AI Data Engineering Services

Purpose-built data infrastructure that gets your AI models into production — faster, cleaner, and at enterprise scale.

Why Enterprise Data Programs Stall Before AI Delivers Value

Most data-mature enterprises already have the raw ingredients for AI — but fragmented pipelines, inconsistent data quality, and no MLOps layer keep models stuck in notebooks and dashboards instead of driving decisions.

A data engineer reviewing a broken pipeline on multiple monitors in a modern office

Where Enterprise AI Programs Break Down

Siloed Data Sources

Production data lives across warehouses, lakes, SaaS tools, and legacy systems with no unified access layer — making model training inconsistent and unreliable.

No Repeatable Pipeline Architecture

One-off ETL scripts built by analysts cannot support the reliability, versioning, or throughput that ML workloads demand at scale.

Model Deployment Gaps

Teams build strong models in development environments but lack the infrastructure to serve them in real-time production systems.

Missing MLOps Discipline

Without automated monitoring, retraining triggers, and drift detection, model performance degrades silently — eroding trust and business value over time.

Governance and Compliance Risk

AI workloads that touch sensitive data need lineage tracking, access controls, and audit trails — gaps that create regulatory exposure at the point of scale.

A clean architectural diagram of an enterprise AI data pipeline showing ingestion, transformation, and model serving layers

Custom AI Data Pipelines Built for Production

StratApps designs and delivers end-to-end AI data engineering engagements — from raw data ingestion through feature engineering, model serving, and ongoing operational health.

Core Capabilities

Custom Data Pipelines

We architect batch and streaming pipelines using modern tooling (dbt, Airflow, Spark, Kafka) that deliver clean, versioned, schema-enforced data to every model and dashboard that depends on it.

Feature Engineering & Data Stores

Consistent feature definitions across training and serving environments — built on a feature store layer that eliminates training-serving skew and accelerates model iteration.

Model Deployment & Serving

We containerize, version, and deploy models to REST APIs or streaming endpoints with latency SLAs, A/B routing, and rollback capability baked in from day one.

MLOps Infrastructure

End-to-end MLOps pipelines covering experiment tracking, automated retraining, drift monitoring, and CI/CD for models — so your AI program operates like production software, not a research project.

Data Quality & Observability

Automated data quality checks, freshness monitoring, and lineage tracking that surface issues before they silently corrupt model inputs or downstream reports.

Cloud-Native Architecture

Implementations built natively for AWS, Azure, or GCP — leveraging managed services to minimize operational overhead while meeting your security and compliance requirements.

How an Engagement Works

1. Data & Architecture Audit

We map your existing data sources, pipeline gaps, and model maturity to produce a clear picture of what needs to be built, replaced, or hardened before AI can scale.

2. Architecture Design & Scoping

Our engineers produce a reference architecture and a phased delivery plan — prioritizing the pipelines and infrastructure that unlock the highest-value use cases first.

3. Pipeline & Infrastructure Build

Hands-on engineering delivery: pipelines, feature stores, serving infrastructure, and MLOps tooling are built, tested, and documented to your team’s standards.

4. Model Deployment & Go-Live

Models are promoted from staging to production with monitoring dashboards, alerting, and runbooks in place — so operations teams can own ongoing health from day one.

5. Ongoing Support & Iteration

Retainer-based or project-extension support for retraining cycles, new data source integrations, and architectural evolution as your AI program grows.

What You Get With StratApps AI Data Engineering

Production-ready pipelines — not PoC code handed back to your team to maintain

Full documentation and knowledge transfer for every component delivered

Tooling chosen to fit your existing stack, not our preferred vendor

Dedicated senior engineers, not junior staff augmentation

Fixed-scope phases with clear deliverables and success criteria

Integrations with your Enterprise BI layer for unified reporting and AI insights

Enterprise AI Engineering Consulting — From Strategy to Scale

Whether you’re formalizing your first production ML pipeline or modernizing a fragmented AI infrastructure across business units, StratApps provides the senior engineering depth and architectural leadership that internal teams rarely have on standby.

Senior consultants collaborating on an enterprise AI engineering strategy at a whiteboard

StratApps replaced three fragile ETL scripts with a single, monitored pipeline that feeds our recommendation model. Time to retrain dropped from two weeks to overnight.

VP of Data Engineering

Global SaaS Platform

We had models. We just couldn't get them into production reliably. StratApps built the MLOps layer we were missing and we shipped four models in the first quarter after engagement.

Head of AI & Analytics

Enterprise Financial Services Firm

The audit alone was worth the engagement. We discovered two critical data quality issues that would have poisoned our churn model before it ever hit production.

Chief Data Officer

Mid-Market Logistics Company

Frequently Asked Questions

How much does AI data engineering cost?

Engagements are scoped per project phase. A focused pipeline or MLOps build typically runs from $40,000 to $150,000 depending on complexity, existing infrastructure, and team involvement. Multi-phase or retainer programs are priced separately. We always start with a scoped audit so you know exactly what you're buying before committing to a full build.

How long does a data engineering engagement take?

A scoped audit and architecture design takes two to four weeks. A single pipeline or deployment build typically runs six to twelve weeks. Full MLOps infrastructure engagements for complex environments run three to six months. We publish a delivery schedule at the start of every phase so there are no surprises.

What industries do you serve with AI data engineering?

We work with data-mature enterprises across financial services, SaaS, logistics, healthcare technology, and retail. The common thread is organizations that already have data assets and want to put them to work in production AI systems — not companies starting from scratch with data collection.

Do you work with our existing cloud and data stack?

Yes. We build to your stack, not ours. We have deep experience across AWS, Azure, and GCP managed services, and with tooling like Snowflake, Databricks, dbt, Airflow, MLflow, Kubeflow, and Spark. We will recommend alternatives where something in your stack is creating a structural bottleneck, but we don't require a rip-and-replace to start delivering.

What is the difference between AI data engineering and general data engineering?

General data engineering focuses on moving and transforming data for reporting and analytics. AI data engineering extends that to include feature stores, training pipelines, model serving infrastructure, and MLOps — the additional layers required to take a model from a notebook to a reliable, monitored production service.

Can you help us implement Managed AI Workflows alongside this engagement?

Yes. Our AI Implementation Services practice covers the full lifecycle from data infrastructure through workflow automation and operational AI. Many clients start with a data engineering engagement and expand into managed AI workflows once the pipeline foundation is in place. See our AI Implementation Services page for more detail.

Ready to Put Your Data to Work in Production AI?

Get a scoped data and architecture audit from StratApps senior engineers — and leave with a clear roadmap, not a sales pitch.

Scope Your AI Data Engineering Engagement