Employees spend excessive time finding knowledge, comparing documents and preparing repeatable materials.
← All services
Intelligence inside your secure perimeter
Enterprise AI & Local Models
We turn AI from an experiment into an operational tool connected to company documents, policies and systems while keeping data under control.
Discuss a project ↗︎SOLUTION
NORMATIVE REFERENCES
Standards and frameworks
We consider applicable requirements when designing architecture, controls and test programmes.
ISO · 42001Standard · INT+
ISO/IEC 42001:2023 · Artificial intelligence management system
International foundation for responsible AI management.
- Jurisdiction
- INT
- Reviewed
- 2026-08-27
ISO · 42005Standard · INT+
ISO/IEC 42005:2025 · AI system impact assessment
Structure for assessing AI impacts before scaling.
- Jurisdiction
- INT
- Reviewed
- 2026-08-27
US · NIST AI RMFFramework · US+
NIST AI Risk Management Framework
Voluntary framework for trustworthy AI risk management.
- Jurisdiction
- US
- Reviewed
- 2026-08-27
RESPONSIBLE AI
Governance starts before the model launches
How we define approved data, autonomy, evaluation, telemetry and provider replacement.
Explore the control model↗︎Value
What your
business gets
Value
Automation of knowledge-intensive work
Control
Answers grounded in corporate knowledge
Flexibility
Private deployment without external data transfer
Outcome
Integration with existing business systems
When it fits
Situations where the service creates value
We start with the operating situation, constraints and the result that must change for the business — not with a technology list.
AI is already being tried informally, but answer quality, access and corporate-data handling are uncontrolled.
Data cannot be sent to external services, requiring a local or isolated deployment.
Included in the solution
We assemble the required
configuration.
Local LLM and RAG platforms
We deploy language models and RAG platforms in the selected environment, connect corporate knowledge and configure access control.
AI assistants and chatbots
We create assistants for employees and customers for answer retrieval, document preparation, guidance and routine workflow automation.
Document recognition and processing
We configure data extraction from contracts, invoices, email and other documents, validate the result and pass it to operational systems.
API integrations and quality monitoring
We integrate AI with corporate applications through APIs, measure answer quality, collect feedback and manage model versions.
Delivery in detail
What we agree before work begins
We select defined use cases, knowledge sources, models and deployment mode. The architecture covers RAG, access control, logging, integrations, compute capacity and a repeatable quality-evaluation method.
We need representative questions and documents, knowledge owners, data classification, security requirements, integration targets and the business measures used to evaluate value.
A benchmark set is prepared before launch. We measure answer accuracy and coverage, unsupported claims, latency, processing cost and the effect on the selected workflow.
Operations cover quality and feedback, model and knowledge-base versions, access, capacity and source changes. Human review remains in every high-impact action.
Process
Three steps
to launch.
Use case
We identify a workflow where AI can deliver measurable value.
Foundation
We prepare data, models, infrastructure and security controls.
Adoption
We launch, measure quality and improve the system on real requests.
Solution: Enterprise AI & Local Models