Build AI and ML Infrastructure
That Performs in Production
| → Scala and JVM-native ML infrastructure | → Feature pipelines and model serving |
| → Engineers with production AI experience | → Integrates with your existing Scala stack |
AI and ML systems fail in production for engineering reasons, not model reasons. We put senior Scala engineers on your ML infrastructure so your pipelines are reliable, your models ship on time, and your architecture is built to scale.
WHY SCALA FOR AI AND ML
The JVM is built for ML infrastructure.
Most ML frameworks are built around Python for experimentation. Production ML systems need something different: reliability, type safety, and performance at scale. Scala on the JVM delivers all three.
01
Type safety catches errors before they cost you
Schema mismatches and transformation errors that corrupt data silently in Python get caught at compile time in Scala. In ML pipelines, that distinction is the difference between a production incident and a non-event.
02
Spark is written in Scala
Apache Spark is the most widely used distributed computing framework for ML workloads. It is written in Scala. Building ML pipelines in Scala gives you full API access and native performance without the overhead of Python wrappers.
03
Concurrency built for high-throughput inference
Cats Effect and ZIO give you structured concurrency that handles thousands of simultaneous inference requests without the thread management overhead that makes JVM services hard to scale. Model serving becomes a tractable engineering problem.
04
Functional programming fits data transformation
Immutability and pure functions make data transformations easier to reason about, test, and debug, especially in distributed ML pipelines where side effects and state are the root cause of most production failures.
05
JVM interoperability with your existing stack
Scala runs on the JVM and integrates directly with existing Java services, enterprise data infrastructure, and the broader Hadoop and cloud-native ecosystem. No rewrites, no bridges, no translation layers between your ML system and your production stack.
06
Proven at scale in production
LinkedIn, Netflix, and Airbnb built their data and ML infrastructure on Scala and Spark. The language and the ecosystem have been stress-tested at a scale most companies will never reach. That production history matters when you are building systems that cannot fail quietly.
WHAT WE BUILD
Scala AI and ML capabilities.
Scala Teams builds the engineering infrastructure that makes AI and ML systems work in production, from raw data to model serving, on Scala and the JVM.
ML pipeline development
End-to-end ML pipelines on Scala and Spark.
We build model serving systems on the JVM using http4s, Tapir, and Cats Effect, designed for high-throughput, low-latency inference with the concurrency guarantees that production SLAs require. When inference volume grows, the JVM handles it without the scaling constraints that Python serving frameworks run into.
Model serving infrastructure
Low-latency inference at production scale.
We build model serving systems on the JVM using http4s, Tapir, and Cats Effect, designed for high-throughput, low-latency inference with the concurrency guarantees that production SLAs require. When inference volume grows, the JVM handles it without the scaling constraints that Python serving frameworks run into.
Feature engineering and feature stores
Reliable feature pipelines your models can depend on.
We build batch and streaming feature pipelines that compute, store, and serve features consistently across training and inference. Built on Spark and FS2 with strong typing to prevent the feature drift and training-serving skew that silently degrades model performance.
AI backend integration
ML capabilities wired into your Scala systems.
We integrate AI and ML capabilities into existing Scala backends and JVM services, including model inference, recommendation engines, and NLP pipelines. Your product keeps running on the stack it was built on. We add the intelligence layer without touching the architecture underneath.
Data platform and lakehouse architecture
The data foundation that serious ML requires.
We design and build data lake and lakehouse architectures using Delta Lake and Apache Iceberg on Scala and Spark. Structured for query performance, data quality, and the governance requirements that regulated industries and large-scale ML systems demand.
NLP and recommendation systems
Intelligent systems built on the JVM.
We build natural language processing pipelines, semantic search systems, and recommendation engines on Scala and the JVM. Whether you are processing documents, ranking content, or personalizing at scale, we build the infrastructure that makes it work reliably in production.
How we work
Three ways to engage with our Scala AI/ML team.
Whether you need one Scala ML engineer embedded in your team or a full team to own your AI infrastructure, we match the model to what you actually need.
01
Scala dedicated ML team
A team of senior Scala engineers working on your AI and ML infrastructure. We staff, manage, and deliver, while you stay focused on the product and the models.
02
Scala ML staff augmentation
One or more senior Scala ML engineers embedded directly in your team. Production-ready from day one. They know the ecosystem and contribute without a ramp-up period.
03
Scala ML project delivery
A defined ML infrastructure project with a clear scope and timeline. We take ownership from architecture through delivery: pipelines, serving, integration, and handoff.
HOW IT STARTS
From the first conversation to engineers contributing.
We cut out the process overhead that slows most engineering partnerships down. A focused conversation, the right engineers, and a fast path to contributing.
01
We learn what you're building
We start with a focused call to understand your ML infrastructure, your stack, and what you're trying to solve. You talk to someone who knows Scala and ML, not a sales process.
02
We match you with the right engineers
Based on your stack and requirements, we recommend the right engagement model and match you with Scala engineers who have solved similar problems in production. We align on scope, timeline, and expectations before anything starts.
03
Engineers start within days
Once agreed, we move fast. Engineers onboard into your tools and workflow and start contributing within days, not weeks. We stay close throughout to make sure the work is right and the relationship is working.
Your ML infrastructure deserves engineers who have done this before.
You've got an AI or ML problem to solve. We've spent years building the Scala infrastructure that makes these systems work in production. Tell us what you're building and we'll take it from there.