Built by ML engineers, for ML engineers
We believe every team deserves access to production-grade ML tooling without the complexity.
Our Mission
CorePlexML exists to democratize machine learning. We combine AutoML, MLOps, data privacy, synthetic data generation, and explainability into a single integrated platform. Our goal is to eliminate the fragmented toolchain that slows down ML teams and replace it with one cohesive workflow — from raw data to production deployment.
Whether you are a solo data scientist prototyping your first model or an enterprise team deploying hundreds of models at scale, CorePlexML provides the tools you need without the operational overhead.
The Problem We Solve
Most ML teams juggle 5-10 disconnected tools just to get a model into production. The result: months of integration work, data silos, compliance gaps, and models that never ship.
- Separate tools for training, deployment, monitoring, privacy, and data prep
- Weeks of glue code to connect APIs, databases, and model registries
- Compliance is bolted on after the fact — audit gaps, manual PII checks
- No lineage: impossible to trace a prediction back to its training data
- One platform: AutoML, MLOps, Privacy Suite, SynthGen, ML Studio, Dataset Builder
- From CSV upload to production endpoint in under 10 minutes
- Privacy and compliance built in: 72+ PII types, HIPAA, GDPR, PCI-DSS, CCPA
- Full lineage: every prediction traces back to dataset version, experiment, and model
Our Values
Innovation
We push the boundaries of what automated ML can do. From AutoML to synthetic data generation, every module is designed to deliver state-of-the-art results with minimal effort.
Privacy-First
Data protection is not an afterthought. Our Privacy Suite with 72+ PII types, compliance profiles, and audit trails is baked into the core of the platform.
Simplicity
Complex ML workflows should not require complex tools. Every feature is designed to be accessible to data scientists and business users alike through intuitive interfaces.
Open Standards
We build on proven open-source foundations — H2O.ai, PostgreSQL, FastAPI — and expose everything through a well-documented REST API and Python SDK. No vendor lock-in.
Product Roadmap
Foundation
Core AutoML engine and PostgreSQL-first architecture. First public experiments with H2O.ai integration, dataset versioning, and SHAP explainability.
Enterprise modules
Privacy Suite (HIPAA, GDPR, PCI-DSS, CCPA), SynthGen for synthetic data with CTGAN/CopulaGAN/TVAE, and full MLOps pipeline with canary deployments and drift detection.
Intelligence layer
ML Studio for What-If analysis, Dataset Builder with conversational AI, A/B testing, auto-retraining, and the Python SDK. 320+ REST endpoints documented.
Scale
On-premise deployment option, SSO/SAML/OIDC, GPU scheduling, RBAC with audit logs, and dedicated enterprise support tier.
Architecture
A modern stack designed for reliability, security, and horizontal scale.
H2O.ai AutoML
Battle-tested ML engine powering automated training, cross-validation, and stacked ensembles across XGBoost, GBM, Deep Learning, and GLM.
PostgreSQL
Single source of truth. All state — jobs, models, experiments, audit logs — persisted with numbered migrations. No Redis, no external caches.
FastAPI + Python
Async Python backend with 320+ REST endpoints, automatic OpenAPI documentation, and type-safe request validation via Pydantic.
LangGraph AI Agent
Conversational Dataset Builder powered by OpenAI and Anthropic LLMs. Multi-step data preparation through natural language with semantic intent classification.
Worker Queue
PostgreSQL-backed job queue for heavy tasks: AutoML training, batch predictions, report generation, and synthetic data generation.
Docker + On-Prem
Containerized deployment with Docker Compose. Enterprise customers can run the full stack on-premise behind their own firewall.
Ready to ship ML faster?
Start building with CorePlexML today. Free tier, no credit card required.