Methodical implementation. Minimized risk.
Our methodology separates data maturation (Data Readiness) from production deployment, ensuring that AI operates on solid foundations, not on document chaos.
Typical Integration Phases
From auditing to scalable operation.
Data Readiness & Auditing
We evaluate information silos, permission schemes, data formats, and document maturity. Result: technical roadmap with clear priorities.
Infrastructure Setup & RAG
Core engine deployment, RAG configuration (document ingestion, semantic indexing), roles (RBAC), and connectors to existing systems.
Operational Pilot (Sandbox)
Controlled launch for a team or business unit. Measurement of real KPIs: time per task, response quality, adoption by role.
Rollout and FinOps
Expansion to the entire organization. Consumption monitoring (AI FinOps), model tuning, onboarding of new areas, and continuous optimization.
Continuous Premium Support
Scale and Enterprise contracts include dedicated solution architects who monitor model degradation (drift) and propose quarterly optimizations.
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