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.
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.
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Our process starts with connecting you to our leaders, who have built over 150 projects. If aligned, the second call will include our Product Managers and Engineers to scope the project. The final step is a proposal presentation.
We start by immersing ourselves in your business, industry, and existing software. We then build a functional proof-of-concept to validate the technical feasibility. This hands-on planning process arms you with a clear architectural blueprint and a precise technical implementation roadmap for the next phase.
Production-grade AI software, built in your infrastructure, with real-time visibility into every task. No prototypes, no maybe's. Just code that delivers testable ROI. We trademarked Full Transparency Development ™ , so you’re never left in the dark.
Go live, measure results, and scale AI across your organization at a predictable cost. We build with the right guardrails and measure KPIs on real results. We train your team to operate and manage what we developed, or we can provide ongoing maintenance and support.
Thanks to 923, we've dramatically increased throughput and reduced manual errors.
The project management team was on top of every phase and maintained detailed notes.
The team that supports and executes the implementation has vast knowledge and experience.
The 923 team was fantastic to work with and taught me a lot about ML and AI.
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
