AI & Machine Learning

NLP Services
That Extract Structure From Unstructured Text at Scale

Numlytics delivers expert natural language processing services for enterprises across the US, UK, Australia & UAE. Text classification, named entity recognition, sentiment analysis, document processing, and information extraction - built with spaCy, Hugging Face transformers, and Azure AI Language to automate the manual reading, tagging, and extraction work your teams currently do by hand.

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Custom-trained models on your domain vocabulary - not generic off-the-shelf
spaCy, Hugging Face BERT, Azure AI Language & AWS Comprehend specialists
Production APIs that integrate with your existing workflows
Up to 50% lower cost vs US/UK AI consulting firms
Typical Outcomes
95%
Extraction accuracy on
domain-specific documents
80%
Reduction in manual
document review time
3wk
First working NLP model
in production
50%
Lower cost vs US/UK
AI consulting firms
We build with
Python / spaCy
Hugging Face
BERT / RoBERTa
Azure AI Language
>AWS Comprehend
Azure Document Intelligence
Tesseract OCR
FastAPI
What We Build

When You Need Structured Output From Unstructured Text

Every organisation accumulates vast volumes of unstructured text - contracts, customer emails, support tickets, regulatory filings, medical records, product reviews, news articles. Most of it sits unanalysed because reading and categorising it manually at scale is too slow and too expensive.

Natural language processing is the discipline that turns text into structured data your systems can act on. Not generative AI that produces new text - applied NLP that extracts, classifies, and structures what already exists. Which contract clauses carry risk? Which support tickets need escalation? Which customers are expressing churn intent? Which documents mention specific entities, dates, or obligations?

Our NLP services build the models and pipelines that answer these questions automatically - text classifiers trained on your categories, entity recognisers trained on your domain vocabulary, sentiment models calibrated to your customer language, and document extraction systems that replace manual data entry with structured, validated output.

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Where We Replace Manual Work
"We have analysts manually reading thousands of documents every month"
Contracts, compliance filings, financial reports, or patient records processed by humans because there's no automated way to extract the specific fields, clauses, or entities the business needs. Document processing NLP can automate 80–95% of this volume.
"Our support team manually tags and routes thousands of tickets daily"
Text classification models trained on your category taxonomy can tag and route incoming tickets, emails, or forms automatically - with human review reserved for low-confidence predictions and edge cases.
"We have millions of customer reviews and can't read them all"
Sentiment analysis and aspect-based opinion extraction surfaces what customers are actually saying about specific product features, service dimensions, or competitor comparisons, at a scale no manual review team can match.
"We need to scan contracts for specific clauses before legal review"
Named entity recognition and clause extraction models that identify specific legal entities, dates, obligations, termination provisions, and risk clauses - so legal reviewers focus on flagged documents rather than reading everything from scratch.
What We Deliver

Six NLP Capabilities We Build for Enterprise Automation

Custom-trained models for your domain, not off-the-shelf classifiers that misunderstand your industry's vocabulary and context.

Text Classification

Custom text classification models trained on your category taxonomy - routing support tickets, categorising documents, tagging customer feedback, classifying regulatory filings. Multi-class and multi-label classification with confidence scores and human-review queues for low-confidence predictions.

Fine-tuned BERT / DistilBERT classifiers
Multi-class & multi-label support
Confidence threshold & review queue design
Named Entity Recognition (NER)

Custom NER models trained to extract the specific entities your domain requires - company names, contract parties, monetary amounts, dates, product names, medical terms, regulatory references, geographic locations, beyond what generic models recognise.

Custom entity type training on your corpus
spaCy + Hugging Face NER pipeline
Entity linking & disambiguation
Sentiment & Opinion Analysis

Beyond positive/negative/neutral - aspect-based sentiment analysis that identifies what customers feel about specific features, products, or service dimensions. Calibrated to your customer language and industry context so "fast" means speed, not impulsive. Trend tracking over time and competitor sentiment comparison.

Aspect-based sentiment per product feature
Domain-calibrated sentiment scoring
Trend analysis & alert thresholds
Document Processing & OCR

End-to-end document processing pipelines - OCR for scanned documents, layout analysis for structured forms, table extraction from PDFs, and field extraction from semi-structured documents like invoices, contracts, and applications. Azure Document Intelligence or Tesseract for digitisation; custom extraction models for field-level accuracy.

OCR pipeline (Tesseract / Azure DI)
Table & form field extraction
PDF-to-structured-data pipeline
Information Extraction & Relation Mining

Structured fact extraction from unstructured text, identifying relationships between entities (company A acquired company B on date C), extracting specific clauses or provisions from legal text, pulling obligation and deadline information from contracts, or mining competitive intelligence from news and analyst reports.

Relation extraction between entities
Contract clause & obligation extraction
Knowledge graph population
Custom Model Training & Fine-Tuning

Domain-specific model training and fine-tuning for organisations where off-the-shelf models underperform - legal, medical, financial, or technical language that general models misunderstand. Annotation tooling, labelling workflow design, active learning loops, and continuous improvement pipelines included.

Domain corpus annotation & labelling
BERT / RoBERTa fine-tuning on your data
Active learning & model improvement loop
How We Deliver It

From Text Problem to Production NLP Pipeline in 4 Phases

First working model in 3 weeks. We annotate a representative sample first - early accuracy benchmarks before any large-scale labelling investment.

Problem Definition & Data Audit

Define the NLP task precisely - what input text, what structured output, what accuracy threshold is acceptable for automation. Audit your existing labelled data or define the annotation scheme. Assess whether off-the-shelf models are sufficient or custom training is required.

⏱ Week 1
Annotation & Baseline Model

Annotation of a representative sample - using existing labels where available, or structured labelling workflows with domain experts where custom training is required. Baseline model trained on the initial sample with early accuracy benchmarks reviewed before full-scale labelling begins.

⏱ Weeks 1–2
Model Training & Validation

Full model training and hyperparameter optimisation - precision, recall, and F1 benchmarks per class and entity type. Error analysis reviewed with stakeholders: which categories the model confuses, which entities it misses, and whether confidence thresholds are correctly calibrated for your automation rate target.

⏱ Weeks 2–4
Production API & Monitoring

REST API deployment via FastAPI or Azure, integrated with your workflows or document management system. Prediction logging, confidence distribution monitoring, and a review queue for low-confidence predictions. Active learning loop that uses reviewed predictions to improve the model over time.

⏱ Weeks 4–6
Why Numlytics

Why Choose Numlytics for NLP Services

We've built NLP pipelines for financial services, legal, healthcare, and retail in the US, UK, and Australia - custom-trained on domain vocabulary, not generic off-the-shelf models.

Custom-Trained on Your Domain
Generic NLP models underperform on domain-specific text - legal clause language, medical terminology, financial product names, or industry-specific jargon. We fine-tune on your corpus and your annotation scheme, not a general-purpose dataset that misses the nuances of your industry.
Early Accuracy Benchmarks Before Commitment
We annotate a representative sample and build a baseline model early - before large-scale labelling investment is made. Early benchmarks confirm the task is tractable at the accuracy level you need. If the data isn't sufficient, we tell you before the engagement deepens.
Active Learning Loop for Continuous Improvement
We design models with an active learning loop, low-confidence predictions are queued for human review, reviewed labels are added to the training set, and the model retrains periodically. Models improve with usage rather than degrading as production data drifts from training data.
Production APIs That Fit Existing Workflows
Every NLP model we deploy ships with a production REST API built in FastAPI, hosted on Azure, and designed to integrate with your existing document management system, CRM, or processing workflow. NLP that processes documents in a separate UI nobody checks isn't NLP that gets used.
NLP + LLM Combination Where Needed
We design combined architectures NLP for structured extraction and classification (fast, cheap, accurate), LLM for the generative or complex reasoning tasks. Not every problem needs an LLM. We recommend the right tool per task, not the most fashionable one.
Up to 50% Lower Cost
Senior NLP engineers from India, same domain NLP quality as US or UK AI consulting firms at up to 50% lower cost. Full timezone overlap, daily standups, and Slack access throughout every engagement.
★★★★★

"We process around 4,000 insurance claim documents per month PDFs, scanned forms, and email attachments. Our team of six was spending roughly 60% of their time extracting the same 15 fields from each document: claimant name, policy number, incident date, loss type, estimated value, and so on. Numlytics built a document processing pipeline combining Azure Document Intelligence for OCR with a custom spaCy NER model trained on our claim vocabulary. After six weeks of training and refinement, the pipeline extracts all 15 fields with 94% accuracy. Documents below the confidence threshold go to a human review queue about 8% of volume. The team now handles 4× the document volume with the same headcount, and spends their time on claims that actually need human judgement."

PO
Patrick O.
Head of Claims Operations · Insurance Company, Australia

NLP sits alongside LLM integration and needs clean data pipelines to feed it reliably.

LLM Integration Services
Generative AI for the tasks that need language generation, not extraction
→
Predictive Analytics
Use NLP-extracted features as inputs to predictive models
→
MLOps Consulting
CI/CD and retraining infrastructure for production NLP models
→
ETL Pipeline Development
Feed structured NLP output into your data warehouse or BI layer
→
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FAQ

NLP Services FAQs

Common questions before starting an NLP services engagement with Numlytics.

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NLP (Natural Language Processing) services use machine learning to extract structured data from unstructured text - classifying documents, identifying named entities, analysing sentiment, and extracting specific fields from contracts, forms, or records. Unlike generative AI that creates new text, applied NLP transforms existing text into structured, actionable output your systems can act on automatically.
NLP services extract structure from text classifying, finding entities, extracting fields. LLMs generate new text in response to prompts. For most automation tasks, NLP is faster, cheaper, more accurate, and more predictable than an LLM. A text classifier trained on your categories outperforms a general-purpose LLM on your specific task at a fraction of the inference cost. See our LLM integration services for generative use cases.
For text classification with sufficient labelled data, 90–97% accuracy is typical. Named entity recognition on domain-specific text typically achieves 88–95% F1. Document extraction accuracy varies by document type. We build a baseline model early to confirm accuracy benchmarks before full-scale labelling investment, and agree the automation rate threshold above which predictions are applied without human review.
For text classification with BERT fine-tuning, 200–500 labelled examples per category is often sufficient. NER on domain-specific text typically requires 500–2,000 annotated sentences. We assess your existing labelled data first even inconsistently labelled historical data is usable as a starting point, and design the minimum viable annotation effort before recommending full-scale labelling.
Yes — NLP and LLM integration are complementary. A common architecture uses NLP for structured extraction and classification (fast, cheap, predictable) and an LLM for generative or reasoning tasks. For example: NLP extracts entities and classifies document type; the LLM summarises or answers questions about the classified document. This gives you NLP's accuracy and cost efficiency for structured tasks with LLM flexibility for open-ended ones. See our LLM integration services for the generative layer.
Ready to Start?

Turn Unstructured Text Into Structured Data, Automatically

Get custom NLP services text classification, named entity recognition, document processing, and sentiment analysis trained on your domain vocabulary, deployed as production APIs. First model in 3 weeks. US, UK, Australia & UAE.

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