SERVICE
We start from the business problem, not from the technology. We identify where automation and artificial intelligence reduce manual work, make knowledge easier to reach or improve efficiency. We design contained use cases, measure how they behave and scale only when there is evidence of value. The question is not where we can use artificial intelligence, but where it is worth using.
Less repetitive manual work
We free up team capacity from high-volume, low-value tasks.
Fast access to knowledge
Questions about corporate information answered from authorized, traceable sources.
Fewer errors and less rework
Automatic classification, extraction and validation of information, with rules and controls.
Scaling on evidence
We start with contained use cases and metrics, and scale only once the value is proven.
We look for opportunities where there is a high volume of information, repetitive manual work, document-heavy processes, frequent questions about corporate knowledge, classification or validation processes, coordination between people and multiple systems, or decisions that can be supported with information. We design, measure and scale through the following solutions:
We answer the most common questions about artificial intelligence use cases in companies, agents, RAG, control and traceability, and how to measure the real impact of AI on a business process.
By reducing manual intervention in high-volume tasks — classifying, extracting, validating, reconciling — speeding up access to knowledge scattered across documents and systems, and supporting decisions with consolidated information. The value appears when AI is built into the process, not when it is used as a tool on the side.
Intelligent document processing, assistants that answer questions about corporate knowledge using RAG, agents that carry out contained tasks under control, back-office process automation, information classification and validation, and support for documentation and testing inside the development cycle.
RAG (retrieval-augmented generation) connects a generative model to the organization’s authorized information sources. Instead of answering from its training, the model consults the company’s own documents and systems and answers from that evidence, which makes it possible to cite the source and control which information is used.
By defining which sources the solution may consult, which actions it may carry out and which require human approval; logging every interaction; evaluating answer quality with periodic testing; and setting owners and metrics within the organization’s artificial intelligence governance.
With indicators for the process before and after: cycle time, volume processed per person, error and rework rate, cost per transaction, and response time to the internal or external customer. If the case has no baseline, we measure first and automate afterwards.
Tell us which system is costing you time, money or peace of mind. We will come back with a diagnostic, not a sales pitch. Fill in our form and leave us your details.
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Headquarters:
Avenida 19 # 95 – 20, Piso 19, Edificio Sigma 95, Bogotá Colombia.
LO Fontecilla 201, Piso 4 Oficina 433, Santiago de Chile.
Oficentro Ejecutivo La Sábana. Edificio 4, Piso 1, Costa Rica.
3350 SW 148th Ave Suite 120 Miramar, FL 33027-3258, USA.
info@asesoftware.com
Colombia (+57 601) 6407414
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