AI Engineering
From an AI idea to a tested application.
We develop AI applications for clearly defined tasks. Suitable data, comprehensible quality criteria and integration into your processes – from the first attempt to controlled operation – are crucial.
Service modules
What we do for you
Use Cases & Feasibility
We examine which task AI can meaningfully support and how benefits and quality can be evaluated. Available data, effort and alternatives determine the first attempt.
A defined use case with measurable evaluation criteria.
Knowledge Assistants & Search
We open up approved documents and knowledge sources for a targeted search or an assistant. Source references, topicality and access rights are built into the solution.
An assistant whose answers lead back to appropriate sources of knowledge.
Document & Process Support
Information from documents can be prepared in a structured way, classified and transferred to specialist processes. Uncertain results and important decisions are given a clear human review path.
A comprehensible workflow with defined release points.
Model selection & integration
We compare suitable models and operating options based on the task. Interfaces connect the AI function to existing applications; costs, data paths and response times remain part of the decision.
A reasoned technical choice and an integrated pilot solution.
Evaluation & Protective Measures
Representative test cases check response quality, unwanted spending, and permission limits. We look at misleading inputs, confidential data, and the limits of automated actions, among other things.
A documented test catalog with release and cancellation criteria.
Operations & Continuous Improvement
We plan versioning, monitoring, feedback and the updating of knowledge bases. Model changes and new data are checked before they change the productive process.
An operating plan with clear responsibility and regulated changes.
Application examples
AI Engineering Use Cases
Typical initial situations – together we adapt the scope and procedure to your project.
Making internal knowledge easier to find
Employees search for information in distributed documents. A limited knowledge assistant supports research with reference to sources and existing access limits.
Preparing documents for processing
Incoming documents contain recurring information. AI supports extraction and classification; humans check critical or uncertain results.
Making a successful prototype resilient
A demo works with individual examples. We expand tests, look at error cases and connect the application with real release and operational processes.
Collaboration
From the first question to the handover
Narrow down the task
We determine users, data sources, benefits, and prohibited actions.
Pilot & Test Setup
A small solution is evaluated on the basis of representative examples.
Integrate into the process
Authorizations, approvals and error handling connect AI and the specialist system.
Control operations
Quality, costs and changes are continuously monitored and reviewed.
Your result
You can continue working with it
- Assessed use case and data requirements
- Pilot with documented limits
- Test Cases, Quality Certificates and Release Criteria
- Plan for Operations, Feedback and Changes
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.
Frequently Asked Questions
Does a separate AI model have to be trained?
Not always. A suitable existing model and a careful connection of your shared knowledge sources are often sufficient. In-house training is only checked if the task and data justify it.
Are answers from a knowledge assistant always correct?
No. Answers with reference to sources can also be incomplete or incorrect. Therefore, suitable tests, clear usage limits and the examination of important results are part of the concept.
Can AI independently perform actions in specialist systems?
Only within explicitly agreed powers. For critical steps, we provide human approvals, limited permissions, and traceable protocols.
Your next step
What task is worthwhile for an AI pilot?
Describe the workflow and the desired result. Together we clarify which data, quality criteria and limits are necessary for a meaningful first attempt.
AI Engineering
Your next step
Tell us what you need. We will route your enquiry to the right team and discuss the next steps with you.
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