Build faster with Fast Data Science
Modular Natural Language Processing components for search, insights, and automation. Compose, deploy, and scale — with transparent pricing and support.
Our catalogue spans classic linguistic pipelines and cutting-edge deep learning
Pluggable Architecture
Pick only what you need: entity extraction, summarization, topic modeling, document classifiers, AR object detection, monocular depth estimation, neural style transfer, and more. Each module is independently versioned and can be deployed as a microservice or embedded library.
Inputs and outputs are strictly typed using OpenAPI/JSON Schema definitions. That means you can wire modules together like LEGO — transform documents, enrich with metadata, then feed into search or analytics without custom glue code.
Our SDKs in Python, JavaScript, and Java include retries, streaming, and circuit breakers, so the same code works in batch pipelines and low-latency applications.
Security by Design
Self-host in your VPC or deploy on our managed EU cloud. Either way, your data stays encrypted in transit and at rest with fine-grained, role-based access control.
Every module exposes a read-only audit trail: configuration, model version, dataset hash, and inference metadata. This makes compliance reviews straightforward and reproducible.
We provide optional data minimisation modes and PII redaction operators that protect sensitive information prior to model inference — ideal for regulated industries.
Scales Effortlessly
Start small with a single container and grow to many replicas behind an autoscaling gateway. Our orchestrator-friendly images support health checks, blue/green deploys, and canary releases.
CPU, GPU, or mixed: modules advertise their resource profile and warm-up characteristics so your platform can schedule them optimally. Batch endpoints support async fan-out and idempotent retries.
Built-in caching, quantization options, and dynamic batchers keep latency predictable under bursty traffic while controlling spend.
Observability
Every request emits structured logs, metrics, and traces. Correlate latency spikes to specific models or inputs and drill into hot spots with distributed tracing.
Quality doesn’t stop at latency: dataset drift detectors compare live traffic with your reference data and trigger alerts or shadow evaluations when thresholds are breached.
AB testing and interleaving tools let you compare model variants safely in production and roll forward only when improvements are statistically significant.
