Generic models solve generic problems. The work that moves your business needs custom architectures, your own data, and engineers who own the system end-to-end. Our AI development and machine learning development team designs, trains, deploys, and operates production models — including the MLOps plumbing that keeps them honest in year two.
Plenty of teams can train a model that scores well on a clean test set. Far fewer can deliver one that holds up under traffic skew, data drift, and the operational realities of a regulated business. That's the gap we close.
The senior engineers you meet on day one are the same people who build and maintain your system. No bait-and-switch staffing.
Testing, monitoring, and a safe way to undo changes are built in from the start, not bolted on after something goes wrong.
We agree on what success looks like before we start, test honestly against it, and tell you what the system is bad at, not just what it's good at.
Everything is documented and saved as we go, so updating or rebuilding your system later is straightforward instead of a dig through old files.
The same team that designs your system is the team that builds it, launches it, and looks after it. Nothing gets lost in a handoff.
We take proven, publicly available AI models and teach them the specifics of your industry and your business, so they respond like an expert in your field instead of a generalist.
AI systems that can plan, use software tools, and carry out multi-step work on their own, with a clear record of every step they take.
Not every problem needs a chatbot. For forecasting, spotting patterns in your numbers, or analyzing images, we pick the right tool for the job.
The behind-the-scenes plumbing that turns a promising experiment into a dependable system that stays up, stays fast, and keeps improving.
Every project follows the same five phases. The pace varies; the structure stays.
A two-week look at your goal and your data to decide whether AI is even the right tool for the job. If it isn't, we tell you.
Most AI failures are really data failures. We clean up, organize, and label your data, and set up a process you'll keep using long after launch.
We design, build, and test the system in short cycles, checking in often with the people in your company who know the work best.
We make the system fast, set up monitoring, write the how-to guides your team needs, and stress-test everything before going live.
Ongoing check-ups and scheduled tune-ups, so quality problems get caught before your users ever notice them.
AI development is the design, training, deployment, and ongoing operation of artificial intelligence and machine learning systems built for a specific business problem. Galatine Technologies builds custom AI development and machine learning development solutions, including model design, training, deployment, and the MLOps needed to keep them reliable in production.
Machine learning development is a subset of AI development focused on training models that learn from data. AI development is broader, covering data pipelines, model design and training, system integration, deployment, and MLOps. Galatine Technologies delivers the full stack as a single team.
Both. Galatine Technologies builds custom architectures when a problem demands it and fine-tunes or integrates foundation models when that is the faster path to ROI. The choice is driven by your data, latency, cost, and compliance requirements.
Tell us what you want the system to do, what data you have, and any limits we should know about. We'll come back with an honest take on whether it's doable, what it would involve, and what a first project would look like.