Do you build custom AI models?
When required, yes. In many cases, we combine proven base models with custom architecture and integration logic.
AI SYSTEMS
INTELLIGENCEAI integrated as structured infrastructure.
We design and integrate AI capabilities into existing platforms and operational systems — with defined data flows, governance models and measurable impact.
AI is a system layer — not a feature.
AI should be architected like infrastructure, not bolted on as a plugin. The integration model determines reliability, governance and long-term performance.
Structured AI capabilities.
LLM-powered workflows
Task flows designed around controlled inputs, traceable outputs and operational maintainability.
Document & data extraction systems
Structured extraction pipelines with validation logic, confidence thresholds and auditability.
AI-enhanced SaaS features
Context-aware product modules integrated into existing SaaS architecture and permission models.
Internal automation tools
Operational tooling for repetitive processes with clear human override and accountability paths.
Recommendation & scoring engines
Decision-assist systems connected to business logic, data governance and measurable performance.
Custom AI pipelines
Tailored pipelines from ingestion to delivery, aligned with integration constraints and cost control.
Defined before deployment.
Data pipelines
Data origin, transformation logic and handover points are defined before implementation.
Model selection strategy
Model choices are mapped to use-case complexity, latency requirements and budget constraints.
API boundaries
Service contracts, fallback behaviour and versioning rules are documented early.
Access control
Role-based permissions and data access scopes are integrated into platform governance.
Monitoring & audit logs
Production behaviour is tracked with traceability for prompts, responses and system actions.
Cost control mechanisms
Usage limits, budget thresholds and performance baselines are embedded in delivery design.
From use-case to production.
01
Use-case definition & data audit
Business relevance, data readiness and risk factors are validated first.
02
Architecture & model strategy
System boundaries, model choices and integration pathways are defined.
03
Integration & testing
Capabilities are integrated into the product stack and tested under production constraints.
04
Monitoring & optimisation
Performance, quality and cost signals guide ongoing refinement.
Investment aligned with system complexity.
AI Integration Module
From €7,500
Focused integration for a defined AI use-case inside an existing system.
AI-Enhanced Product Layer
From €18,000
Broader feature layer across product flows with governance and monitoring.
Custom AI Platform
From €35,000
Dedicated platform architecture for multi-capability AI operations.
Common questions.
Can AI be integrated into our existing SaaS?
Yes. Most implementations are built around existing SaaS products, data structures and workflows.
How do you handle sensitive data?
We define data boundaries, access controls, encryption requirements and auditability from the start.
What models do you use?
Model selection depends on use-case requirements, quality targets, latency and cost parameters.
How long does implementation take?
Typical delivery ranges from 8 to 16 weeks, depending on scope and integration complexity.
Do you provide ongoing optimisation?
Yes. We support post-launch monitoring, tuning and iterative extension of capabilities.