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OUR BEAT

We build software, ship ML models, and deploy AI systems. Usually on the same engagement.

Not sure where to start?

Tell us what's slowing you down. We'll tell you which capability fits.

→ AI Product Engineering

"We want AI in our product but can't make it work reliably outside of a demo."

→ ML Systems & Production

"Our data science team has models. None of them are in production."

→ Enterprise AI Integration

"Our teams are drowning in internal data they can't query fast enough to make decisions."

→ Software & Platform Engineering

"Our codebase was built for where we were. Every new feature takes twice as long as it should."

→ Data & Cloud Infrastructure

"Our pipelines break silently and our infrastructure wasn't designed for the workloads we're running now."

All capabilities

Data & Cloud Infrastructure

AI systems are only as reliable as what's underneath them. We build data pipelines, cloud architecture, and delivery infrastructure designed for ML workloads and systems that can't afford to go down.

Kubernetes Terraform GitHub Actions AWS / Azure / GCP Apache Spark Kafka
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All segments

Software & Platform Engineering

Your codebase was built for where you were. We build new products and modernize systems that have outgrown themselves — without stopping what's already running.

React Node.js .NET / Java TypeScript PostgreSQL Microservices
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Scaleups · Enterprise · PE

Enterprise AI Integration

Your teams are making decisions based on data they can't fully access. We build RAG pipelines, LLM agents, and conversational interfaces on your existing enterprise data — secure, on-premises, auditable.

RAG LangChain FastAPI PostgreSQL Docker / K8s
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Enterprise · All segments

ML Systems & Production

A model that lives in a notebook isn't a product. We take ML from proof-of-concept to production — pipelines, monitoring, retraining, deployment.

MLflow, Airflow, Python,Scikit-learn, Docker / K8s
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Enterprise · PE

AI Product Engineering

Most teams add AI to a product. We build products around what AI makes possible — recommendation engines, intelligent search, LLM-powered workflows, architected for production.

Python, LLMs, LangChain, React, Node.js, AWS / Azure
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Scaleups · Founders

How we work

How we work has changed.

Our engineers use AI tools as a standard part of how they build — not as an experiment. That changes the pace of an engagement, the leverage per engineer, and what's achievable in a fixed timeline. It also changes what we can commit to.