Data & AI

Turn Data Into Intelligence. Intelligence Into Impact.

Transform raw information into a competitive moat. As a specialized software development company in Austin, we build the data pipelines and AI models that power predictive diagnostics in Healthcare and algorithmic precision in Finance.

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Trusted Frameworks for Regulated Data

Turn Data into a Strategic Advantage.

We build the "blueprints" for your data ecosystem, ensuring it is compliant with financial audits and healthcare regulations while remaining accessible for business intelligence.

  • Scalable data architecture design (data lakes, warehouses, pipelines)
  • Master data management and metadata frameworks
  • Data quality, lineage, and lifecycle management
  • Compliance with GDPR, HIPAA, and other regulatory standards
  • Governance models for access control, privacy, and auditability
Data Architecture & Governance
Skilled software engineers and project managers collaborating in a modern office — representing Loku Digital’s staff augmentation services.

Data Architecture & Governance

Securing the integrity of your most valuable asset.

Robust Engineering for Real-Time Insights

Build Reliable Pipelines. Unlock Actionable Insights.

Our engineers build the high-performance pipelines that move data from source to insight, ensuring your dashboards and AI models always have the freshest information.

  • ETL/ELT pipeline design and implementation
  • Real-time streaming and batch data processing
  • Data integration across multiple sources and platforms
  • Cloud data platforms (AWS, Azure, GCP) and data warehousing
  • Data quality, validation, and performance optimization
Data Engineering
Engineering team collaborating on custom software, cloud integrations, and compliant enterprise systems.

Data Engineering

Moving data at the speed of your business.

Generative AI & AI Engineering

Custom AI solutions for complex decision-making.

We help you identify where Generative AI can actually drive value—whether it's automating medical charting or identifying fraudulent financial patterns—and build the custom models to execute it.

  • Generative AI solutions for content, code, and design automation
  • Predictive modeling and recommendation engines
  • Natural language processing (NLP) and computer vision applications
  • AI model deployment, monitoring, and lifecycle management
  • Integration of AI capabilities into existing products and workflows
Generative AI & AI Engineering

Generative AI & AI Engineering

Moving beyond the hype to real-world ROI.

Our Journey

8+
Years in Business
50+
Clients served
4
Worldwide Offices
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The Intelligence Lifecycle: From Raw Data to Insights

Great AI is built on great data. Our process ensures your data is clean, compliant, and structured to support high-performance machine learning models.

Data Source Discovery
Identifying siloed data across your organization to create a unified single source of truth.
Pipeline Architecture
Designing scalable infrastructure to handle high-velocity data ingestion without latency.
Governance Framework
Embedding HIPAA and GDPR-grade security into the data layer to ensure privacy by design.
ETL/ELT Implementation
Automating the extraction, transformation, and loading of data into high-performance warehouses.
Model Development
Building custom ML models tailored to your specific financial risk or clinical outcome goals.
MLOps & Deployment
Operationalizing AI with robust monitoring to ensure model accuracy and prevent drift over time.
Continuous Learning
Refining algorithms based on real-world performance to drive increasing business value.

You are in good hands, we have worked with

Data & AI Strategy FAQ

Navigating the frontier of custom software development in Austin.

How do you handle sensitive PII/PHI in AI models?

We use advanced de-identification, differential privacy, and secure enclaves to ensure models learn from patterns without ever exposing sensitive individual data.

Can your software developers help us with Generative AI?

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What is the difference between Data Engineering and Data Science?

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How do you prevent bias in AI models?

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Is my data used to train public AI models?

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