Natural Language Processing turns unstructured text — reviews, tickets, contracts, transcripts — into structured data your systems can act on. We build sentiment models, entity extractors, classifiers, and semantic search on top of your text data, not a generic sentiment API.
Natural Language Processing (NLP) is the field of AI focused on extracting structured meaning from unstructured text — sentiment analysis, named entity recognition, document categorization, and semantic search across large corpora.
Classical models (fast, cheap, fixed taxonomy) and LLM-based approaches (flexible, higher cost) both have a place. The right choice depends on latency, privacy, and how often your label set changes.
Semantic search is closely related to RAG pipelines — both use embeddings to retrieve relevant text by meaning. NLP classification and entity extraction often sit in front of or alongside a RAG system.
Each extracts a different kind of structure from raw text, with a different data requirement and accuracy benchmark.
Scores reviews, support tickets, or social mentions as positive, negative, or neutral — often with an intensity score, not just a label.
Best for
Reviews, support, social listening
Data need
Labeled examples per domain
Identifies names, dates, amounts, product codes, or custom entity types from contracts, emails, or transcripts.
Best for
Legal, finance, ops automation
Data need
Annotated entity examples
Routes tickets, emails, or filings to the right queue or category without manual triage.
Best for
Support routing, compliance sorting
Data need
Labeled examples per category
Retrieves relevant results based on meaning — a search for "cancel my plan" also surfaces docs about "subscription termination."
Best for
Knowledge bases, internal search
Data need
Existing document corpus
Estimates assume a reasonable volume of representative text data is available. Add cost for large-scale manual labeling if starting from zero.
Sentiment analysis
Timeline
3–4 weeks
Cost
$6K–12K
Entity extraction
Timeline
4–6 weeks
Cost
$10K–20K
Document classification
Timeline
3–5 weeks
Cost
$8K–16K
Semantic search across a large corpus
Timeline
4–8 weeks
Cost
$12K–30K
| Use Case | Typical Timeline | Typical Cost |
|---|---|---|
| Sentiment analysis | 3–4 weeks | $6K–12K |
| Entity extraction | 4–6 weeks | $10K–20K |
| Document classification | 3–5 weeks | $8K–16K |
| Semantic search across a large corpus | 4–8 weeks | $12K–30K |
A clear path from data audit to production monitoring — so every engagement ships with a defined taxonomy, a held-out benchmark, and a deployment plan.
Review existing text volume, quality, and any prior labeling to determine whether a classical model or LLM-based approach fits the accuracy and cost target.
Define categories or entity types precisely — ambiguous taxonomy is the most common cause of low classification accuracy downstream.
Classical model, fine-tuned LLM, or prompted LLM — chosen per capability, not a single default across the whole system.
Precision/recall on a held-out labeled set before launch, following the same evaluation-first standard across all our AI work.
Production deployment with accuracy monitoring — see our MLOps checklist for what's tracked post-launch.
Sentiment scoring on millions of reviews with a fixed category set — a small trained classifier is far cheaper per call than an LLM prompt.
Ticket routing where new categories appear regularly, or extraction that needs contextual judgment — an LLM adapts without retraining.
Real-time filtering at scale favors a lightweight trained model over an LLM call on every item.
Concept-level retrieval requires embeddings from a language model — this is the same mechanism behind RAG.
