NLP & Language AI
Text classification, entity extraction, sentiment analysis, semantic search, and document understanding: at scale and in production.
What we build
Categorize documents, tickets, reviews, or messages by topic, intent, or priority. Multi-label and multi-class.
Extract named entities, dates, amounts, locations, and custom domain entities from unstructured text.
Measure sentiment at document, sentence, and aspect levels. Works on reviews, support tickets, and social data.
Vector-based search that finds conceptually similar documents, not just keyword matches. Built on embedding models.
Extractive and abstractive summarization of long documents. Useful for contracts, reports, and research papers.
RAG pipelines that let users ask questions against a private document corpus. Grounded answers with source citations.
Stack
Process
step 01
We assess your text corpus: volume, languages, quality, and domain specificity. We identify labeling requirements.
step 02
We choose between fine-tuning a pre-trained model vs. few-shot prompting based on your data size and latency requirements.
step 03
We build evaluation datasets and metrics beyond accuracy: F1, precision-recall curves, and qualitative spot-checks.
step 04
Production API with latency optimization, caching, and monitoring for distribution shift in input text.
FAQ
