Natural Language Processing

Ticket classification and routing, sentiment dashboards, contract and document data extraction, summarization pipelines, and semantic search over enterprise knowledge. We build with transformer models (BERT family, fine-tuned LLMs) and spaCy, choosing the lightest model that hits your accuracy target.

A scoped pilot — say, classifying your support tickets — takes 3-5 weeks; production systems with integrations and retraining pipelines take 2-5 months. Pre-trained models mean we rarely start from zero, which keeps timelines short.

Pilots start around $8,000-$18,000, with production systems running $25,000-$70,000 depending on languages, volume, and integrations. Inference costs are low — classifying a million documents per month typically costs $50-$500 in compute or API fees.

Yes — fine-tuned model weights, training data we prepare, and all pipeline code transfer to you with full IP rights, deployable in your own cloud or on-premise. For sensitive data we deploy fully self-hosted so text never leaves your infrastructure.

We agree on metrics upfront (accuracy, F1, precision/recall per class), validate against a held-out test set from your real data, and route low-confidence predictions to human review. Production systems typically reach 90-97% accuracy on defined tasks within two tuning cycles.

Deployment as APIs or batch jobs into your existing systems on AWS, Azure, or GCP, plus 90 days of included post-launch tuning. Retainers from $800/month cover drift monitoring, retraining on new data, and quarterly accuracy reports.