Text classification and routing
Text classification models that tag tickets, emails or documents by topic, urgency or intent, sending each item to the right queue without someone reading it first.
Machine Learning & Data
Natural language processing services that make text searchable, sortable, useful.
Our natural language processing services help you pull structure and meaning out of the emails, contracts, tickets and reviews piling up across your business. We combine classic NLP, fine-tuned transformers and LLMs, picking whichever is most accurate and affordable for each task.
- Classical NLP plus LLMs
- Domain-tuned language models
- Multilingual support
01 Overview
Turning unstructured language into data you can act on
Most organizations hold far more information in free text than in their databases, yet it rarely feeds reporting or automation because nobody can read it all. A support queue hides recurring product defects, contracts bury renewal terms, and survey comments contain the reasons behind a falling score. The challenge is not just reading the text but doing it consistently, with domain vocabulary, abbreviations and the occasional typo-ridden message handled correctly.
As an NLP development company, we scope each problem by the decision it supports, then choose the lightest tool that meets the accuracy bar. Text classification and named entity recognition often run fastest and cheapest as fine-tuned compact transformers or spaCy pipelines. Summarization and open-ended reasoning may justify an LLM. Our natural language processing services include building labeled evaluation sets, so you can compare approaches on your own documents rather than on a public benchmark that looks nothing like them.
02 Capabilities
Natural Language Processing capabilities.
End-to-end Natural Language Processing: strategy, engineering and the production operations that keep it working.
Entity and relation extraction
Named entity recognition for people, products, amounts, dates and domain-specific terms, plus relation extraction that links them into records your systems can store.
Document information extraction
Document information extraction from contracts, invoices, medical notes and reports, combining layout-aware models with validation rules so extracted fields are trustworthy.
Sentiment and opinion mining
Sentiment analysis solutions that go beyond positive or negative, identifying which product aspect a customer is praising or complaining about across reviews and survey responses.
Summarization and semantic search
Concise summaries of long threads, calls and reports, plus embedding-based search that finds passages by meaning rather than exact keyword matches.
Multilingual language processing
Multilingual NLP using cross-lingual transformer models and translation layers, so one pipeline can serve customers and documents in many languages consistently.
03 How we deliver
From first call to production.
Every Natural Language Processing engagement follows the same five stages, so you see value early and risk stays low.
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01
Discover Typically 1–2 weeks
AI readiness assessment, data audit and use-case discovery. We rank opportunities by business value and feasibility and agree on measurable success criteria.
Output: Roadmap · data audit · success metrics
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02
Prototype Typically 2–4 weeks
A working proof of concept on your real data, scored against an evaluation set — so you see accuracy, latency and cost before committing to a full build.
Output: PoC · evaluation report · build estimate
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03
Build Sprint-based
Production engineering: data pipelines, model training or fine-tuning, RAG retrieval, agent tooling, APIs, guardrails and the user interface your team will actually use.
Output: Production code · APIs · guardrails
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04
Deploy Cloud, on-prem or edge
Secure deployment on AWS, Azure, Google Cloud, your private infrastructure or edge devices, with CI/CD, LLMOps and MLOps pipelines from the first release.
Output: CI/CD · infrastructure as code · rollback
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05
Operate Ongoing
Monitoring, drift detection, retraining and cost optimization keep models accurate and affordable as your data, users and the AI landscape change.
Output: Monitoring · retraining · cost reports
04 Use cases
Where it pays off.
Practical, high-value applications we design and build for clients across industries.
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UC-01
Support ticket triage
Classify incoming tickets by product, issue type and priority, auto-assign them to the right team, and surface emerging issue clusters before they become escalations.
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UC-02
Contract clause review
Extract parties, renewal dates, liability caps and non-standard clauses from contracts, giving legal and procurement teams a searchable register instead of a folder of PDFs.
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UC-03
Voice of customer analysis
Mine reviews, social posts and survey comments for recurring themes and aspect-level sentiment, linking customer language directly to product and service decisions.
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UC-04
Clinical and claims text processing
Extract diagnoses, procedures and key facts from clinical notes or claim narratives, in pipelines designed around HIPAA requirements for access control and data handling.
05 Technology
The stack we use.
Proven, well-supported tools chosen per project for accuracy, cost and long-term maintainability.
NLP libraries
- spaCy
- Hugging Face Transformers
- NLTK
- Sentence Transformers
Models
- BERT
- RoBERTa
- DeBERTa
- Llama
- OpenAI GPT models
Search and serving
- Elasticsearch
- OpenSearch
- pgvector
- FastAPI
- ONNX Runtime
06 Industries
Built for your sector.
We deliver Natural Language Processing for regulated and fast-moving industries alike.
What are natural language processing services used for?
Common uses include routing support tickets, extracting data from documents, analyzing customer sentiment, summarizing long text, powering semantic search and detecting compliance risks in communications. The common thread is turning free text into structured information or actions. We focus on tasks where automating the reading step saves real time or reveals patterns people would otherwise miss.
Is NLP still relevant now that large language models exist?
Very much. LLMs are a powerful part of modern NLP, but for high-volume, well-defined tasks like classification or entity extraction, a fine-tuned compact model is often faster, cheaper and more predictable. We frequently combine them, using an LLM to bootstrap labels or handle rare cases while a smaller model processes the bulk of the traffic.
Can you build NLP for industry-specific terminology?
Yes, and that is where custom work pays off. We fine-tune models on your documents, add domain vocabularies and rules for abbreviations, and build evaluation sets with your subject experts. Medical, legal, financial and technical text all benefit from this, because general-purpose models tend to misread jargon that your staff consider obvious.
Do you support languages other than English?
We do. Multilingual transformer models handle many languages in a single pipeline, and we can add language detection and translation where useful. Accuracy varies by language and by how much training data exists, so we evaluate each priority language separately with native-speaker review rather than assuming English performance carries over.
How is sensitive text data protected during an NLP project?
We can run models entirely inside your cloud or on-premises environment so text never leaves your control. Pipelines can redact personal data before processing, enforce role-based access and log every request. Designs are built around GDPR and HIPAA requirements where they apply, and we avoid sending regulated data to third-party APIs without your explicit approval.