We turn data into decisions and automate processes that today consume time and resources. We specialize in building data infrastructure, machine learning models, and AI-powered applications that go to production and generate measurable value.
Unify data sources that currently don't talk to each other
We connect legacy databases, cloud systems, and external APIs into a single accessible environment. We implement ETL pipelines so information flows reliably, stays current, and reaches the systems that need it without manual intervention.
Act on what's happening now, not on last night's report
We implement systems that capture, process, and analyze data the moment it's generated—transactions, operational events, user behavior. That means detecting fraud in seconds, alerting on anomalies before they escalate, and making decisions based on current reality.
Turn complex data into information your team actually uses
We design custom dashboards that transform operational data into visualizations anyone in your organization can read and act on. These aren't static reports—they feed from real-time data and update automatically, so decisions rest on current numbers.
Deploy models that solve specific problems and hold up in production
We design, develop, and implement machine learning models adapted to your business context. We cover the full cycle: data preparation, training, validation, and production deployment. Applications include automated credit scoring, fraud detection, and anomaly detection in regulated environments.
Automate processes that today depend entirely on manual work
We develop applications that integrate AI into operational workflows: document classification, automatic categorization, text analysis, and process automation. Every application is built to be auditable—because in regulated environments, a process you can't explain creates more risk than the one it replaced.
Manage your data pipelines with the same discipline as your software
We apply DevOps methodologies to data: pipeline automation, continuous quality testing, transformation versioning, and flow monitoring. Data arrives on time, with expected quality, and problems surface before they reach your reports or your models.
The full cycle rather than a slice of it: connecting the sources that do not talk to each other, ETL and real-time pipelines, dashboards your team actually reads, and machine learning models taken through data preparation, training, validation, and production deployment — managed with the same DataOps discipline as your software.
In practice, yes. Models are only as good as the data reaching them, so most engagements start by unifying legacy databases, cloud systems, and external APIs into one accessible environment with reliable pipelines. Skipping that step is the most common reason AI pilots never reach production.
Production is the point. We built the AWS infrastructure to fine-tune Autoptic’s domain-specific LLM while the product kept shipping, and scaled a cloud HPC cluster to thousands of GPUs for ML research at Meta (formerly Facebook). Both were production systems with cost control and orchestration, not demos.
Every model has to produce results you can explain to a regulator. That means auditable pipelines, versioned transformations, and traceable decisions built in from the start — because changing how events are recorded has to be coordinated with compliance before the first commit, not after the system is live.
Primarily AWS, and on Azure or GCP when that is where your stack already lives. We built an automated data pipeline for one of the largest US real estate investment services firms, replacing manual integration across three external sources that was producing duplicates and stale records.
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