Build accurate models and integrate them into your CRM – no data science needed.
We will estimate model deployment time with your current resources.
No data scientists needed
No coding required (only SQL)
Works well with raw/messy data
SOC2 and ISO Certified
Traditional data science is slow and expensive
Accelerate time to production and cut costs with
Data analysts can build ML models with SQL — without coding or data science skills
Chat-based use case identification extracted from thousands of popular predictive questions, proprietary LLM-based query generation and SQL co-piloting, built-in connectors and ETLs, automated ML optimization, built-in data validations and guardrails based on the analysis of hundreds of common pitfalls, and one-click deployment.
Automate your data prep in our secure platform
While no data prep can be fully automated, manual work can be significantly reduced by automating data cleansing, transformation, and feature engineering. The platform connects to raw data, handles missing values, and normalizes the data using patented data engineering algorithms that specialize in transforming raw tabular and transactional data into an ML-ready dataset. This includes the automatic extraction of labels, construction of samples, timestamping of entities, and avoiding data leakage.
Rapidly test and scale to succeed, supported by our ML engineers
The platform includes a GenAI-powered chat that helps craft predictive questions and translates them into SQL queries, while a team of highly experienced data science engineers, who have worked on numerous projects, is available to guide you in building the best AI strategy and ensuring success throughout the process.
Common data fields built on the platform
The platform processes raw data (e.g., transactional data) and accepts business prediction inputs provided by the user in natural language. We apply a wide range of data engineering and preparation techniques, leveraging our patented technology and LLMs. These algorithms analyze the raw data, identify relevant elements, and construct an ML-ready dataset tailored to the predictive question. Dataset construction includes creating entities/samples, extracting and computing the label, transforming the data into a rich time series, aggregating diverse features, and performing feature selection.
In parallel, it generates a SQL notebook using SOTA Gen-AI techniques to manage modeling definitions.
The platform continuously monitors for critical ML issues such as bias, leakage, overfitting, and drift to ensure the integrity of the data and the custom model.
We will estimate model deployment time with your current resources.
The algorithms automatically identifies outliers, anomalies and missing data and normalizes/imputes the data.
The platform continuously monitors and recalibrates models as new data is integrated, detecting accuracy-drops or data drifts and automating adjustments in real time,
typically achieving high predictive accuracy – up to four times better than rule-based logic from initial deployment.