Natural Language Processing Services

Scope determines timeline more than anything else. A focused text classifier or NER model on a reasonably clean labeled dataset ships to production in 8 to 12 weeks: two weeks of data assessment and labeling strategy, four to six weeks of model development and iteration, and two weeks of integration and hardening. A full document intelligence pipeline — intake, OCR, extraction, validation, output API — typically runs 16 to 20 weeks. We provide a detailed timeline after a two-week discovery sprint.

Both, depending on what the use case warrants. For tasks where latency tolerance is high, data privacy concerns are manageable, and quality off the shelf is close enough, LLM API calls (GPT-4o, Claude, Gemini) with careful prompt engineering are cost-effective and fast to ship. For tasks that demand low-latency inference at scale, require processing sensitive data that can't leave your infrastructure, or need accuracy improvements beyond what prompting achieves, we fine-tune open-source models (Llama, Mistral, DeBERTa, domain-specific BERT variants). Most production systems end up using both.

It depends on the task complexity and model strategy. For a binary or small-class classifier using a pre-trained foundation model, 200 to 500 labeled examples per class are often sufficient for strong performance. For a custom NER model on specialized entity types, plan for 1,000 to 5,000 annotated sentences. For fine-tuning a generative model, instruction datasets of 500 to 2,000 examples typically move the needle significantly. If you have limited labels, we apply techniques like data augmentation, active learning, and few-shot prompting to reduce labeling burden while maintaining quality.

NLP features integrate at whatever layer makes sense: REST or gRPC APIs consumed by existing services, async workers that process queues (Kafka, SQS, RabbitMQ), database triggers that run extraction on new records, or embedded SDKs for client-side inference where latency demands it. We've connected NLP pipelines to Salesforce, Zendesk, SharePoint, custom CRMs, and proprietary data warehouses. Integration design is included in the discovery sprint.

We build production monitoring into every engagement: prediction confidence distributions, label drift alerts, and a human-review sample pipeline that flags low-confidence outputs for analyst review. Those reviewed examples feed back into the training set on a scheduled basis (monthly is typical). We also deliver a retraining runbook so your team can trigger retraining runs without KodersCode involvement once you're comfortable with the tooling. For clients who want ongoing support, we offer retainer arrangements.