AI & Data
DATA ENGINEERING SERVICES
Great AI starts with great data. We engineer modern data platforms and pipelines
that make information reliable, accessible, and ready for intelligent use.
Building Reliable, Scalable Data Foundations for AI
Architectures span data lakes/warehouses, feature stores, and vector search where appropriate. We balance cost, performance, and governance for long-term sustainability.
Pipelines are instrumented for lineage, observability, and recovery. Your teams get trusted data with the freshness and fidelity AI requires.
Cloud Data Architecture & Modernization
We align architecture choices with your partner ecosystem (AWS, Azure, GCP) and analytics tools. Security and compliance are embedded rather than bolted on.
Reference implementations accelerate adoption while leaving room for future services and growth.
Data Pipeline Engineering (ETL/ELT/Streaming)
We deliver reusable components for ingestion, transformation, and validation with end-to-end monitoring. Retry and backfill strategies minimize data loss and delays.
Our approach reduces operational toil so data teams can focus on higher-value work.
Data Quality, Lineage, & Governance
Lineage and cataloging make it clear where data comes from and how it’s used. Policies and roles ensure the right people have the right access at the right time.
Controls integrate with your compliance and security practices, making governance operational rather than theoretical.
MLOps & Model Lifecycle Management
We build CI/CD for models, feature and prompt stores where appropriate, and evaluation harnesses tied to business metrics. Alerts surface anomalies before they impact users.
This creates a dependable lifecycle that aligns data science, engineering, and operations around shared outcomes.
We wanted to build a partnership and develop a good product. Ultimately, we wanted someone we could have a long-term relationship with. SOLTECH created that trust with us. This was one of the smoothest technology-related projects we’ve done recently.
Our Process
Our data engineering process lays the foundation for reliable, AI-ready information. We start by assessing your current data landscape, identifying gaps in quality, accessibility, and scalability. From there, we architect cloud-native platforms that unify structured and unstructured data, balancing cost, performance, and governance for long-term sustainability.
We design and build data pipelines that automate ingestion, transformation, and validation—ensuring accuracy, lineage, and recovery at every stage. By instrumenting systems for observability and traceability, your teams gain continuous visibility into data freshness and fidelity. This approach turns fragmented data into a trusted asset, ready to power analytics, machine learning, and intelligent automation across the enterprise.
