Data Engineering
We integrate, clean, structure and expose data so AI can work with reliable and governable information.
- ETL/ELT, pipelines and data APIs.
- Data lakes, warehouses and migrations.
- Quality, classification and technical governance.
AI and intelligent automation
We turn data, models, assistants and automation into secure, traceable and useful operational capabilities for the business.
Data, Artificial Intelligence & Automation
Applied AI with architecture, reliable data and operational control
We design and implement artificial intelligence and automation solutions that connect models, data, documents, internal systems and real business workflows.
AI is not just about connecting a chatbot. It requires data engineering, LLM architecture, security, privacy, traceability, evaluation, governance and responsible integration with the organization’s processes.
AI Factory
We create assistants, RAG, agents and automations with security, privacy, evaluation and operations from design.
Capabilities
From data to intelligent workflow
We cover the full chain: preparing data, designing the solution, integrating models, automating processes, evaluating results and operating with governance.
Applied AI
We build solutions that use language models and multimodal capabilities to solve concrete business tasks.
- Enterprise assistants and internal copilots.
- RAG, semantic search and document analysis.
- Classification, extraction, summarization and generation.
Agents and agentic systems
We design internal agents capable of using tools, consulting sources, executing tasks and operating under supervision.
- Tool calling, MCP and multi-agent workflows.
- Integration with applications and internal systems.
- Guardrails, permissions, limits and traceability.
Intelligent automation
We automate processes where AI contributes judgment, classification, extraction, writing, analysis or decision support.
- Workflows, system integration and APIs.
- Document and operational automation.
- Event-driven automation and RPA when appropriate.
AI Engineering
Engineering to make AI sustainable
An enterprise AI solution needs more than prompts: it requires architecture, evaluation, observability, cost control, security and operations.
LLM architecture
Model selection, model routing, context design, tools, permissions and operational limits.
RAG and corporate knowledge
Ingestion, chunking, embeddings, vector databases, retrieval, citation and source control.
Quality evaluation
Test cases, evaluation datasets, metrics, human review and regression monitoring.
Observability and costs
Traces, token consumption, latency, errors, prompts, cost per flow and operational alerts.
Privacy and security
Sensitive data handling, minimization, segregation, access controls and prevention of information leakage.
Guardrails and operations
Usage policies, approvals, autonomy limits, fallback, auditability and continuous improvement.
Use cases
Operational AI for real processes
- Corporate AI assistant for internal queries and operational support.
- Automation of legal, commercial, technical or operational documents.
- Intelligent analysis of regulatory compliance, policies and evidence.
- Automatic information classification and extraction of relevant data.
- Internal service chatbot connected to authorized sources and workflows.
- AI for risk analysis, prioritization and executive report generation.
Deliverables
Results that allow operation and improvement
- AI maturity, opportunity and risk assessment.
- Solution architecture, workflows, integrations and controls.
- Functional prototype or minimum operable product.
- Data pipeline, knowledge retrieval or connected automation.
- Evaluation plan, metrics, traceability, guardrails and costs.
- Technical documentation, handover, backlog and evolution roadmap.
Methodology
From opportunity to controlled system
1. Discovery
Process, users, available data, constraints, risks and measurable objective.
2. Design
Architecture, model, sources, permissions, flows, UI, integrations and output criteria.
3. Prototype
Early validation with real data, limited users and minimum controls.
4. Evaluation
Response quality, security, privacy, cost, latency and operational accuracy.
5. Implementation
Integration with systems, automation, roles, monitoring, logs and controlled deployment.
6. Continuous improvement
Feedback, metrics, new sources, prompt adjustments, controls and workflow evolution.
AI governance
Responsible adoption is also designed
We help organize AI inventory, risks, use cases, providers, data, roles, internal policies, impact classification and usage controls.
We can align the work with frameworks such as NIST AI RMF, ISO/IEC 42001, ISO/IEC 23894 and OWASP GenAI Security when the context requires it.
AI security
Controls to reduce risk in systems with models
AI solutions can introduce new attack surfaces, operational errors and information exposure. That is why security is incorporated from design.
Prompt injection
Testing and mitigations against malicious instructions, context manipulation and tool abuse.
RAG Security
Source control, permissions, citation, document contamination, data leakage and knowledge isolation.
Agent Security
Autonomy limits, action authorization, human review, tool abuse and decision traceability.
Data leakage
Prevention of sensitive data, secrets, internal documents, customer information and unauthorized response exposure.
Important criterion
We do not automate for the sake of automation
Before building, we validate whether the use case has enough data, measurable impact, adequate controls and realistic operations. When a simple rule, traditional integration or process improvement solves the problem better, we say so.
Good signals to start
- High-volume repetitive processes.
- Documents that are difficult to analyze manually.
- Scattered internal knowledge.
- Decisions that require traceability.
Contact us
Tell us what you need to build, modernize or protect
Fill in your details and we will schedule an initial conversation to understand your technical challenge.
Contact an expert
- Software development, cloud platforms, AI, automation and security.
- Initial review with no commitment to identify scope, risks and next steps.
- (SLA) We respond in less than 4 business hours.