Artificial Intelligence & AI Engineering

Results-oriented Artificial Intelligence

Artificial Intelligence can transform processes, unlock enterprise knowledge, automate activities and support better decisions. However, adopting AI requires first identifying where it creates value, choosing the right architecture and maintaining control over costs, risks, security and quality.

• Identify real opportunities to apply Artificial Intelligence.
• Turn enterprise information and documentation into usable knowledge.
• Automate processes through agents and intelligent models.
• Integrate language models into existing applications and systems.
• Apply Machine Learning and Computer Vision to specific problems.
• Incorporate AI into the software engineering lifecycle.
• Design scalable, observable and governable AI solutions.

We assess every need from the business, data, architecture and technology perspectives to define viable solutions—from proofs of concept and laboratories to architectures ready to evolve into enterprise scenarios.

Artificial Intelligence capabilities

1. AI Strategy, Readiness and Transformation

We help identify where Artificial Intelligence can deliver real value to your organization before you invest in technology.

We analyze business objectives, processes, available information, technology capabilities, risks and costs to prioritize use cases and establish a progressive adoption path.

We can help you with:

• AI readiness assessment.
• Use case identification and prioritization.
• Digital and technology maturity assessment.
• Technical feasibility analysis.
• Build-versus-buy assessment.
• Selection among local models, cloud services and APIs.
• Definition of proofs of concept and MVPs.
• Artificial Intelligence adoption roadmaps.
• Assessment of risks, costs and expected return.

The goal is to avoid adopting AI simply because it is available and to focus investment on problems where it can produce measurable results.

2. Enterprise AI Architecture and Governance

We design architectures that integrate Artificial Intelligence with applications, data, infrastructure and business processes in a controlled and evolutionary manner.

An enterprise AI solution does not depend on a model alone. It must address data access, model execution, interaction with other systems, controls and behavior measurement.

We can help you with:

• Artificial Intelligence solution architecture.
• AI reference architectures.
• Assessment of local models versus cloud services.
• Hybrid AI architectures.
• Integration of models, APIs and enterprise applications.
• Model routing and AI service layers.
• Control and guardrail design.
• Traceability and observability.
• Model and agent governance.
• AI-related risk assessment.
• Architectures prepared for RAG and agent-based systems.

Our approach connects technology decisions to business objectives while making security, cost, performance, control and future evolution criteria explicit.

3. RAG and Intelligent Knowledge Management

We design and develop intelligent assistants based on Retrieval-Augmented Generation (RAG) to query enterprise documentation through natural language.

These solutions use manuals, procedures, technical documentation, policies and other knowledge sources as context to generate answers grounded in the organization’s own information.

Capabilities:

• Document processing and structuring.
• Hierarchical document chunking.
• Embedding generation.
• Semantic search.
• Vector databases.
• Contextual information retrieval.
• Integration of images and associated content.
• Integration of local or API-based models.
• Answers grounded in original sources.
• Retrieval and quality evaluation.
• Model latency and usage metrics.

Potential applications:

• Enterprise document assistants.
• Intelligent technical support.
• Manual and procedure lookup.
• Internal training.
• Knowledge management.
• Assistance for system users.
• Semantic search of corporate information.

Our RAG laboratory has allowed us to validate document processing, semantic retrieval, local model usage, contextual image association and benchmark-based evaluation in practice.

4. AI Agents and Intelligent Automation

We design workflows based on specialized Artificial Intelligence agents that collaborate to execute compound processes.

Instead of using a single model to solve the entire problem, an agent-based architecture can distribute responsibilities among components specialized in research, planning, analysis, generation, validation or review.

Capabilities:

• Specialized agent design.
• AI workflow orchestration.
• Multi-agent systems.
• Integration with tools and APIs.
• Tool calling.
• Integration with information sources.
• Human-in-the-loop processes.
• Execution control and checkpoints.
• Retries and failure recovery.
• Result validation.
• Fact-checking and evidence handling.
• Agent observability.

As a laboratory for this architecture, we developed AI Content Production Agents, a workflow of six specialized agents for research, planning, writing, factual verification, SEO and editorial review, with automatic improvement cycles and human approval.

This pattern can be transferred to other business processes where several specialized activities must be coordinated in a controlled manner.

5. AI-Powered Applications and Model Integration

We integrate Artificial Intelligence capabilities into new or existing applications through local models, APIs and AI services.

Artificial Intelligence can be added as a specific capability within an enterprise system without turning the entire application into an exclusively AI-based solution.

We can implement:

• Language model integration through APIs.
• Local models using Ollama.
• Cloud model integration.
• Hybrid local/cloud architectures.
• Intelligent classification.
• Semantic analysis.
• Information extraction.
• Content generation and summarization.
• Scoring and prioritization.
• Decision-process assistance.
• Integration of traditional rules with semantic models and LLMs.

We have applied this experience experimentally in our own projects, including Job-Seeker, Side Hustle Seeker, AI Content Production Pipeline and AI Content Production Agents, where models are part of broader processes rather than isolated components.

6. AI-Assisted Software Engineering

We apply Artificial Intelligence across the software engineering lifecycle to support analysis, architecture, planning, code generation, testing, review and documentation.

Our approach is not limited to asking a model to generate code. We incorporate AI into a structured engineering process with requirements, architectural decisions, validation, evidence and human oversight.

Capabilities:

• AI-assisted requirements analysis.
• Architecture design and review.
• Implementation planning.
• Assisted code generation.
• Test generation and execution.
• Code review.
• Technical documentation.
• Quality gates.
• Traceability across requirements, architecture, implementation and testing.
• Engineering-process observability.
• Human oversight and approval.
• Assessment of AI execution costs, time and outcomes.

As a research and experimentation laboratory, we developed AI Engineering Framework (AIEF), a framework designed to turn prompt-only development into a governed, verifiable and evidence-based engineering process.

AIEF has been used experimentally to build different kinds of applications and explore the systematic use of AI across multiple languages, platforms and frameworks.

7. Applied Artificial Intelligence, Machine Learning and Computer Vision

We develop and evaluate solutions that use Machine Learning, Deep Learning, semantic processing and Computer Vision to solve specific problems.

Our Artificial Intelligence experience includes technologies that predate the current generative-model boom, from expert systems and inference engines to neural networks and computer vision.

Areas of work:

• Machine Learning.
• Classification and regression.
• Neural networks.
• Deep Learning.
• Convolutional neural networks.
• Image classification.
• Object detection.
• Computer Vision.
• YOLO.
• MediaPipe.
• Pose estimation.
• Semantic processing and analysis.
• Rule-based systems.
• Inference engines.

Our accumulated experience includes expert systems applied to healthcare, semantic emotion processing, experimentation with TensorFlow and scikit-learn, and research into Computer Vision for intelligent video surveillance.

From opportunity to an AI solution

Not every problem needs Artificial Intelligence, and not every solution requires the largest or most complex model.

We start with the problem and expected outcomes, then determine the right combination of architecture, data, models, rules, RAG, agents, applications and human oversight.

We can support the different stages of the process:

Assess → Design → Experiment → Implement → Integrate → Measure → Evolve

The goal is to build useful, maintainable AI solutions aligned with the organization’s real needs.

Technologies and tools

We work and experiment with technologies such as:

Python · FastAPI · TensorFlow · scikit-learn · LangGraph · Ollama · DeepSeek · Qwen · nomic-embed-text · ChromaDB · YOLO · MediaPipe · Tavily · Language model APIs · Oracle Cloud Infrastructure AI

Technology selection depends on each project and may combine local models, cloud services and traditional software components.

Outcomes for your organization

Gain a clear Artificial Intelligence adoption strategy, architectures suited to your needs, and solutions that use information, automate processes, incorporate enterprise knowledge and explore new capabilities while maintaining control over risks, costs and quality.

Do you have an idea or process where Artificial Intelligence could help? Let us assess its feasibility and define the next step together.