Natural Language Processing

Turn unstructured text into structured, actionable intelligence

Cogninest AI builds NLP systems that extract meaning from documents, conversations, and data feeds at enterprise scale. From sentiment analysis and entity recognition to semantic search and automated summarization, we make your text data work as hard as your structured data.

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The Situation

Your team manually reads thousands of documents, emails, and support tickets to extract insights. Critical information gets missed, response times lag, and there is no consistent taxonomy across departments. Every analyst interprets the same text differently.

The Deliverable

An NLP pipeline that ingests unstructured text, extracts entities, classifies intent, and delivers structured output via API. Sentiment scores, topic clusters, and named entities flow directly into your dashboards and downstream systems without manual intervention.

In Production

Core use cases

Sentiment

Sentiment Analysis

Classify text as positive, negative, or neutral across customer reviews, social media, and support tickets. Track brand perception in real time with granular emotion detection.

NER

Named Entity Recognition

Extract people, organizations, dates, amounts, and custom entities from contracts, medical records, and legal filings. Trained on your domain-specific vocabulary.

Search

Semantic Search

Move beyond keyword matching. Our vector-based search understands user intent and context, surfacing relevant results from large document repositories.

Summary

Automated Summarization

Condense lengthy reports, meeting transcripts, and research papers into actionable summaries. Configurable length, tone, and focus areas.

Tech Stack

How it works

We combine transformer-based models with classical NLP techniques, choosing the right tool for each task rather than forcing every problem through a single architecture.

  • spaCy / Hugging Face Transformers
  • BERT / RoBERTa / DeBERTa
  • AWS Comprehend / Azure Text Analytics
  • Elasticsearch / OpenSearch
  • FastAPI / Flask serving layer
  • Custom NER training pipelines
Frequently Asked Questions

NLP Services FAQs

We build NLP systems in English by default and support multilingual deployments using models like XLM-RoBERTa and mBERT. Custom language support is available based on training data availability.

Ready to build AI that reaches production?

Book a strategy call with our engineering team. We will scope your use case, estimate timelines, and show you what production-grade AI looks like for your business.