Machine learning and forecasting
Develop classification, anomaly detection and time-series capabilities for recurring decisions.
Data & AI Engineering
AI Engineering: Integrate machine learning into real workflows
A proof of concept is not a dependable production application. We develop, evaluate and integrate machine-learning and generative AI capabilities with defined quality, access controls and human oversight.
The challenge is not just the tool
A model can work on curated test cases and still fail when new data arrives, APIs change or a prediction is wrong. Production readiness depends on evaluation, integration, monitoring and the cost of failure.
We start with the workflow and expected outcome. Then we compare deterministic rules, conventional machine learning, existing models and generative AI. Technology is selected against business baselines, not trends.
What we engineer
Generative AI and knowledge access
Use retrieval, permissions and citations to make approved information available in applications.
Document processing
Extract and validate document data before passing it into business workflows.
Model integration
Connect inference services and models to existing APIs and applications.
Evaluation and operations
Monitor quality, drift, costs and latency with controlled fallback and human review.
Architecture and trade-offs
Training a proprietary language model is rarely the right starting point. Existing services and models can be appropriate when quality, data handling and integration satisfy the task. Retrieval-augmented generation helps with changing source documents; fine-tuning addresses model behaviour but does not provide access control on its own.
A rule-based solution can be safer and easier to maintain for deterministic tasks. For learning-based methods, we define test sets, baselines, error categories and thresholds before deployment. Sensitive or high-impact decisions require explicit escalation paths.
From the requirement to production
01 · Define the outcome
Describe the task, baseline and acceptable failure conditions.
02 · Assess the data
Review quality, representativeness, rights and access.
03 · Compare approaches
Test rules, statistical models, machine learning and generative AI.
04 · Integrate with applications
Implement APIs, monitoring, permissions and review workflows.
05 · Measure production behaviour
Track accuracy, drift, latency, costs and operational incidents.
Related capabilities and use cases
Develop image-based recognition and inspection.
Deploy models near equipment and operational systems.
Explore specific adoption challenges for mid-sized firms.
Frequently asked questions
Do we need to build our own model?
Usually not. We evaluate available models and standard solutions against the task and its data constraints first.
How do you measure generative AI quality?
Through representative test cases, human evaluation criteria, error categories and source verification.
What happens when the model is wrong?
The architecture should provide validation, escalation, human approval or a safe fallback according to the risk.
Is generative AI suitable for every automation?
No. Rules, conventional ML or simpler software can be more reliable and cost-effective.
Explore a related capability
Extract, validate and integrate document data into workflows.
Technical discovery
Which task should an AI capability perform in your systems?
We review current systems, technical boundaries and a practical first implementation step.
Aleksandar Basara
Managing Director
aleks@valanor.co
+49 (0) 711 16 44 6 83
Lindenspürstrasse 22, 70176 Stuttgart
