We build ML models that forecast outcomes, flag anomalies, and recommend actions using your historical data. Custom algorithms built for your specific business problem.
We build custom AI systems that deliver measurable ROI for established companies. Production-grade solutions built by engineers who've shipped 150+ AI projects. Purpose-built systems around your data, your workflows, and your team.
Surface trends and anomalies in your data that would take a human team weeks to find manually.
With the right data and the right approach, custom ML models consistently outperform off-the-shelf tools.
Go from raw data to a working prototype that proves the model can deliver results for your use case.
Off-the-shelf AI tools work for generic problems. If your business runs on proprietary data with specific rules, you need a model built for you.
We train models on your historical data to forecast outcomes specific to your business. Whether it's predicting which customers will churn, which repair orders have errors, or which products a shopper will buy next, the model learns from patterns only your data contains.
Custom anomaly detection surfaces the outliers your team doesn't have time to find. Amerit Fleet's ML model flags billing errors across thousands of repair orders daily, catching issues that used to slip through manual review.
Every model we build includes retraining pipelines so it improves as your data grows. New products, new customers, new patterns. The model adapts without starting from scratch.
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Models that analyze your historical data to forecast future outcomes. DataFlik uses our ML model to predict which houses in America will be listed for sale with 85% accuracy.
Systems that sort, categorize, and flag data automatically. From identifying billing errors in repair orders to detecting fraud in financial transactions, these models handle the high-volume review work your team can't scale.
Personalized product and content recommendations based on user behavior and purchase history. Cymbiotika's ML recommendation engine replaced their basic algorithm and delivered more accurate product matches to shoppers from day one.
We analyze your data to determine if it has the predictive power needed. If it doesn't, we tell you before you spend a development budget.
We build a working model in 4 to 6 weeks and test it against your real data. You see measurable results before committing to a full build.
We wrap the model in cloud infrastructure with training, retraining, and monitoring pipelines so it runs reliably in your environment.
We deploy into your infrastructure, train your team, and hand over all code and models. You own everything.
Machine learning with a measurable impact
Machine learning is the underlying technology. It builds models that predict, classify, and recommend. Agents and chatbots are applications that can use ML models, but ML on its own solves problems like forecasting, anomaly detection, and pattern recognition.
Historical data relevant to the problem you want to solve. It doesn't need to be perfect. Part of our process is assessing data quality and cleaning it before training.
We start every engagement with a data exploration phase. If the data doesn't have predictive power, we tell you before any model development begins.
Yes. We deploy into AWS, Azure, GCP, or on-premise environments. You own the model, the code, and the pipeline.
A working prototype typically takes 4 to 6 weeks. Full production deployment takes 12 to 16 weeks depending on data complexity and integration requirements.
We build retraining pipelines so the model improves as new data comes in. We can provide ongoing support or train your team to manage it internally.
Build a new platform, or add AI capabilities to existing software, we deliver solutions that work in real operational environments.
Meet with founders Andrew Amann & Pavel Kirillov
Custom AI implementation roadmap
No sales pressure
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