VECTOR DATABASE SANDBOX

DarkLabs

Test DarkMath on your own data safely

A secure, isolated sandbox where your team can validate identity resolution and audience lift on your real data  with no risk to production, before you commit.

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Isolatedno production risk
Vector-onlyraw PII stays with you
2.5k QPSweb-scale infrastructure
Self-serveno DarkMath engineers needed
THE BASICS

What is DarkLabs?

DarkLabs is DarkMath's secure sandbox for testing AI idenity resolution on your own data.
It's an isolated environment where enterprise data teams can test embedding models, experiment with matching logic, and validate use cases on real data without disrupting production systems. With vector-only ingestion, you convert data to vectors before sending it, so raw personal information never leaves your perimeter.
WHAT YOU GET

Everything you need to prove it

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.
USE CASES

What can your team do in DarkLabs?

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 assesment
Understand the fragmentation and duplication in your customer data
Team training
Let your data scientists explore vector-based matching without production stakes
THE TECHNOLOGY
Under the hood: a secure vector sandbox
For data teams who want the specifics, how DarkLabs stays private and fast at web scale.
// raw PII never transmitted
Vector-only ingestion
Convert records into vector embeddings inside your own perimeter and send only those vectors. DarkLabs operates on the math, so personal data never crosses the boundary.
// ~2.5k QPS per node
HNSW-indexed vector DB
Approximate nearest-neighbor search over a Hierarchical Navigable Small World graph, with horizontal sharding for web-scale datasets at millisecond latency.
// SOTA embeddings + tokenization
Model + tokenization access
The same embedding models and intelligent tokenization behind DarkMatch, DarkMirror, and DarkWatch — swap and benchmark models without infrastructure overhead.
// API + notebooks + viz
Self-service tooling
REST API access, a coding/notebook platform, and visualization tools let your team run, score, and inspect experiments end to end — no DarkMath engineering in the loop.

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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