Predictions you can act on.

We train models on your closed-won history: lead scoring, churn propensity, lifetime value, and demand forecasting, plus catalog-trained recommendations that raise order value and repeat purchase.

If it is a data problem attached to revenue, we have built it: fraud detection, pricing optimization, anomaly detection. We backtest every use case against your own history before you pay.

We deploy internal LLMs and retrieval pipelines over your documents, knowledge base, and warehouse, powering search, drafting, and answers your team can trust. Your data stays inside your environment, behind your access controls, and nothing is sent to public model providers, so the whole system runs in your own cloud.

We tune models to your data and domain: retrieval-augmented generation when your knowledge changes daily, fine-tuning when a model must internalize your tone, taxonomy, and judgment, and often both. We prove a well-grounded base model cannot already clear your accuracy bar before you pay to fine-tune, and every tuned model is versioned and scored against a held-out set, so accuracy is a number you can see.

We build pipelines that classify tickets, reviews, calls, and leads, score sentiment, and route what matters to the right owner. Speech-to-text, video understanding, and image analysis turn media libraries and feedback into searchable, reportable data, so unstructured text, audio, and video become structured signal in your dashboards.