1. Process assessment and priorities
We examine the workflow, establish baseline measures and prioritize one use case with an accountable owner and a realistic business-value hypothesis.
Valanor AI · Data-to-Action Engineering
Your data should do more than explain what happened. It should help your teams act.
Valanor AI connects enterprise, machine and sensor data with analytics, machine learning and AI. We prioritize a specific use case, engineer the workflow and integrate evidence-backed recommendations and actionable tasks into your systems—from the data foundation and real-time processing to ongoing operations.
From data to decisions. From decisions to action.
Reports, models and AI assistants have little value if their output never reaches the people responsible for a process. Valanor AI connects reliable data to a defined workflow that checks information, detects deviations and prepares the next step.
The outcome is neither another strategy document nor an isolated prototype. Teams receive traceable recommendations or tasks inside existing systems. High-impact actions remain subject to explicit approval.
We take responsibility for the agreed engineering scope—from data integration and application logic through analytics or machine learning to testing, deployment and monitoring.
What Valanor AI delivers
2. Data foundation
We connect business systems, machines and sensors, check quality and freshness, and engineer reliable data flows. Depending on the process, we use batch pipelines or continuous event streams.
3. Analytics, machine learning and AI
We develop metrics, forecasts, classification or generative features where they serve the use case. We compare performance with the existing approach.
4. Actionable tasks and system integration
We turn results into explainable recommendations, prioritized tasks or approved actions and deliver them to applications your teams already use.
5. Operations and continuous improvement
We monitor the agreed solution, review errors, quality, adoption and costs, and implement prioritized improvements within the defined service scope.
A recommendation needs a next step.
Actionable tasks, not disconnected dashboards.
Consider an order fulfilment workflow: the system detects that a confirmed inbound delivery is due after the promised customer delivery date. Rather than displaying only a risk score, it prepares a task for purchasing and planning: check alternative stock, assess a partial shipment and record the decision.
The task includes the underlying data, when it was last updated, the rationale, an owner and a clear review path. The system does not place a new order or change the customer commitment without approval.
This creates a verifiable process: collect data, assess the situation, assign a task, make a decision and measure the outcome.
Three steps. Clear ownership.
1. Valanor AI — Start
We review one business area, relevant data sources and a specific workflow. You receive a prioritized implementation decision with data requirements, a value hypothesis, dependencies and acceptance criteria. If prerequisites are missing, we identify them before implementation.
2. Valanor AI — Build
We engineer one defined Data-to-Action workflow: data integration, application logic or models, user interaction, a target-system integration, testing and controlled rollout. We agree the scope and fixed price after Start.
3. Valanor AI — Run & Improve
We operate the agreed workflow, monitor data quality, system behavior and cost, and improve it against a prioritized backlog. Support hours, capacity and extensions are defined in the contract.
Where Valanor AI fits
1. Service operations
Classify service requests, bring together relevant information, prepare reasoned responses and assign tasks to responsible teams.
2. Planning and scheduling
Analyze demand and deviations, detect inventory or delivery risks and prepare specific checks or planning tasks.
3. Document-driven workflows
Extract information from documents, validate it against business rules and pass verified records or follow-up questions to core systems.
4. Operational reporting and analytics
Bring data from different systems into consistent metrics, make deviations visible and turn findings into owned actions.
Act on machine and sensor data while it still matters.
Real-time data processing and Edge AI
Temperature, vibration, energy consumption and machine states change continuously. We capture relevant signals, calculate metrics and detect deviations as the process runs—without waiting for the next data export.
When vibration readings become unusual, we compare them with the machine's historical behavior and prepare an inspection task for maintenance. The task records the data source, evidence, priority and owner. Interventions still require defined approvals.
Depending on latency and network conditions, processing runs on edge devices or a central platform. Explore local models and industrial use cases under Edge AI.
Technology follows the process, not the other way around.
Not every workflow needs a language model or AI agent. Reliable pipelines, rules and established forecasting methods are often more economical. We evaluate data availability, business value, the cost of errors, integration effort and operations.
We develop new components and use existing software where it meets the requirements. Access control, data provenance, human oversight, testability and monitoring are part of the architecture from the outset.
Read more about our Data & AI Engineering services. If architecture or technical feasibility is still unclear, we start with Consulting & Diagnostics.
A reliable data foundation for analytics and AI.
The architecture behind Valanor AI
Our data foundation uses dlt, Floe, Apache Iceberg, dbt and Trino. dlt ingests source data into Bronze. Floe checks data contracts and quality for Silver. dbt and Trino transform verified data into business-oriented Gold models for analytics, machine learning and applications.
Dagster orchestrates data products. Apache Iceberg and object storage separate the data from processing engines. An Iceberg catalog, such as Apache Polaris or AWS Glue depending on the environment, manages tables. OpenMetadata can provide governance and lineage; Superset offers an analytics interface.
We extend that foundation with machine learning, generative AI and operational workflows. Keeping data processing, model operations and business actions separate makes testing, permissions, failure handling and change management more manageable.
Streaming and the lakehouse work together.
We add a streaming path to batch data processing for continuous sensor feeds. Event volume, latency requirements, availability and existing OT/IT systems determine the architecture.
Connect sensors and machines
We connect equipment, PLCs and sensors through gateways and protocols such as OPC UA, MQTT and, where needed, Modbus. EMQX or Mosquitto handles MQTT messaging.
Distribute events
Apache Kafka or Redpanda buffers and distributes events where multiple consumers, larger volumes or replay are required. Smaller deployments can often use a simpler MQTT-based pipeline.
Process streams continuously
Apache Flink evaluates time windows, correlates events and handles late-arriving data. For straightforward rules and aggregations, we avoid unnecessary streaming infrastructure.
Live analytics and historical data
ClickHouse supports fast analysis of incoming events; TimescaleDB is an option for PostgreSQL-oriented time-series use cases. We transfer historical data into Apache Iceberg through controlled pipelines.
Local inference and tasks
Depending on hardware and models, we use ONNX Runtime or NVIDIA Triton for edge inference. We send findings through defined APIs to MES, ERP or maintenance systems.
From validated data to production models.
MLflow: experiments and model versions
For traditional ML use cases, MLflow tracks training runs, parameters, metrics and artifacts. The Model Registry records approved versions and supports reproducible deployments.
Dagster: training and recurring jobs
Training and batch-scoring jobs use validated Silver or Gold data. Dagster manages scheduling and dependencies. Models read from the lakehouse without taking ownership of Bronze, Silver or Gold write paths.
BentoML or KServe: inference
BentoML is an option for a lightweight API endpoint. KServe fits cases that justify Kubernetes-native scaling and more complex serving. Batch forecasts may not need a permanent serving endpoint.
Evidently and metrics: model quality
We measure input quality, data distributions and prediction performance against agreed reference values. Drift signals trigger review rather than automatic retraining or replacement.
Generative AI with controlled data access.
vLLM: run language models locally
We use vLLM to serve suitable language models on customer-controlled infrastructure. A governed model gateway manages access, limits and usage. External model APIs are not required; isolated deployments keep models and runtime dependencies local.
Qdrant or pgvector: search internal knowledge
For document and knowledge applications, we choose Qdrant or pgvector according to the workload. Haystack or LlamaIndex may handle retrieval. We design for freshness, permissions, source attribution and locally generated embeddings.
MLflow: tracing and evaluation
We test generative features against representative tasks and capture prompt versions, traces and quality assessments. MLflow supports these workflows; specialized RAG evaluation tools are added where needed.
Actions with approvals
Models only propose operations the application explicitly allows. A separate API and workflow layer checks user permissions, business rules and human approvals before changing records in ERP, CRM or ticketing systems.
Production operations require more than working models.
We monitor pipeline runs, model endpoints, failure rates, processing time and infrastructure with metrics and logs, for example through Prometheus and Grafana. MLflow tracks experiments and model versions; it does not replace infrastructure monitoring.
For sensitive or business-critical processes, we design secrets management, permissions, network boundaries, backup and restore, tested rollback procedures, cost controls and environment isolation. The workload determines which services must run continuously.
The lakehouse data path is primarily batch-oriented. Real-time and streaming needs require additional components. We validate production deployments with tests, locally available dependencies for air-gapped environments and an explicit operations model.
Open components. A clearly defined architecture.
We use dlt, Floe, Apache Iceberg, Trino, dbt and Dagster for ingestion, lakehouse storage and transformation. MLflow handles model versioning and evaluation; vLLM, Qdrant or pgvector and appropriate serving tools support AI applications. We select components based on data, security, load and the business process—not a fixed software checklist.
Frequently asked questions about Valanor AI
Is Valanor AI a software platform?
No. Valanor AI combines an open data architecture with analytics, machine learning and AI applications. We engineer and integrate a workflow within your existing systems. You do not have to introduce another off-the-shelf user interface.
What data do we need to get started?
We need a defined process, a business owner and access to representative data or appropriate samples. We assess scope and data quality during Start.
Do we need an existing data platform or AI models?
No. We assess your current architecture and develop missing data pipelines or functionality within the agreed scope. Replacing the entire IT landscape is not automatically part of the engagement.
How do you limit incorrect recommendations and uncontrolled actions?
We define traceable sources, evaluation criteria, failure boundaries and human approvals. Missing or low-quality data must result in controlled behavior, including escalation to a person when needed.
How do you measure success?
Before implementation, we agree baseline values and business metrics such as processing time, error rates, forecast quality or manual review effort. In operation, we also track usage, data quality and cost.
How much does Valanor AI cost?
Start is a clearly scoped engagement. We price implementation and ongoing operations based on the workflow, integrations and operational requirements, with explicit service boundaries.
Can Valanor AI process sensor data in real time?
Yes. We connect machine and sensor data through protocols such as OPC UA or MQTT, process events continuously and make results available for live analytics, anomaly detection and operational tasks. We validate latency targets against the actual process. Safety-critical control and shutdown functions remain in suitable deterministic systems such as PLCs.
Valanor AI
Which process should your data improve next?
Tell us about the workflow, systems and decisions that currently consume too much time or involve too much uncertainty. We will assess with you where the first Data-to-Action workflow makes sense.
Aleksandar Basara
Managing Director
aleks@valanor.co
+49 (0) 711 16 44 6 83
Lindenspürstrasse 22, 70176 Stuttgart
