BUSINESS AI: SECURE, PRIVATE, AND PRECISE
Our goal is clear: to increase your productivity and competitiveness in a SUSTAINABLE way
Applied Artificial Intelligence 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 existing systems (ERP, CRM, backend, etc.), without friction or unnecessary dependencies.
Safety and compliance as pillars
Our architectures prevent leaks, critical errors, model "hallucinations," and exposure of sensitive data. All within the applicable regulatory framework.
RAG Enterprise Systems
Our Specialty
What is RAG?
Retrieval-Augmented Generation , known as RAG, is an advanced approach within the field of artificial intelligence that seeks to enhance 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 used to train them, this approach allows access to real-time, up-to-date knowledge sources . This addresses one of the main challenges of LLMs: outdated data, the absence of certain information, and (to some extent) the models' inaccuracies.
In business contexts, this technology is especially useful, as it allows you to take advantage of 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 sources —such as documents, web pages, or specialized databases— RAG allows for the generation of more accurate, relevant, and tailored responses to the specific environment of the queryer .
How does it work?
A RAG-based solution combines the ability to understand a language model with a contextualized and always up-to-date knowledge base.
In the first stage, the organization's data is transformed into vectors (numerical representations of the content) using an embeddings 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 submits a query, that input becomes 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 actual knowledge, without needing to retrain the base model.
Visual comparison between various types of AI solutions and custom RAG developments, based on their complexity, frequency of use, and privacy level. (Exclusive credits to IMHOIT)
Simplified workflow of a traditional RAG architecture, from query to response generation with an LLM. (Exclusive credits to IMHOIT)
RAG Agent Use Cases
Our EXPERIENCE
Although agentive RAG can be adapted to any traditional RAG application, the higher computational demands make it more appropriate for situations that require querying multiple data sources.
RAG agentiva 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 possibilities for autonomous resolution have been exhausted are the most complex cases referred to the appropriate human personnel . This allows for escalation of 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.
Quick access to critical data
With RAG, users can find key information in large volumes of structured or unstructured data without having to search manually .
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 are integrating into multiple work tools
CUSTOM DESIGN
Some examples of what you can manage
Meetings and Calendars
Repetitive Tasks
Reminders
Report Preparation
Document Management
Collections Management
Sales Channel Management
Order Tracking
Candidate pre-selection (CVs)
Decision-Making Assistance
Internal Customer Service (onboarding, continuous training)
