Working patterns for real AI problems.

Your ML team wants to train on the real data: actual transactions, actual claims, actual interactions, actual outcomes. Today they get a sanitized extract that strips the signal, a synthetic surrogate that drifts from reality, or a months-long clean-room build. The high-value models stay trapped behind the data classification policy. This solution unlocks the real corpus. Train, evaluate, score, and run analytics on protected data in the compute environments you already trust: Databricks, SageMaker, Snowflake, Vertex AI, Azure ML, your own on-prem GPUs. Enigmata Anonymizer for workflows where reversal would never be permitted. Enigmata Cipher for workflows where authorized reveal is required. Audit on every step.

Some of your most valuable assets are the ones you can’t ship: research data, customer behavior, transaction histories, internal expertise encoded in models. Today you either give counterparties raw access (and lose control), send anonymized extracts (and lose precision), or leave the value on the table. This solution makes a third path real. Vendors, partners, consortia, researchers, and licensees compute against your protected data: train models on it, analyze it, query it, build derivatives. The raw values never leave your perimeter. You set the policy. They get the utility. Every access is audited.

Some AI workflows should run entirely inside your environment. Enigmata lets you deploy approved open-weight or private models in your VPC, data center, or controlled cloud environment, with Enigmata Policy and audit controls applied from retrieval through response. Because the underlying data is protected with Enigmata Cipher, access can be policy-gated or licensed securely without exposing plaintext. This is built for high-sensitivity work such as M&A, security incidents, executive compensation, source code, clinical records, and other cases that require customer-controlled inference end to end.