From model to agent: enterprise AI infrastructure

We build company-specific AI agents on a rule/skill/MCP/hook architecture, connect a KVKK-compliant knowledge base (RAG) that answers questions over your procedures and documents, and bridge your ERP/PLM systems together with agents.

Enterprise AI Agent Infrastructure: company-specific agents on a rule/skill/MCP/hook architecture
Enterprise Knowledge Base (RAG): Q&A over procedures and quality documents
LLM–ERP/PLM integration: agent bridges to Enovia, SECTION, SAP
KVKK-compliant deployment with an on-prem option
We train your team to the same standard — linked to the Enterprise AI Competency program

Enterprise AI Agent

Claude-Code-based agent infrastructure that runs on rules specific to your company.

RAG Knowledge Base

Natural-language Q&A over your procedures and technical documents.

ERP/PLM Agent Bridge

Agents manage data flow between Enovia, SECTION, and SAP.

From camera to model, from model to decision

We deploy parametric or learned vision systems for quality, safety, ergonomics, and counting, and solve problems like AGV routing, scheduling, and load planning with reinforcement learning and optimization algorithms.

Computer vision: camera systems for quality, safety, ergonomics, and counting
Reinforcement learning (RL) for AGV/AMR and scheduling optimization
Combinatorial optimization problems such as 3D load and route planning
Monitoring and periodic retraining of deployed models (MLOps)
Hardware selection, installation, and MES integration under one roof

Autonomous Visual QC

A learned model that catches defects via camera — live reference case.

AGV Dispatch Optimization (PPO)

Manages the fleet with reinforcement learning — live reference case.

MLOps & Model Maintenance

Production model performance is monitored and retrained when needed.

Find out where to start — in two weeks

We assess your data maturity and processes in a short discovery engagement and hand you a concrete roadmap with a quick-win list and ROI estimate.

A two-week discovery engagement: data maturity and process mapping
A quick-win list with prioritization
An investment-ready report with an ROI estimate
A concrete mapping of which AI approach fits which solution area
Next step: pilot project scope and timeline

Data Maturity Analysis

We assess whether your existing data sources are ready for AI.

Quick-Win List

We rank the AI opportunities with the lowest effort and highest impact.

Pilot Roadmap

A concrete next step with scope, timeline, and ROI estimate.

Consolidate, cleanse, and ground your data on a reliable foundation

Quality analytics starts with quality data. AdAstra's data warehouse and ETL solutions consolidate data from ERP, IoT platform, CRM, file sources, and external services into a central data warehouse through automatic Extract, Transform, and Load processes. Ensure reliable data flow with data quality controls and automatic scheduling.

Automatic data extraction from multiple sources (database, API, file, IoT)
Data transformation: cleansing, merging, aggregation, and format standardization
Periodic ETL jobs with scheduler: hourly, daily, or event-triggered
Data quality controls: detection of missing data, inconsistencies, and outliers
Data warehouse schema design: star schema, snowflake schema, and dimensional modeling

Data Warehouse

Central data storage, schema design, and high-performance querying.

ETL Platform

Visual ETL designer, data source connectors, and scheduling.

Data Quality

Automatic quality controls, error reporting, and data cleansing.

Visualize your data with interactive dashboards

Business intelligence tools transform raw data into understandable visualizations and interactive panels, accelerating decision-making. AdAstra's BI solutions serve everyone from business users to the board of directors with a drag-and-drop dashboard designer, extensive widget library, drill-down navigation, and self-service analytics.

Drag-and-drop dashboard designer: build custom panels without writing code
Widget library: bar chart, line chart, pie chart, gauge, heatmap, table, and KPI cards
Drill-down: navigation from summary to detail (country > city > factory > line > machine)
Filtering and segmentation: filter data by date, department, product, and other dimensions
Scheduled report distribution: automatically share dashboards as PDF/Excel via email

BI Dashboard

Interactive dashboard designer, widget library, and sharing.

Self-Service Analytics

No-code data discovery, querying, and visualization for business users.

Report Distribution

Automatic report scheduling, PDF/Excel output, and email distribution.

Model and forecast the future from historical data

While traditional reporting answers 'what happened?', advanced analytics seeks answers to 'what will happen?' and 'what should we do?'. AdAstra's advanced analytics solutions enable you to model future demand, costs, and performance with statistical modeling, time-series forecasting, and regression analyses.

Time-series forecasting: projections for metrics like demand, production volume, and energy consumption
Regression analysis: modeling the effect of independent variables on outcomes
Automatic decomposition of seasonality, trend, and cyclicality components
What-if scenarios: comparing alternative scenarios with different assumptions
Monitoring model performance with forecast accuracy metrics (MAPE, RMSE)

Forecasting Engine

Time-series models, regression, and seasonality analysis.

Scenario Analysis

What-if scenarios, parameter changes, and result comparisons.

Model Management

Model versioning, performance monitoring, and automatic retraining.

Achieve operational excellence with industrial AI

Artificial intelligence and machine learning uncover opportunities and risks that the human eye cannot see by discovering complex patterns in industrial data. AdAstra's AI solutions integrate machine learning models into operational processes for industrial use cases such as predictive maintenance, quality prediction, process optimization, and demand planning.

Predictive maintenance: prevent unplanned downtime by predicting machine failure probability
Quality prediction: predict product quality from process parameters during production
Process optimization: determine optimal production parameters with AI
Demand planning: more accurately forecast complex demand patterns with ML models
Natural language processing: automatic sentiment analysis and classification from customer feedback

Predictive Maintenance

ML-based failure prediction, remaining useful life calculation, and maintenance recommendations.

Quality Prediction

Product quality prediction from process data and parameter optimization.

ML Platform

Model development, training, deployment, and monitoring infrastructure.

Automatically detect unusual situations and intervene early

Anomaly detection enables early detection of potential problems by automatically identifying deviations from normal operational behavior. AdAstra's anomaly detection solutions automatically identify anomalies in production, quality, energy, and financial data using statistical methods and machine learning algorithms, and send notifications to relevant personnel.

Detect abnormal cycle times, downtime patterns, and efficiency drops in production data
Detect deviations in quality metrics early to prevent serial defect risk
Detect abnormal increases in energy consumption to catch leakage or fault signals
Automatically flag unusual spending and transaction patterns in financial data
Deliver anomaly alarms to relevant personnel via email, SMS, and dashboard

Anomaly Detection

Statistical and ML-based anomaly detection, classification, and reporting.

Early Warning

Automatic notifications, escalation rules, and response tracking.

Pattern Analysis

Detection of recurring anomaly patterns and root cause correlation.

Structure your data-driven decision-making processes

Decision support systems provide managers with a structured decision-making framework — based on the right data at the right time. AdAstra's decision support solutions improve the quality of your operational and strategic decisions with KPI management, target tracking, comparative analyses, and strategy dashboards.

KPI definition and target setting: at department, team, and individual levels
Target vs. actual tracking: real-time status with green-yellow-red indicators
Comparative analyses: inter-period, inter-facility, and benchmark comparisons
Board dashboard: high-level summary of financial, operational, and strategic KPIs
Automatic reporting: automatic generation and distribution of weekly/monthly management reports

KPI Management

KPI definition, target setting, monitoring, and performance evaluation.

Management Dashboard

Executive management panel, strategic KPIs, and trend indicators.

Benchmark

Performance comparison across facilities, departments, and periods.

Artificial Intelligence, Data & Analytics

Transform your data into insights, insights into action

Central Data Platform & ETL

Business Intelligence & Interactive Dashboards

Artificial Intelligence & Forecasting

Core Solutions