AI and GenAI solutions
Bring AI into the workflows where it can help.
Find information faster, process documents or support recurring workflows. AI's value becomes clear in a specific task. SYNEDAT develops solutions that fit your data, applications and responsibilities. A testable pilot provides the basis for an informed decision about production use.
SYNEDAT PLATFORM
Platform experience for your project
We use these selected tools in SYNEDAT PLATFORM or its delivery processes. We adapt suitable practices to your project and align their integration with your existing systems.
Identities, secrets and policies
Keycloak · OpenBao · External Secrets · Kyverno
Sign-in, technical secrets and platform policies serve different purposes. We connect them with roles, limited permissions and documented exceptions. The selected tools form part of a common access and operating model.
Controlled access and more consistent platform policies.
Observability and operations
Prometheus · Grafana · Alloy · Loki · Tempo
Metrics, logs and traces provide different views of applications and platforms. We organize data sources, dashboards and alert paths around specific operating questions. Retention, sensitive data and costs are considered when planning data collection.
Better incident diagnosis and informed operating decisions.
Data, search and messaging
MySQL · Redis · RabbitMQ · Solr · OpenSearch
Data storage, caching, messaging and search have different consistency, access and recovery requirements. We select and connect these components around your business processes. Ownership and maintenance are part of the integration.
Technical components that fit your data and application processes.
From your requirements to practical results
We start with a task that takes time or involves knowledge that is difficult to access. Together, we define suitable test cases and quality requirements. Data access, confidentiality, human review and running costs are considered from the start. The solution should fit your workflow and demonstrate its value under real conditions.
Make company knowledge easier to access
A knowledge assistant can search approved documents and support answers grounded in those sources. Data preparation, retrieval and permissions are designed together. Test questions show whether relevant content is found and used appropriately.
Process documents into useful information
For quotations, reports and other document types, we assess extraction, summarization and classification. Domain experts define fields and evaluation criteria. Uncertain results follow an appropriate review or exception process.
Integrate assistance into applications
AI capabilities are integrated where users already work. This includes context transfer, permissions, feedback and clear error handling. Existing applications and interfaces are considered in the architecture.
Automate workflows with defined controls
Agents and tools can support bounded tasks. Permitted actions, technical privileges and required approvals are explicitly defined. Retries, cancellation and handover to people form part of testing.
Continuously evaluate quality and costs
Representative tasks make answer quality, errors and resource use visible. We plan repeatable evaluations for changes to models, prompts and data. These help the teams responsible make informed decisions about updates.
Prepare adoption and governance
Deployment also needs roles, rules and clear guidance. We support technical documentation and coordination with the responsible functions. The use case determines which controls and evidence are needed.
A clear path to deployment
Determine the task and success criteria
We select a suitable use case, check data and define representative tasks. Benefits, limits and required approvals are recorded.
Test with real tasks
A limited pilot connects data, model and application. Business users evaluate the results based on the agreed criteria and document open points.
Deploy and improve
The production version receives agreed access controls, evaluation and operational responsibilities. Feedback and changes feed into a documented improvement process.
What you receive
- A prioritized AI use case with appropriate data and evaluation criteria.
- A testable pilot with documented quality, limitations and resource use.
- An agreed path to integration, deployment and ongoing support.
Three ways to get started
AI opportunity and data assessment
For a well-founded start: suitable task, available data, risks and criteria for the pilot.
Discuss your project: AI opportunity and data assessmentAI pilot with evaluation
For a verifiable decision: real tasks, limited integration and documented quality and costs.
Discuss your project: AI pilot with evaluationProduction AI integration
For daily use: application integration, access controls, testing, deployment and agreed operational tasks.
Discuss your project: Production AI integrationQuestions before you decide
Which AI use case is suitable for getting started?
A frequent, clearly defined task with available data and testable results is a useful starting point. Knowledge retrieval or a bounded document-processing task may be suitable. We assess benefits, effort and risks together.
Do we need our own trained language model?
Not necessarily. Existing models, suitable prompts and targeted access to company knowledge can already provide a suitable basis. Whether additional adaptation makes sense is checked on the basis of the task, quality and available data.
How does RAG differ from a general chatbot?
A RAG solution retrieves suitable content from defined sources and provides it to a language model as a context. Data preparation, search, and authorizations are therefore central components. Source references make checking easier, but do not guarantee an error-free answer.
How do we protect confidential information?
We assess data flows, model access, storage locations, logs and permissions across the solution. Vendor terms and organizational requirements must align. Approval criteria and tests also address unauthorized access and unintended information disclosure.
Can AI make changes in systems on its own?
This is possible for explicitly authorized tasks, with limited privileges and clear rules. Critical actions may require human confirmation. Errors, retries and cancellation are tested before production approval.
How do we check if the answers are good enough?
Test cases from your business domain and defined criteria make results comparable. These can cover correctness, source references, completeness and handling questions that cannot be answered. The evaluations are repeated when relevant changes are made.
What are the running costs?
Model use, data preparation, search, infrastructure and the maintenance of evaluations and sources are relevant. Load, response length and need for updates influence consumption. A pilot can provide data for realistic further planning.
What does an initial SYNEDAT proposal include?
An initial scope can include use-case assessment, data review, a bounded pilot and evaluation against agreed criteria. Deliverables and contributions are clearly defined. Integration and production operations are agreed as further steps.
Which task should your first AI use case make easier?
Describe the workflow, available data and your expectations. We help define a testable starting point and the components it needs.
Discuss your project