GenAI and LLM Integration
Use AI where your users are already working.
Summarising information, drafting text or understanding complex cases: generative AI can add useful capabilities to existing applications. We turn a use case into an integrated feature with a clear task, understandable controls and measurable quality. Data access, model integration, costs and operations are planned together so that a demonstration can become a usable part of your product.
Your options
Services that move your project forward
Define the user task and expected benefit
We select a process step with identifiable users and clear success criteria. Current effort, required output quality and the possible consequences of errors are assessed together.
The first use case can be evaluated against real tasks.
Compare models and operating options
We test model capabilities, language support, response times, data processing and costs with representative examples. Hosted APIs and self-managed deployment options are assessed against your requirements.
The selection is based on the results of your use case.
Integrate AI functionality into the application
Interfaces, user interface, identities and data access are integrated into the existing architecture. Waiting states, errors and retries are given understandable behavior.
Users can use the feature within their existing workflow.
Control outputs and permissions
Expected formats, permitted data and available actions are constrained in the application. Business validation and necessary approvals are integrated at appropriate points.
The application determines which results may be further processed.
Measure quality and resource usage
An agreed test set checks task quality, error patterns, response time and resource usage. Changes to the model, prompt or context are evaluated against the same criteria.
You receive a documented basis for release decisions and further development.
Prepare for deployment and support
Versions, monitoring, fallback behavior and responsibilities are documented. Experience with Git, CI/CD, Kubernetes and observability from the SYNEDAT PLATFORM supports the integration into your IT operations.
The AI feature follows a defined path from development into everyday use.
Where to start
GenAI and LLM Integration Use cases
Three example situations show how we can help.
Case handlers need a draft based on existing information
We integrate an assistance feature whose output can be reviewed before use.
A business portal needs to make extensive content easier to understand
Summaries and structured answers are aligned with access permissions and user tasks.
A successful AI prototype is to become part of the product
We fill in the missing basics for integration, evaluation, error handling and IT operations.
From requirements to results
A clear process with agreed milestones
Agree on use case and criteria
User tasks, data, limits and verifiable results are described.
Test models and integration
A limited prototype checks key quality and architecture assumptions.
Implement and evaluate the feature
Application, model connection and controls are developed and tested on agreed cases.
Prepare release and operations
Results, responsibilities, monitoring and next improvements are handed over.
Your benefit
What you receive
- A prioritized use case with defined quality and success criteria.
- An integrated AI function in the agreed scope of application.
- Documented evaluations of quality, errors, response time and consumption.
- A deployment and operation concept with versions, responsibilities and fallback behavior.
Ways to work with us
Choose a starting point that fits your needs. We agree the scope and required effort in a tailored proposal.
Assess a GenAI use case
For an informed decision: user task, data, model options and success criteria.
Request a quote: Assess a GenAI use caseDevelop an integrated AI pilot
For practical evaluation: a function, representative tests and documented results.
Request a quote: Develop an integrated AI pilotPrepare an AI feature for production
For long-term use: integration, controlled releases and agreed support.
Request a quote: Prepare an AI feature for productionSYNEDAT 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.
Questions before you get started
What tasks are suitable for a first GenAI function?
A clearly defined step such as summarising, structuring or drafting text can be suitable. Representative examples, assessable results and a practical way to handle errors are essential. We test the benefit within the actual workflow.
Do we need to train our own model?
An existing model with suitable integration and context often provides a useful starting point. Custom adaptation or training is considered when the requirements justify it. Data, evaluation and operations matter in either case.
How is the right model selected?
We compare suitable candidates based on your tasks, languages and quality criteria. Response time, data processing, availability and running costs are all taken into account. A model name alone is not a sufficient basis for decision-making.
Can the AI work within an existing application?
Yes, where suitable interfaces and access are available. We integrate the feature into the user experience, identity controls and error handling. Your existing architecture and product team capacity determine the specific scope.
How are incorrect or inappropriate answers handled?
Tests, suitable context and application controls can reduce known sources of error. Visible limitations, clarification requests, fallback procedures and human approvals are designed for the task. We do not assume that a model will be completely error-free.
Can company data be transmitted to a model service?
Before integration, the responsible people in your organisation assess data types, contractual terms, processing arrangements and permitted uses. The technical solution implements the agreed approach. Alternatives to external processing are evaluated where necessary.
How are costs and response times controlled?
We measure representative requests and take into account context scope, output length, repetitions, and parallelism. Appropriate limits and monitoring are part of the design. Consumption costs and development services are shown separately in the quote.
What happens if the model changes later?
Model, prompt and application versions are recorded. Changes are checked with the agreed test set. Release decisions, fallback procedures and necessary adjustments follow a defined process.
Discuss your next step
Which step should be easier for your users?
Describe the task, application and available information. We define a concrete starting point and a way to verify the benefit.
GenAI and LLM Integration
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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