NLP natural language intelligence.

Natural language processing systems for text classification, entity extraction, sentiment analysis, summarization, and semantic search — engineered for the specific languages, domains, and accuracy targets your business actually needs.

Overview

What it means in practice.

NLP problems split into two camps: ones where modern LLMs excel out of the box, and ones where smaller fine-tuned models win on cost and latency. We help you pick correctly. The wrong tool for the job costs ten times more and runs ten times slower for the same result.

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What we deliver

Capabilities & deliverables.

Every engagement gets shaped to fit, but these are the building blocks we rely on.

01

Text Classification

Document categorization, intent detection, content moderation, and routing logic — accurate, fast, and explainable enough to debug.

02

Entity Extraction

Named entity recognition for invoices, contracts, medical records, and structured data extraction from unstructured text. Domain-specific accuracy.

03

Sentiment & Topic Analysis

Brand monitoring, customer feedback analysis, and topic modeling across reviews, support tickets, and social conversations.

04

Summarization

Document summarization for research, meeting notes, support tickets, and long-form content. Extractive or abstractive, picked per use case.

05

Semantic Search

Vector search and hybrid retrieval for knowledge bases, product catalogs, and document repositories. Better than keyword search when context matters.

06

Multilingual Pipelines

Cross-language NLP for global products. We work in English, Hindi, Arabic, Spanish, German, and beyond — with realistic accuracy expectations.

spaCy Hugging Face Transformers sentence-transformers Pinecone Weaviate FastText PyTorch scikit-learn
Why it works

The SD Technolabs approach.

Two decades of engineering practice, sharpened by the realities of production AI.

01

LLMs vs. classical, picked deliberately

We benchmark LLM-based and traditional ML approaches on your data, then recommend the one that wins on cost and accuracy.

02

Production latency targets

Sub-100ms inference where it matters. We optimize, quantize, and serve models efficiently rather than calling expensive APIs for everything.

03

Evaluation on your data

Standard benchmarks lie about real-world performance. We build evaluation sets from your actual data and target accuracy on those.

04

Multilingual without compromise

Indian language NLP done right — proper tokenization, transliteration support, and benchmarks against native speakers.

Ready to start something good?

Let's discuss how this fits your business. We reply within one working day.

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