Artificial Intelligence
Corporate
Autonomous agents, intelligent document processing and predictive modeling, integrated with governance, ethics and measurable results from day one.
From repetitive tasks to autonomous and controlled processes.
Intelligent automation with real impact
From repetitive tasks to autonomous and controlled processes.
Decisions based on actionable information
From scattered data to actionable insights.
Forecasting to anticipate, not to react
AI as a driver of planning and competitive advantage.
Ethical, governable and regulation-ready AI
Trust integrated from the design.
SOLDIG Artificial Intelligence Capabilities
We design artificial intelligence as an integrated business capability, through real operation, control and governance by design.
Intelligent agents and multi-agent systems for advanced automation
Artificial intelligence agents capable of executing complex processes, making contextual decisions and coordinating with each other, natively integrated with existing business systems, under clear rules of control, traceability and human supervision.
Common use cases:
- check_circle Customer service and support
- check_circle Operations and backoffice
- check_circle Procurement, ITSM and business processes
Artificial intelligence to understand documents and speed up decisions
Intelligent Document Processing (IDP) solutions with AI to understand large volumes of unstructured information - contracts, invoices, reports, emails and PDFs - without relying on rigid templates.
Key capabilities:
- check_circle Intelligent document extraction and classification
- check_circle Automatic summaries and multi-document summaries
- check_circle Identification of relevant clauses, risks and patterns
- check_circle Support for data-driven investment decisions
Tailor-made predictive and classification models for the business
Customized artificial intelligence models, aligned to the operational and strategic reality of each organization.
Key applications:
- check_circle Forecasting: demand, revenue and financial planning
- check_circle Predictive maintenance: failures, downtime and critical assets
- check_circle Classification: fraud, risk, churn and operational prioritization
Governance and trust at source
There is no sustainable artificial intelligence without ethics, governance and a clear definition of scope.
Ethical impact assessment from the ideation phase onwards.
Cross-validation between business, data and technology
Continuous controls during training, deployment and operation
AI that connects those who decide, operate and govern
Address and C-Level
Clarity on where and how automated decisions are made
Traceability and explainability for management and committees
Confidence to scale AI without compromising reputation or compliance
Operations and Business
Less operating friction and manual loading
Actionable insights integrated into real processes
Ability to anticipate scenarios and act before
Data, Technology and Architecture
Governable, explainable and scalable architectures
Cost control, inference and consumption of resources
Native integration with existing systems and data
Risk, Legal and Compliance
Design-based ethical impact assessment
Clear roles of responsibility and validation
Regulatory readiness and full traceability
"We used to have interesting models. Today we have decisions that we can defend to management."
"AI stopped being an IT experiment and became an everyday business tool."
"For the first time we have clear visibility into the impact, risk and cost of each automated decision."
"We used to have interesting models. Today we have decisions that we can defend to management."
"AI stopped being an IT experiment and became an everyday business tool."
"For the first time we have clear visibility into the impact, risk and cost of each automated decision."
Frequently Asked Questions
All you need to know about Artificial Intelligence
Yes, we design solutions that are ready for regulated environments, with explainability, data control and auditability from design.
We incorporate observability and consumption control from the start, avoiding cost overruns and operational surprises as the AI scales.
It depends on scope and criticality, but our approach prioritizes operable and scalable MVPs, not isolated tests without continuity.
Controlling decisions is as important as automating them
Controlling decisions is as important as automating them