Business Agents with AI
Our goal is clear: to increase your productivity and competitiveness sustainably.
Artificial Intelligence Applied with Business Precision
What makes us different ?
AI designed to execute, not just respond
We automate complex processes, integrating AI models directly into your systems to perform tasks that previously required human intervention or multiple isolated tools.
Custom development and integration
Each solution is customized and integrates with your current systems (ERP, CRM, backend, etc.) without unnecessary friction or dependencies.
Security and compliance as pillars
Our architectures prevent leaks, critical errors, model "hallucinations," or exposure of sensitive data. All within the regulatory framework that applies to you.
RAG Enterprise Systems: Our Specialty
What is RAG?
Retrieval-Augmented Generation (RAG) is an advanced approach in artificial intelligence that seeks to boost the performance of large-scale language models (LLMs) . It achieves this by integrating their generative capabilities with external mechanisms that retrieve relevant information.
Unlike traditional models , which rely solely on the data they were trained on, this approach allows access to updated knowledge sources in real time . This solves one of the main challenges of LLMs: outdatedness, missing data, and (to some extent) model hallucinations.
In business contexts, this technology is especially useful, as it allows you to leverage information stored on platforms such as Confluence , Jira , Google Drive, SharePoint to name a few.
By combining the model's reasoning with data obtained from external databases —such as documents, web pages, or specialized databases— RAG allows for the generation of more precise, relevant, and tailored responses to the query's specific environment .
How does it work?
A RAG-based solution combines the understanding capabilities of a language model with a contextualized and always-updated knowledge base.
In the first stage, the organization's data is transformed into vectors (numerical representations of content) using an embedding model (a model that converts texts into formats that an AI can easily compare and retrieve).
This information is stored in a vector database (a special type of database optimized for semantic searches), which must be kept synchronized with the original sources.
When a user enters a query, that input is converted into a prompt (an instruction that triggers content generation), which is enriched with the most relevant information retrieved from the vector database. The result is an accurate, up-to-date response aligned with the company's real-world knowledge, without the need to retrain the underlying model.
Visual comparison of various types of AI solutions and custom RAG developments, based on their complexity, frequency of use, and privacy level. (Exclusive credits to IMHOIT)
Simplified flow of a traditional RAG architecture, from query to response generation, using an LLM. (Exclusive credits to IMHOIT)
RAG Agent Use Cases: Our Experience
Although agentive RAG can be adapted to any traditional RAG application, its greater computational demands make it more appropriate for situations that require querying multiple data sources. Agentive RAG applications include:
RAG Agent Use Cases: Our Experience
Although agentive RAG can be adapted to any traditional RAG application, its greater computational demands make it more appropriate for situations that require querying multiple data sources. Agentive RAG applications include:
Accurate answers instantly
We implement RAG agents that allow real-time access to technical, operational or regulatory information directly from internal documents, knowledge bases or business systems .
USE CASE
Internal support with immediate access to corporate knowledge
A customer service or human resources team can respond immediately and accurately to inquiries about internal policies, benefits, regulations, or product features, without having to escalate each question to specialized personnel.
AI accesses validated sources, extracts updated information and presents contextualized responses , reducing response times and improving the internal or external user experience.
Scalable automated care
Queries are initially handled by intelligent conversational systems that traverse multiple flows, combine contextual information, and execute automated actions.
Only when all autonomous resolution options have been exhausted are the most complex cases referred to the appropriate human resources . This allows for scaling up care while maintaining efficiency, traceability, and operational quality.
USE CASE
Intelligent automation in logistics claims management
A transportation company optimizes its after-sales service through an AI system that automates claims management.
The system validates the tracking number, checks the shipment's status in real time, analyzes possible causes of deviation, and proposes solutions tailored to the protocol.
Only in the case of unusual exceptions—such as lost packages or complex customs incidents—is the service automatically referred to the human resources department, allowing the operation to be scaled without compromising service quality.
Agile access to critical data
With RAG, users can find key insights in large volumes of structured or unstructured data without the need for manual searching .
USE CASE
Intelligent access to critical documentation for decision-making
A financial or legal analyst can access strategic documents—such as historical financial reports, internal minutes, updated regulations, or criteria from previous decisions—in seconds , even if they are in different formats and repositories.
By using RAGs and vector databases, AI extracts only what is relevant, preserves the context of use, and enables decisions with greater documentary support and less downtime.
Integrations
Our Agents integrate with multiple work tools
Custom Design
What allows you to manage
Meetings
Repetitive Tasks
Reminders
Repetitive Tasks
Repetitive Tasks
Collection Management
Management of
Sales Channels
Repetitive Tasks
Repetitive Tasks
Decision-Making Assistance
Repetitive Tasks
