Teach machines to understand language at scale

An LLM API is a general-purpose model you prompt per request great for one-off tasks. NLP is a production pipeline tuned for your data: consistent labels, batch throughput, accuracy benchmarks, and cost controls at scale. We use LLMs inside NLP when they fit; we add specialized models, fine-tuning, and orchestration when you need repeatable results on millions of documents.

It depends on the task, data quality, and language. During scoping we set target metrics (precision, recall, F1, or human-eval agreement) and validate on a held-out test set before launch. Most production pipelines land in the 90–97% range for well-defined tasks; we document tradeoffs when perfect accuracy isn't realistic.

Not always. Many projects start with few-shot LLM classification or zero-shot extraction, then add labels only where accuracy or cost demands it. When fine-tuning pays off, we help you label efficiently (active learning, weak supervision) and use the smallest labeled set that hits your target.

We design pipelines with your compliance requirements in mind: PII detection and redaction, regional data residency, VPC or self-hosted deployment, and contracts that keep your text out of model training. Sensitive workloads can run entirely on your infrastructure with no data sent to third-party APIs.

Yes common for regulated industries. We deploy to your cloud (AWS, GCP, Azure) or on-prem with Docker/Kubernetes, optional GPU nodes, and private model endpoints. The same REST API contract works whether we host or you do.