Artificial intelligence
AI for your business operations, integrated into business operations
We apply artificial intelligence to customer service, processes, internal knowledge bases, and systems the company already uses. The goal is to reduce repetitive work and answer with the context of your business.
Where AI usually stalls in companies
The problem is rarely “lack of a model.” It is lack of the right data, integration, and a place for AI to work with rules, history, and accountability.
A high volume of repeated questions
The same question arrives dozens of times on WhatsApp. The team answers by hand and the lead goes cold.
Internal information is not yet accessible to AI
Manuals, contracts, catalogs, and support tickets remain scattered across folders. Without RAG or a private knowledge base, the model may generate answers that are not grounded in company information.
A standalone AI tool, outside the system
The team pastes text into ChatGPT. The result never enters the CRM, the ERP, or the approval flow.
Fear of putting internal data in a public tool
Policy, customers, and operations cannot go into a generic chat without access control and retention rules.
What SISNAWEB does with AI
We build AI automations, customer-service agents, lead generation, voice synthesis, WhatsApp message auditing, and AIs that answer from the company's private data. We also provide LevoMais AI, an aggregator with more than 250 models, when the team needs controlled access to several providers in one place.
In this context, AI integrates with WhatsApp, CRM, APIs, and access controls, supported by infrastructure for models, embeddings, and processing when needed.
Concrete applications
- Customer-service agents and chatbots with handoff to a human
- AI over private data (policies, products, history)
- Process automation with model-assisted decisions
- LLM integration into the system the company already uses
- Voice: text-to-speech and natural speech in contact flows
- LevoMais AI for access to multiple models with governance
How we implement
Choose the use case
Start with a specific use case, such as customer service, back-office work, document retrieval, or operational support, with a clearly defined objective.
Prepare the context
Data sources, permissions, what AI can answer, and when it should transfer to a person.
Integrate into the channel
WhatsApp, internal system, API, or dashboard. AI needs to exist where the work already happens.
Operate and adjust
We include logs, response reviews, usage limits, and ongoing knowledge-base updates to keep the solution reliable in production.
Who it is for
- Companies that want AI in customer service or in the process, not just a showcase chatbot
- Teams that already use WhatsApp and CRM and need an agent with context
- Operations with an internal knowledge base that nobody consults properly today
- Businesses anywhere in the world that need a vendor to integrate, not just to “try GPT”
Direct answers
What is this, in practice?
It is putting a model to work on a company task with data, rules, and a channel. It is not an AI course.
Does it work with the system we already have?
In most cases, yes, via API, webhook, or an integration layer. Rewriting the entire system only enters the conversation if it is necessary.
What about company data?
We work with private knowledge bases and access controls. The design depends on the case: what can go to the model, what stays internal, and what is only an index for search.
Do you host the AI application?
Yes. Development and production are different stages. Managed infrastructure covers APIs, databases, storage, and operations.
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Frequently asked questions
Want to apply AI to a real process?
Tell us which queue, customer-service flow, or lookup currently consumes the team's time.