AI Knowledge Engine

Precision Required for the Industry

The combination of LLM + structured logic in the Knowledge Engine Tools involves the use of advanced language models alongside defined technical rules and deterministic workflows. The LLM interprets natural language and generates understandable solutions, while the structured logic ensures that responses align with specific regulations and internal procedures. This fusion enables the generation of reliable, actionable, and tailored solutions for the regulated environment.

Use Cases

The AI Knowledge Engine is designed to act as a transversal cognitive layer across the technical, regulatory, and operational processes of the industry. This enables multiple use cases that generate value from day one:

Technical analysis of critical assets

Diagnosis and traceability of failures in pumps, valves, compressors, heat exchangers, etc.

Compliance validation

Verification of compliance with technical standards (API, ISO, ASTM) based on blueprints, designs, or operating procedures.

Structured generation of technical documentation

Automatic creation of maintenance reports, root cause analyses, intervention minutes, and standardized procedures.

Cognitive field assistance

Guided training and on-site support for technicians through conversational AI interaction, specialized prompts, and role-based solution generation.

Expected Impact Indicators

70%

Less time spent on technical searches

50%

Fewer errors due to incorrect procedures

40%

Less dependence on legacy experts

60%

Faster response times to incidents

Compatibility with Existing Systems

The AI Knowledge Engine has been designed to integrate non-invasively and progressively with the existing systems in the technological infrastructure of industrial plants. Recognizing that these organizations already operate with multiple critical platforms (EAM, ERP, SCADA, CAD, EDMS, BIM, among others), the solution acts as a transversal cognitive layer that extracts, relates, and makes actionable the dispersed knowledge without requiring replacements or forced migrations.

This headless and API-first architecture, combined with secure and governed generative AI models, enables the connection and orchestration of information from sources such as:

Maintenance and asset management systems (e.g., IBM Maximo, SAP PM, Infor EAM)

Technical documentation (AutoCAD, Aveva, SmartPlant, SharePoint)

Regulatory repositories and internal databases

BIM models, Digital Twin, and multidisciplinary design environments

This approach achieves a unified technical knowledge experience without altering existing workflows and respecting the security, control, and privacy levels required by the industry.

Services Interaction Algorithm

The SIA is the cognitive core of myservy: an active intelligence system designed to transform every digital service into an autonomous, contextual, and evolutionary entity. This architecture not only automates but enables services to dynamically adapt, make decisions, and respond in a personalized manner to each interaction.

Active intelligence

Personalization at scale

Cognitive interaction

The SIA allows AI to not only interpret but actively operate the structured services within myservy.

Transforms operational experience into reusable knowledge

Replaces multiple non-integrated vertical systems

Activates a culture of continuous improvement and technical traceability

Enhances collaboration between maintenance, engineering, processes, and quality

Ensures complete control of critical information

Reduces the operational burden on engineering and technical support

La imagen muestra una plataforma petrolera offshore en un entorno marítimo, destacando su complejidad industrial y su integración con el paisaje natura

Refineries and integrated petrochemical plants

Multivertical catMining and metallurgical processing facilitiesegorization

Automated and complex manufacturing plants

Energy infrastructure and thermoelectric power plants

Pharmaceutical or regulated industries with extensive technical documentation

Why myservy?

The AI Knowledge Engine is built on the principles of myservy's cognitive service model, an architecture designed to transform data, processes, and experiences into applicable, living, and governed knowledge assets.

myservy combines three fundamental pillars

Applied cognition

Interaction processes designed to help users think better, with AI as a facilitator for complex decisions.

Intelligent service models

Structures that transform technical activities into structured, reusable, and transferable knowledge.

Knowledge and data governance

A modular approach that respects organizational context, validation workflows, and the sensitivity of critical information.

This conceptual foundation allows each AI Knowledge Engine implementation to learn through use, align with operational culture, and enhance the organization’s key capabilities without imposing closed models or generic solutions.

for Industries

for petroleum

for Mining

for Gas

The Solution

It is a cognitive platform that converts technical knowledge into an intelligent network. With AI Knowledge Engine, it offers a modular and adaptable solution that adjusts to each plant, first implementing the functions with the greatest impact and expanding progressively.

This model allows for gradual adoption at the customer's pace, avoiding massive implementations and generating value from the outset.

Secure and Governed Architecture

Flexible infrastructure

No external exposure

Total governance