The use case is described in business terms, the data situation is sufficient, acceptance criteria can be formulated. The goal is a tested and measurable pilot application as the basis for the investment decision.
Solution area 03 · Delivery and AI production
Productive AI agents on enterprise data
Design, piloting and productive rollout of AI applications built on your enterprise data. Architecture with defined guardrails, documented eval logic and data residency within the European Union. Pilot operation in six to ten weeks, followed by structured transition into productive operation.
Typical triggers
Three constellations
Three constellations
where we start
The implementation of productive AI applications happens in clearly scoped phases. What matters is the maturity of the use case, the reliability of the data foundation and the clarity of the acceptance criteria.
A working prototype exists. Open topics include hallucination protection, data access model, versioning, monitoring, eval suite and a formal acceptance test against defined quality thresholds.
Multiple use cases require a maintainable architecture with shared components for eval, monitoring and deployment, as well as a defined onboarding pattern for new use cases.
Approach
Five phases,
Five phases,
from specification to production operation
Pilot and production operation are methodically clearly separated. The pilot phase serves functional and technical validation. The transition to production operation happens only after the acceptance criteria are met.
1
Use-case specification
Description of actors, triggers, inputs and outputs, and the relevant success metrics. Formulation of acceptance criteria in a form that allows an objective assessment of the pilot outcome.
2
Assessment of data access
Structured survey of the required data sources with respect to timeliness, completeness, quality and access rights. Where necessary, the application is preceded by a short data-foundation phase.
3
Pilot architecture
Model selection based on functional requirements and the privacy profile. Definition of the retrieval and tool-use pattern, setup of the vector database, implementation of guardrails for input validation, output checking and hallucination protection.
4
Validation via the eval suite
Building an eval set from real requests. Automated assessment of correctness, source fidelity, latency and operating cost. Involvement of qualified reviewers for edge cases. Completion of the pilot phase once the defined acceptance criteria are met.
5
Productive rollout and handover
Setup of monitoring for latency, cost and eval trends. Versioning of prompts and models, a defined update process, complete documentation. Structured handover to the internal team and functional support during the stabilization phase over 30 to 90 days.
Delivery format
Pilot in 6 to 10 weeks. Productionization in 4 to 8 weeks.
The pilot phase serves to validate against defined acceptance criteria. Once validation is passed, productionization follows with monitoring, versioning and handover.
Strategy first
Impact
The impact of productive
The impact of productive
AI applications
Success is measured along business metrics from the application domain of the respective use case, as well as along technical quality metrics from the eval suite.
Measurable business value
Impact is measured in processing time, hit rate, output per employee, or other business-defined metrics. Acceptance criteria are fixed in writing before the pilot starts.
Acceptance in the business unit
Traceability of answers via visible sources, a defined approach to uncertain data situations, and a documented fallback path to the manual process ensure continued adoption in day-to-day operation.
A viable platform
The eval suite, monitoring, prompt and model versioning, as well as reusable architecture components form the basis for orderly expansion with further use cases.
Scope of services
Scope of services and
Scope of services and
boundaries
We are responsible for architecture, implementation and productive rollout of the AI application. Topics outside this scope are addressed transparently through the partner network.
In scope
- Use-case specification with measurable acceptance criteria
- Pilot architecture including retrieval and agent patterns
- Connection to existing data sources (data warehouse, APIs, file storage)
- Guardrails for input validation, output checking and hallucination protection
- Building and operating an eval suite for ongoing quality assurance
- Production environment with monitoring, versioning and deployment process
- Structured handover to the internal team with functional support
Out of scope
- Pre-training and fine-tuning of proprietary foundation models
- Procurement and operation of GPU infrastructure
- Permanent 24/7 operation of the AI platform after handover
- Applications without a clearly scoped business benefit
- Pure strategy work with no delivery component (see solution area 01)
For foundation-model research and GPU infrastructure we work with specialized technology partners.
Technology selection
Sovereignty as a
Sovereignty as a
selection criterion
Model and infrastructure selection is guided by functional requirements, the privacy profile and total cost of ownership. European models and providers as well as open components take priority. International platforms are used exclusively in EU regions with documented data processing agreements.
Mistral (EU)
Aleph Alpha (EU)
Llama (self-hosted)
OVHcloud (EU)
Scaleway (EU)
STACKIT (EU)
Qdrant
Weaviate
pgvector
LangChain
LangGraph
LlamaIndex
Pydantic AI
Langfuse
Azure OpenAI (EU region)
AWS Bedrock (EU region)
Application patterns
Three application patterns
Three application patterns
for AI agents
The following patterns form the focus of our engagements. They cover both knowledge-based applications and data-centric analysis and process applications.
Knowledge-based assistance
Structured access to internal documentation, technical specifications, contracts and knowledge bases. Requests in natural language are answered with referenced sources from the connected systems.
Conversational data analysis
Analysis of structured data via natural-language queries. Translation into SQL, plausibility checking and responses with tabular and visual presentation based on the consolidated data foundation.
Process automation
Automation of defined operational tasks such as classification of incoming requests, pre-qualification of tickets or extraction of structured content from documents, with defined involvement of qualified reviewers and a complete audit log.
Frequently asked questions
Questions from the
Questions from the
preparation phase
Specific questions about the AI agents and delivery solution area. General questions about working with LYN Intelligence can be found on the homepage.
How are hallucinations handled?
This is handled on three levels. First, architecturally through retrieval-augmented generation and through explicit behavior when the data situation is uncertain. Second, through traceable sources in every answer. Third, through an eval suite that automatically checks factual accuracy and confabulation tendency before every model or prompt change.
Who is responsible for operations after go-live?
Responsibility is typically handed over to the internal team. Architecture, eval setup, monitoring and the deployment process are fully documented and taught as part of the handover. During the stabilization phase of 30 to 90 days, functional support is provided. Where internal capacity is lacking, we arrange an implementation partner.
How are data protection and data residency handled?
Personal and business-critical data is processed on the basis of a data processing agreement in EU regions. For particularly sensitive data, we use European models and providers or a self-hosted architecture on European infrastructure. The technical and organizational measures taken are documented.
How is the model selection made?
Selection is based on the functional requirements, the privacy profile, latency and cost requirements, and the preferred data residency. Comparison runs against the eval set provide the basis for the decision. A later model switch remains possible thanks to the abstraction layer in the architecture.
How is quality ensured on an ongoing basis?
The eval suite runs continuous automated tests against a growing eval set. Monitoring captures latency, cost, error rates and qualitative indicators during live operation. Anomalies trigger a defined review and escalation process.
What are realistic prerequisites for a pilot?
What's required is a clearly formulated use case with measurable acceptance criteria, a responsible business unit, and a sufficient data situation. Where the data foundation is insufficient, an upstream data-foundation phase is recommended.
Solution areas at a glance
Strategy, data foundation and AI
Strategy, data foundation and AI
as connected phases
The three solution areas form phases that build on one another. AI applications unfold their value on a reliable data foundation and a clearly formulated strategy.
AI and data strategy
Prioritization of relevant use cases, assessment of the data situation, building a reliable roadmap and a viable business case.
View solution →Data foundation and business intelligence
Consolidation of data sources, building the single point of truth and modernizing reporting as the basis for AI.
View solution →AI agents and delivery
Business-oriented AI applications on enterprise data, from piloting through rollout to productive operation.
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Discuss
your use case
In a 60-minute exploratory conversation we provide an initial assessment of the use case regarding feasibility, data situation and economic viability of a pilot project.