AI implementation

AI systems built for production.

Your business case needs technical delivery that performs in operation. From agents and knowledge bases to integrations and rollout, architecture, quality and adoption are planned together.

Discuss your AI application

Who this is for

For companies and business units with a prioritised AI initiative, an operational constraint or a pilot ready to move towards production. Commercial objectives and technical requirements provide a shared starting point.

From business case to architecture

Establish value potential, requirements and success criteria first. These inform decisions about data access, models, integrations and operation. Existing systems are considered; each new technical component needs a clear purpose.

Forward Deployed Engineering keeps delivery close to your business team, so feedback from real cases informs development and evaluation.

Agents and agentic workflows

Agents can handle multi-step tasks, use approved tools and adapt to intermediate results. Their scope of action is defined around the task. Critical actions have approvals and clear boundaries.

Defined sequences are structured as workflows. Agents can handle variable steps. Tests against your business requirements establish which combination works.

Knowledge bases and knowledge graphs

The knowledge base is the domain information available to your application: approved documents, system data and structured information. Search and retrieval provide relevant context. Sources, freshness and permissions are part of that foundation.

A knowledge graph represents entities and their relationships, such as customers, products, requirements and evidence. It can structure all or part of a knowledge base and work alongside text or vector search. Your use case determines whether the additional effort is worthwhile.

Harness engineering

The execution environment helps determine how an agent works: which context it receives, which tools it can use, how state is maintained and how failures are handled. Harness engineering designs that environment, including evaluations, logging, monitoring and recovery.

We assess the complete system of model, context, tools and workflow. Measures include domain accuracy, reliable execution, latency and cost.

Rollout and operation

Introduce a tested application in stages. Pilot groups, business acceptance, ownership and relevant training prepare it for wider use. Operating responsibilities, monitoring and changes are agreed with IT.

After rollout, adoption, quality criteria and commercial measures are reviewed. This informs further investment, adjustments or the decision to stop an unsuitable approach.

Discuss your AI application

Which task should the application support? What information and systems does your team need?

office@grabner.tech

A short description is enough. Please do not send confidential documents at this stage.