Artificial Intelligence Applied with Business Precision
Our goal is clear: to increase your productivity and competitiveness sustainably.
We transform
Complexity in Solutions
At IMHOIT IA we transform
complex operational challenges in secure artificial intelligence solutions ,
scalable and aligned
with the business objectives .
We design automations that make the difference
We don't develop AI for fashion or limit ourselves to conversational assistants: we design intelligent automations that optimize critical processes,
improve decision-making and
They allow you to manage complete workflows with efficiency and traceability .
We Create Advantages That Sustain Your Growth
Each implementation starts from a deep understanding of the business environment and seeks to increase productivity , reduce operational friction and generate sustainable competitive advantages .
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.
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:
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
A support team can immediately answer questions about internal policies or product details, without relying on an expert.
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
A logistics company automates the handling of shipment claims using AI that validates the tracking number, checks the status, offers solutions, and only transfers to human support if an exception outside of protocol is detected.
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
An analyst can access historical financial reports, current regulations, or past decisions stored in various formats in seconds.
Learn what our innovative solution is all about
Our Agents are personal assistants designed for each member of your company, adapting to their specific role and needs.
Our agents automate daily tasks , optimize workflows, and improve productivity.
Agents aren't a platform; they're a system integrated into your operations, without requiring you to learn new tools.
How do we take your project from idea to reality ?
1
Initial strategic analysis
We identify processes that can be automated or enhanced with AI . This analysis includes interviews with the teams involved, workflow reviews, and an assessment of potential impact.
2
Dedicated team per project
We assign a team led by a Project Manager (PM), with functional analysts, developers, data scientists, security specialists, and infrastructure engineers. Each one is focused on transforming your needs into concrete results.
3
Multiple points of contact and fluid coordination
From the start, you work with technical and functional specialists to conduct the survey . The PM then coordinates the entire MVP and project development. Depending on the phase, specific technical and business meetings are scheduled.
4
Functional MVP and
real validation
We developed a minimal yet functional MVP focused on quickly measuring impact . This allows for iteration, scaling, and risk reduction before moving to production.
5
Modern collaboration tools
We use Slack and Notion for communication and tracking. This helps us provide complete traceability and real-time visibility into every step.
6
Metodologías ágiles, con base técnica sólida
We adapt agile frameworks based on a clear initial design of the data pipeline, model architecture, and evaluation criteria, allowing us to move quickly without losing technical focus or compromising integration quality. We incorporate iterative model validation , automated testing, and dataset versioning to ensure reproducible results aligned with business objectives .
7
Support, evolution and cost control
We include a post-delivery support layer tailored to the client's solution and needs . We also plan the functional costs associated with infrastructure, tokens, and platform usage with the client.
8
Operational flexibility
We adjust the frequency of deliveries and meetings based on your availability and the project's stage: daily iterations, weekly deliveries, or sprint reviews, as needed.
Agents
Integrations
Our Agents integrate with multiple work tools
Agents
Custom Design
What allows you to manage
