Oscar Hoffmann
IT & architecture
- System architecture and production operations
- RAG, embeddings, and retrieval pipelines
- Data layer: Postgres/pgvector, ingestion
- Security: RBAC, isolation, logging
- Integrations and performance optimization
Automate the work.
Keep control.
We build AI systems for automation and knowledge retrieval on your own data — built for operations with security, traceability, and GDPR by design.
- • From unstructured data to decision support
- • Workflow automation
- • Enterprise security and operations
Tailored AI and data solutions for operations
We build production-ready systems that automate repetitive processes and make knowledge operational. The focus is controlled operations, governance, and traceability.
- • Workflow automation (prioritization, routing, quality assurance)
- • Knowledge systems on internal data (RAG)
- • Document intelligence (extraction, structuring, validation)
- • Data transformation of unstructured documents into chunks, metadata, and searchable vector data
Technology method in production
A controlled, vertical delivery pipeline from scoping to operations. Each phase has a concrete deliverable and a decision gate.
01
Workflow and scope analysis
We map your current processes, manual bottlenecks, and data sources so the project starts with a realistic target state. Employee perspectives and concerns are captured from the start.
Deliverables
- Process map
- Time and quality baseline
- Scope document
- Success criteria
- Stakeholder analysis and employee perspective
02
Tailored system design
We design the solution for your context with architecture, data models, governance, and security from day one.
Deliverables
- Solution design
- Data contracts
- RBAC design
- Integration plan
03
Integration with existing systems
We connect the solution to your current system landscape via API, webhook, or batch for stable access and data flow.
Deliverables
- Test-environment integrations
- Stable data ingestion
- Access and logging structure
04
Pilot and implementation with employee involvement
The solution is validated with realistic data and users so impact, quality, and operational readiness are documented before full production. Employees are actively involved to ensure acceptance, well-being, and real value in daily work.
Deliverables
- Pilot in an operational context
- Evaluation framework
- User enablement and onboarding
- Employee feedback and well-being assessment
- Production decision basis
05
Production deployment
We deploy with monitoring, quality assurance, and a controlled release process.
Deliverables
- Production deployment
- Monitoring (metrics, tracing, alerts)
- Control flow
- Release process
06
Ongoing support and optimization
After go-live we continuously optimize performance, cost, and quality through defined operating routines.
Deliverables
- SLA/SLO (if agreed)
- Monthly performance and cost review
- Controlled improvements
- Governance follow-up
Why we are called Vertex
In geometry, a vertex is the point where two or more lines intersect. It often marks a tip or peak. For us, it defines our role precisely: the point where technology, business, and delivery converge into one direction.
Oscar Hoffmann
IT & architecture
- System architecture and production operations
- RAG, embeddings, and retrieval pipelines
- Data layer: Postgres/pgvector, ingestion
- Security: RBAC, isolation, logging
- Integrations and performance optimization
Emil Kanneworff
Commercial & strategy
- B2B go-to-market and partnerships
- Process anchoring and implementation
- Scoping, prioritization, and business case
- Stakeholder management and decision material
- Platform and product strategy
Vertex
Solutions
The convergence point of architecture, business, and reliable delivery.
What we deliver
- Production-grade AI and data flows
- Workflow automation and decision support
- Knowledge systems on internal data (RAG)
- Document intelligence (extraction, structure, validation)
- Governance-by-design (GDPR, traceability, cost control)
Previous cases
See all
FAQ: quick answers
How fast can we move from scoping to pilot?
We typically start with a scoped track where use case, data foundation, and integrations are validated before pilot.
Can this run on top of our Microsoft environment?
Yes. We typically build on existing Entra ID, Microsoft Graph, SharePoint, and relevant APIs.
How is sensitive data handled?
Data is scoped by policy, access is role-based, and critical events can be documented through audit logs.
How do we measure impact?
We track before/after KPIs such as cycle time, error rate, throughput, and quality metrics for prioritized workflows.
Related pages
Access the most important pages on solutions, strategy, security, and next steps.
Explore AI solutions for workflow automation, RAG, and document intelligence
Overview of deliverables, implementation tracks, and FAQ.
Open page
Read our AI strategy for leadership, governance, and implementation
A practical direction for safe AI adoption in organizations.
Open page
Learn about AI technology, security, GDPR, and audit requirements
Technical architecture and controls for enterprise operations.
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Get a concrete assessment of scope, impact, and next steps.
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Book a meeting. We assess use case, integration depth, governance requirements, and expected business value.