DarkLabs: Vector Database Sandbox

DarkLabs is DarkMath's secure experimental environment for enterprise data teams. Test embedding models, experiment with matching logic, and validate use cases on your own data without disrupting production systems. DarkLabs operates on a vector-only ingestion model, convert your data to vectors before transmission so raw PII never leaves your secure perimeter. Access state-of-the-art embedding models and intelligent tokenization without infrastructure overhead.

What DarkLabs Provides

Isolated Sandbox Environment: Test DarkMath capabilities on your data in a secure, isolated environment. No production risk, no cross-contamination with other customers.
State-of-the-Art Embedding Models: Access the same embedding models that power DarkMatch, DarkWatch, and DarkMirror. Compare different models for your use case.
Vector-Only Ingestion: Preprocess data into vector representations before sending to DarkLabs. Raw PII stays in your environment; only mathematical representations are transmitted.
Web-Scale Infrastructure: DarkLabs runs on the same HNSW-indexed vector database infrastructure that handles 2.5k queries per second in production.
Self-Service Experimentation: Your data team can run experiments without DarkMath engineering involvement. API access, coding platform, and visualization tools included.

DarkLabs Use Cases

Proof of Concept: Validate DarkMath performance on your data before production commitment
Embedding Evaluation: Compare different embedding models to find optimal fit for your domain
Threshold Tuning: Experiment with match confidence thresholds to balance precision vs. recall
Data Quality Assessment: Understand the fragmentation and duplication in your customer data
Team Training: Let your data scientists explore vector-based matching without production stakes

Transform your data into Revenue today with DarkLabs

Experience immediate impact with our straightforward integration process and easily measure the benefits of DakLabs

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