
- a customer's data is not accessible to others;
- the body of knowledge used by AI is delimited by access rules and scope.
Practical implication: the response tends to be more consistent, auditable, and compliant with company policies, with a lower chance of incorporating irrelevant information.
3) Knowledge base curation and governance
The quality of generative AI is directly dependent on the quality of the content that feeds the base.
Curation best practices include:
- validation of internal sources (official documents, policies, FAQs, transactional bases);
- removal of duplicates and old versions;
- standardization of critical nomenclatures and definitions;
- periodic maintenance with area managers.
Practical implication: reduces contradictions, improves the accuracy of recurring questions, and reduces human service rework.
4) Creativity management with generation parameters
The level of “creativity” influences how much the model varies in the answers. In corporate applications, the objective is to control variation to maintain accuracy and predictability.
Good operating practices:
- adjust generation parameters (e.g. temperature) according to the use case;
- use more conservative settings for sensitive topics (policies, financial, procedures);
- allow more flexibility only in low-risk language tasks (e.g., rewriting and summarizing, when allowed).
Practical implication: reduces improvised responses and improves consistency of service at scale.
5) Response monitoring and context compliance
The operation requires observability.
Points that usually make a difference:
- continuous monitoring of responses with samples and auditing;
- adherence metrics (accuracy, fallback rate, human rework);
- operational learning based on real cases, with base adjustments and parameters.
Practical implication: prevents AI from straying from the expected pattern over time and accelerates corrections without wide impact.
Security and privacy as platform requirements
In corporate environments, security must be present in the architecture and in the process.
Common elements in a robust approach include:
- access control by profile and audit trails;
- data protection with encryption and security protocols;
- data retention and classification policies;
- alignment with LGPD and internal customer governance.
How companies can prepare to use generative AI
Consistent adoption depends on four fronts, with clear and defined responsible decisions.
Technological infrastructure
Map necessary integrations (CRM, service, document bases, internal systems), availability requirements, and access governance.
Capacity building and training
Train service, IT, security, and business teams to operate AI: writing internal requests, validating content, and updating the base.
Operation culture and continuous improvement
Define who approves changes to the database, how new documents are entered, which metrics determine parameter adjustment, and when rollback occurs.
Governance and ethics
Create objective policies for: prohibited topics, acceptable language, treatment of sensitive data, traceability, and accountability for decisions.
How to apply generative AI with governance
Generative AI can increase efficiency and quality when it operates with a controlled context, private client base, and governance mechanisms. At Plusoft, the practical application involves personalized data, knowledge isolation, curation, and parameter adjustment to maintain operational predictability.
Do you want to understand how to safely apply generative AI to your service and processes? Talk to Plusoft to evaluate the best knowledge base design, governance, and integrations.
