Natural Language Processing
Services
Turn unstructured language into structured information your applications and teams can use.
Our NLP development services help organizations classify text, detect intent, extract entities and business fields, analyze sentiment, process multilingual content and automate language-heavy workflows.
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Recognized for
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AI engineering experience across language processing, machine learning, document intelligence and enterprise applications.
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Natural Language Processing
Services We Deliver
Build NLP systems that understand, classify and extract useful information from human language.
Categorize
Text Classification
Automatically classify emails, documents, support requests and other text into defined business categories.
Common applications include email routing, ticket categorization, document classification, issue detection and content tagging.
Purpose
Intent Classification
Identify what a customer, employee or user is trying to accomplish from written or transcribed language.
Intent classification can support enquiries, service requests, call intake, support triage and workflow routing.
Entities
Named Entity Recognition
Identify and extract meaningful entities such as people, organizations, locations, dates, products, reference numbers and domain-specific terms.
Fields
Information Extraction
Transform unstructured language into structured fields that downstream applications can process.
Opinion
Sentiment Analysis
Analyze reviews, feedback, surveys and support interactions to identify sentiment and recurring opinion patterns.
Languages
Multilingual NLP
Process classification, extraction and language-understanding tasks across supported languages.
For generative language applications, model integration and fine tuning, explore our LLM Development Services.
NLP Solutions
We Build
The systems organizations most often need when language volume outgrows the people reading it.
Inbox
Email Classification Systems
Interpret incoming emails and classify them by intent, department, urgency or topic before routing them into the appropriate workflow.
Service
Support Ticket Routing
Analyze requests and automatically assign them to the correct support queue, workflow or business function.
Documents
Document Classification
Identify document types before extraction, validation or downstream processing begins.
Extraction
Entity Extraction Systems
Extract names, dates, references, products, codes and business-specific information from free-form text.
Insight
Feedback Analytics
Analyze large volumes of reviews, surveys, complaints and open-ended customer feedback.
Policy
Content Moderation
Classify text according to predefined moderation categories and business policies.
What NLP
Can Improve
Where language processing changes how work actually moves through the business.
Volume
Faster Text Processing
Reduce the manual effort required to read, classify and process large volumes of written information.
Routing
Better Request Routing
Route messages and requests according to meaning instead of relying only on fixed keywords.
Structure
Structured Information
Turn free-form text into categories, entities and fields that business systems can use.
Consistency
Consistent Classification
Apply the same classification logic across high-volume workflows.
Insight
Customer Insight
Identify patterns across reviews, complaints, surveys and support interactions.
Global
Multilingual Operations
Support language-heavy workflows across international users and markets.
NLP
Architecture
A production NLP system turns raw language into structured information that business applications can use.
Stage 01
Text Input
Process information from sources such as:
Stage 02
Language Processing
Apply the appropriate model for classification, extraction or language understanding.
Stage 03
Structured Output
Return useful results such as:
Stage 04
Validation
Apply business rules and confidence thresholds before information moves downstream.
Stage 05
Application Integration
Send approved NLP results to CRM, ERP, support, document or custom business applications.
NLP Models
and Technology
Not every language problem requires a large language model. We select the approach according to the task, available data and required level of accuracy.
Deterministic
Rule-Based NLP
Useful for workflows with highly deterministic terminology and well-defined language patterns.
Supervised
Machine Learning Models
Suitable for many classification and extraction tasks when representative labeled data is available.
Contextual
Transformer Models
Use contextual transformer models for more complex language-understanding requirements.
Adapted
Pretrained NLP Models
Adapt established models for classification, entity recognition and domain-specific tasks.
Generative
LLM-Assisted NLP
Use language models when they provide a measurable advantage for the specific language-processing task.
For deeper model engineering, explore our LLM Development Services.
NLP
Evaluation
NLP systems should be measured against the task they actually perform.
Accuracy
Classification Accuracy
Measure whether text is assigned to the correct category.
Balance
Precision and Recall
Track false positives and missed examples for important classes.
Combined
F1 Score
Balance precision and recall when both are important.
Entities
Entity Accuracy
Measure whether required entities are identified correctly.
Fields
Field Accuracy
Validate extracted business values against expected results.
Per Language
Multilingual Performance
Evaluate supported languages individually instead of relying on one overall score.
Thresholds
Confidence Thresholds
Use model confidence to determine when automatic processing is appropriate and when human review is required.
NLP Solutions
by Industry
Where language volume and language complexity are highest.
Finance
Financial Services
Classify correspondence, extract structured information and analyze customer communications.
Health
Healthcare
Process administrative text, forms and operational language workflows.
Legal
Legal
Classify documents and extract entities, references and structured information from legal text.
Logistics
Logistics
Extract shipment references, dates, parties and operational details from documents and correspondence.
Commerce
Retail and E-Commerce
Analyze customer enquiries, reviews, product feedback and support messages.
Service
Customer Service
Classify requests, detect intent and improve support routing.
NLP
Technology Stack
Chosen for the language task, not for the largest available model.
Where the language work happens
Training and inference
Matched to the task
How the capability is exposed
Where it runs
NLP Systems
in Production
Real implementations showing language understanding, classification and information extraction inside operational workflows.
Pulastya AI Call Intake
A service business needed incoming calls understood according to what callers actually said instead of forcing people through fixed phone menus.
Pulastya interprets natural conversation and maps each call to a defined business intent before routing logic determines the appropriate workflow.
Typical intents can include booking requests, support enquiries, pricing questions, service requests and department-specific enquiries.
ConversationTranscriptIntent ClassificationBusiness IntentRouting
- Natural-language understanding
- Intent classification
- Structured request capture
- Transcript processing
- Caller-context extraction
- Defined intent taxonomy
Pulastya uses NLP to identify caller intent before routing each request into the appropriate workflow.
Data AI Ninja
Data AI Ninja converts uploaded business documents into structured information.
The system identifies the document type, applies the appropriate extraction workflow and returns structured fields with confidence information for review.
Supported workflows can include invoices, receipts, contracts, statements and other business documents.
DocumentClassificationField ExtractionStructured OutputConfidence Review
- Document classification
- Information extraction
- Field detection
- Confidence scoring
- Reviewer validation
- Structured JSON output
Global Logistics
A logistics operation needed to extract important information from invoices and bills of lading arriving in different formats.
The document-processing layer identifies required business fields and converts unstructured document content into structured information for validation and downstream processing.
Depending on the document, extracted information can include invoice details, shipment references, vendor information, dates, amounts and operational fields.
DocumentText ProcessingField DetectionStructured DataValidation
- Invoice details
- Shipment references
- Vendor information
- Dates
- Amounts
- Operational fields
Featured image from the Transworld document-processing case study.
What Clients Say
About SDLC Corp
Founders, CEOs, and operating leaders share what it's like to build with SDLC Corp.
Eric Leist
CEO, Edgerton Strategies
Doug Schmidt
CEO, Roofaid USA
Reyzal Razmi
All Star Influencers
They approached our Salesforce discovery with real technical depth, uncovered structural gaps others missed, and delivered a solution that worked exactly as promised.
SDLC CORP built a mobile application that met our strategic requirements with strong technical execution. The solution performs reliably and has become an important operational asset.
They saw inefficiencies in our Salesforce workflow and redesigned our entire quote-to-cash system. We now operate faster, cleaner, and with better accuracy.
From planning to post-launch, SDLC Corp guided us every step of the way. Their support makes them more than a vendor. They're a trusted partner.
The SDLC Corp team scaled our platform with impressive technical expertise, ensuring it's secure, robust, and ready for future growth.
The SDLC Corp team scaled our platform with impressive technical expertise, ensuring it's secure, robust, and ready for future growth.
From planning to post-launch, SDLC Corp guided us every step of the way. Their support makes them more than a vendor. They're a trusted partner.
They saw inefficiencies in our Salesforce workflow and redesigned our entire quote-to-cash system. We now operate faster, cleaner, and with better accuracy.
SDLC CORP built a mobile application that met our strategic requirements with strong technical execution. The solution performs reliably and has become an important operational asset.
They approached our Salesforce discovery with real technical depth, uncovered structural gaps others missed, and delivered a solution that worked exactly as promised.
AI Engineering
at Scale.
Language systems built by teams that also build the applications they feed.
Our NLP
Development Process
Six stages, from defining the categories to watching the language change.
Discover
Define the language sources, business task, required categories or entities, supported languages, downstream workflow and success criteria.
Prepare
Organize representative language data and create labels where supervised learning is required.
Build
Develop or adapt the NLP model and processing pipeline.
Evaluate
Test performance against representative unseen examples.
Integrate
Connect NLP capabilities to applications and workflows through APIs or application services.
Deploy
Release the NLP system into the required production environment and monitor performance.
Why Choose SDLC Corp
for NLP
NLP judged by the task it performs, not by how impressive the model sounds.
Right-Sized
Language and ML Engineering
Use the appropriate NLP and machine-learning approach for the problem rather than applying the same model architecture everywhere.
Connected
Enterprise Workflow Experience
Connect NLP output with real operational systems and business processes.
Usable
Structured Business Outputs
Turn language into categories, entities and fields that applications can act on.
Oversight
Human Review Where Needed
Use confidence thresholds and exception handling when language is ambiguous or business impact is higher.
Per Language
Multilingual Design
Evaluate target languages individually for production use.
Measured
Production Evaluation
Measure NLP quality against representative business scenarios.
NLP
Resources
Background reading on the layers around language processing.
OCROCR and Text Processing
Learn how text can be extracted from scanned documents before downstream language processing.
Read the OCR Guide
IndustryNLP in Publishing
Explore language-processing applications including categorization, analysis and text intelligence.
Read the Publishing AI Guide
ModelsLarge Language Models
Understand how modern language models fit within the broader Natural Language Processing landscape.
Read the LLM GuideBuild Your
NLP Solution
Turn emails, documents, feedback, messages and other unstructured language into structured information your software and teams can use.
From text classification and intent recognition to entity extraction, sentiment analysis and multilingual NLP, our team can take your project from data preparation through production deployment.
Contact Our
NLP Team
Tell us what language data you need to process, which categories or entities matter, which languages are involved and where the results need to go.
Contact Us
Share a few details about your project, and we’ll get back to you soon.
Let's Talk About Your Project
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- 24/7 Experts Support
- On-Time Delivery
- sales@sdlccorp.com
- +1(510-630-6507)