AI Data Foundation
Build a data foundation ready for AI.
- Connect data sources and business systems
- Standardize data, access and quality
- Design pipelines and data stores

Connect data, models and engineering to build AI applications around your business.
Talk to our expertsAI creates value when it solves real problems. AQPTech combines data engineering, software development and systems integration to bring AI into business workflows.
Start with a clear use case, validate through a PoC and scale based on evidence. Each step considers data quality, security and long-term operations.
Explore our offerings ↗
DATA → INTELLIGENCE → ACTIONFrom field data to knowledge assistants and workflow automation — designed around your systems and goals.
Discuss your use case ↗
Choose individual capabilities or connect them into one delivery roadmap.
Build a data foundation ready for AI.
Turn business challenges into applications you can validate.
Bring models into the right operating environment.
Compare processing time and workload before and after the trial.
Measure accuracy and task quality against an agreed dataset.
Track access, outputs and human review steps.
Evaluate cost per task and resources needed to scale.
Select a use case, assess data and define success criteria.
Design a PoC, test models and measure results.
Connect existing software, devices and workflows.
Hand over, monitor quality and improve with feedback.
Flexible architecture connecting your data sources, model platforms and infrastructure. Technology choices follow integration needs, cost and data control requirements.
Design solutions around users and operating systems.
Agree on access, data handling and oversight before deployment.
Connect applications, devices and edge computing in one architecture.
Use PoC results and acceptance criteria to decide the next step.
AI Factory is AQPTech’s approach to connecting data, AI engineering and operations: from selecting use cases to trials, integration and continuous improvement.
A specific use case, a business owner and information about available data and systems. We assess readiness together before agreeing on scope.
On-premise, cloud and hybrid deployments can be considered. Architecture depends on data requirements, compute resources, security and budget.
We agree on test data, a baseline and acceptance criteria covering output quality, latency, task success, cost and human oversight.
Access, storage location, retention and model service usage are agreed for each project before connecting data.
Share your requirements using the form below. Our engineering team will help define the use case, PoC scope and delivery plan.
Share your goals and existing systems. Let’s define the next step together.
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