Natural Language Processing (NLP) Development Services
Make use of NLP development services that enable your computers to recognize, understand, and generate human-like text and speech for better data analysis, text processing and content generation.
75
client locations
18
years in the IT market
500
employees
650
delivered projects
Notable NLP usage numbers
$439.85B
is the projected global NLP market size by 2030
80%
of corporate data is unstructured, and NLP is used to analyze it
72%
of companies already use at least one NLP solution
84%
of Fortune 500 companies use NLP for different purposes
30%–40%
reduction in manual tasks due to NLP automation
Our NLP app development services
Strategy and solution architecture
We help organizations identify a business-aligned NLP solution and create a step-by-step roadmap. For this, our natural language processing consulting specialists analyze your workflows, data sources, select necessary tools and technologies, as well as select pre-trained LLM or build custom models.
Custom NLP software development
We develop custom NLP solutions that understand, interpret, and generate human language by using domain-specific data. Our work includes designing and training NLP models from the ground up and building bespoke architectures by using transformer-based NLP models and modular components.
Pre-trained LLM development and fine-tuning
Our developers specialize in language modeling, fine-tuning LLMs like Claude, BERT, and GPT, as well as traditional models, and training them on labeled data. We use techniques such as transfer learning, LoRA, prompt engineering, and continual learning to adapt models to specific goals while achieving high accuracy on domain-specific tasks.
Security and regulatory compliance
SoftTeco ensures the security and regulatory compliance of NLP systems throughout their lifecycle, from development through to decommissioning. Our approach includes strict security controls, industry best practices, and alignment with leading standards such as the NIST AI Risk Management Framework, ISO/IEC 42001, and the EU AI Act.
MLOps, CI/CD, and production monitoring
As part of our NLP services, we automate the entire ML lifecycle using CI/CD pipelines for model building, testing, and deployment. Our developers utilize GitOps, MLflow, and canary releases to ensure safe rollouts, and tools like Prometheus and OpenTelemetry to monitor solution performance in production.
Support and maintenance
We offer support and maintenance for NLP systems to keep them scalable, optimized, and accurate as language, terminology, and context evolve. For this, we update models with new datasets, fine-tune parameters, and continuously evaluate performance, thereby reducing errors and ensuring reliable outputs.
Move beyond automation – understand user intent, emotion, and context with NLP solutions.
NLP software SoftTeco builds
01
Conversational AI and chatbots
By using ML and NLP, our conversational artificial intelligence systems and chatbots use natural language understanding to process, and respond to user queries in a human-like manner. They can accurately answer complex questions, reduce customer service costs, and drive user engagement.
02
Intelligent document processing
With built NLP solutions that help companies extract, classify, summarize, and process different types of data from documents such as PDFs, images, and emails. Document processing automation accelerates workflows, reduces manual labor, and enhances regulatory compliance.
03
Fraud detection systems
We deliver fraud detection systems that analyze user behavior,emails, transaction logs, and contracts, to detect fraudulent activities, including phishing attempts and scam messages. With them, businesses are able to strengthen security and establish lasting trust with customers.
04
Personalized recommendations
Personalized recommendation systems process textual data, such as user preferences, search queries, and social media posts, to identify patterns, predict user needs, and deliver highly tailored suggestions. With them, businesses can deliver more relevant products, content, and services.
05
Sentiment analysis
We implement NLP algorithms that help businesses analyze product reviews, customer feedback, and social media posts. They can identify customer friction points at every stage of the client journey and make strategic brand decisions.
06
Semantic search
Semantic search solutions based on NLP and semantic analysis help uncover a deeper understanding of the meaning and intent behind a search query. Users get more natural, context-specific, and human-like responses, improving content discoverability and user experience.
07
Intent classification solutions
Intent classification solutions help to accurately identify what a customer is trying to achieve during an interaction. Using them, businesses can provide the user with fast and relevant responses and improve personalization across channels.
08
Named entity recognition (NER)
Our named entity recognition solutions identify and classify specific entities, such as people’s names, organizations, locations, and dates, from unstructured text. With these models, companies can gain insights in customer data, reports, docs, and enhance decision-making.
09
Speech recognition
Our speech recognition solutions accurately identify, analyze, and convert human language into text and provide audio transcription for meetings, calls, and voice recordings. They help produce relevant, context-aware responses to people, enhance user experience and engagement when using smart voice assistants, like Siri and Alexa, or other apps.
10
Text analytics and categorization
Our solutions for text help understand, process, and categorize information for deeper analytics. They enable organizations to turn unstructured information into structured data, identify business insights, and improve the decision-making.
11
Language translation
Our natural language generation solutions take into account cultural differences, translation context, and language nuances. They make translation and localization processes easier, faster, and more accurate.
12
Content generation
Using AI-powered NLP solutions, companies can automatically create, summarize, and adapt texts to match specific brand voice and audience needs. With them, companies can analyze data faster, improve readability, and optimize for SEO, thereby making the text-creation process easier and more streamlined.
Our strategic technology partners
Challenges we solve during NLP development
Inaccurate training data
If you provide the system with incorrect, outdated, or biased data, it will either provide unreliable outputs or learn inefficiently. To ensure that your machine learning algorithms learn high-quality data in the present and over time, our specialists:
- Use cleansing and preprocessing techniques
- Continuously audit, validate and monitor training data
- Incorporate human review in labeling and annotation processes
- Regularly retrain models with refreshed data
Development time
To fully train models (especially if there are data issues that require debugging, and retraining) and develop an NLP system, you may spend more time than planned. To keep NLP solution design on the right track and reduce time-to-market, our experts:
- Leverage MLOps and CI/CD practices
- Use deep learning model and multiple GPUs infrastructure
- Utilize iterative training to detect issues as fast as possible
- Work with pre-trained models and transfer learning
False positives and uncertainty
In production, NLP systems may have false positives and uncertainty that leads to unreliable outputs and reduces trust in system results. To minimize false positives or negatives in models, our specialists:
- Fine-tune models on domain-specific datasets
- Apply probability calibration and encoder models
- Select the most suitable model for a specific task
- Enhance the quality of collected data
Resource requirements
Data storage and processing, along with training and deploying models require significant computational power, memory, and scalable infrastructure, which makes NLP development costly and complex. To mitigate infrastructure constraints and optimize resource utilization, we:
- Use cloud infrastructure and distributed data pipelines
- Leverage data compression and preprocessing strategies
- Fine-tune pre-trained models where possible
- Optimize model architecture for efficiency
Language differences
NLP systems often struggle with multilingual data, regional dialects, slang, and context-specific meanings. To ensure NLP models perform as expected across a range of languages and regions, our experts:
- Train models on domain-specific datasets
- Fine-tune or adapt models for each language
- Use translation models and cross-lingual embeddings
- Integrate language detection and routing systems
SoftTeco’s NLP projects
Diagnostic and configuration platform modernization
SoftTeco modernized the centralized diagnostic and configuration platform for Jaguar and Land Rover vehicles. For this solution, we designed an AI chatbot assistant that helps to allocate the correct repair procedures, wiring diagrams, and diagnostic codes. Powered by natural language processing models, the bot reduces search time and improves user efficiency. We also added an intelligent search engine that allows users to find docs not only by keyword but also by semantic meaning.
SoftTeco developed an AI-powered chatbot for enhanced customer service on a website. The bot provides 24/7 support, answers user inquiries, and helps customers quickly find relevant information. Powered by large language models and a RAG, it processes natural language queries and generates accurate responses based on internal company data. Additionally, our team also integrated multilingual support, allowing the bot to understand and respond in multiple languages.
SoftTeco created an AI-powered hotel concierge for Cisco Webex Desk Pro devices. The solution utilizes large language models to understand and generate natural-language responses within a conversational interface. It is supported by a RAG system with a vector database to find relevant information and enhance response accuracy. The bot also uses Whisper for speech-to-text and NLP tools to understand user intent and respond correctly.
Industries we work with
We, at SoftTeco, provide NLP solutions across a wide range of industries and domains, including but not limited to:
Healthcare
We help healthcare settings to reduce process friction by automatically extracting key data from medical records, summarize clinical notes for improved decision-making support and reduce administrative burden.
- Clinical documentation automation
- Patient sentiment and feedback analysis
- Clinical decision support
- Information extraction from medical records
Finance
We help financial organizations automatically analyze and categorize overwhelming volumes of financial documents, detect and quantify sentiment in text, and strengthen compliance.
- Financial document summarization
- Risk assessment and management
- Keyword extraction for compliance and reporting
- Portfolio management
Telecom
Telecom companies can rely on us to analyze massive streams of customer interactions, automate customer responses, detect emotions, and provide personalized support.
- Request categorization using voice recognition
- Speech analytics
- Sentiment analysis and topic modeling
- Personalized content and communication
Manufacturing
In manufacturing, NLP is used to analyze textual data, automate human-machine communication, extract actionable insights from customer feedback, and analyze maintenance logs for predictive maintenance.
- Incident and safety reports сategorization
- Specific entities identification
- Supply chain optimization
- Quality control
Logistics
With our built NLP solutions, businesses can gain actionable insights from order details, shipping updates, and supplier communications, automate document processing and improve supply chain management.
- Supplier communication and manage relationships optimization
- Demand and manage inventory levels forecasting via text clustering
- Real-time monitoring and alerting
- Voice-activated warehouse operations
Oil and gas
Oil and gas companies use NLP solutions to extract insights from reports and docs, analyze drilling and production logs, process maintenance and inspection records, and organize and classify unstructured data.
- Semantic search
- Incident and risk report analysis
- Document summarization
- The provision of operational insights
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Benefits of custom NLP applications
Improved customer service
NLP powers chatbots and virtual agents can handle routine questions at any time of day without making customers wait. This leads to smoother experiences across every touchpoint while maintaining the best-ever customer service, with retention rates up to 20–35%.
Reduced manual processing
NLP apps automate repetitive tasks like extracting information from documents, categorizing entities, and processing customer feedback. By leveraging them, companies can reduce manual processing volumes by 41%, the workload on employees, and minimize human errors.
Document value extraction
From 80% to 90% of all enterprise data is unstructured. NLP solutions can automatically extract relevant information from unstructured text and convert it into a structured format. This makes analysis easy, reduces the time and effort required for manual data entry and review.
Better data analysis
NLP algorithms analyze vast amounts of customer reviews, social media posts, and support queries fast. With text mining techniques, NLP can identify patterns, trends, customer sentiments, and areas for improvement that are not immediately obvious in large datasets.
Task automation
NLP technologies facilitate the generation of reports, and extraction of vast volumes of data, without human intervention. NLP-powered chatbots can handle numerous routine tasks that often overburden employees, with 42% of organizations using NLP for document automation.
Enhanced search
NLP can improve keyword searches by resolving word ambiguity based on context, comparing synonyms, and accounting for morphological variants. As a result, search systems become more reliable and context-aware.
Our awards and recognitions
Our NLP software development roadmap
To ensure effective workflows, meet deadlines, and remain on budget during NLP development, we follow a transparent and thorough roadmap.
Step 1. Gather project requirements
First of all, we define NLP objectives, use cases (text classification, summarization, chatbot, search, etc.), select appropriate algorithms and techniques. During this stage, our specialists are responsible for planning system architecture, establishing compliance, and meeting project requirements.
Step 2. Collect and prepare data
We collect text data from various sources, such as social media, documents, and perform text annotation and preprocessing to prepare datasets for language model training. Then, we analyze the dataset to understand language patterns, data quality, class distribution, and potential biases for optimal modeling strategy.
Step 3. Extract features and representation
Our specialists convert preprocessed text into a numerical format understandable to ML models. For this, we use advanced methods, such as transforming words and text segments into vector representations (embeddings).
Step 4. Select and train a model
We choose the most suitable NLP model based on the task you want to perform and then train it on your prepared datasets. Our experts also perform hyperparameter tuning to optimize model performance and accuracy.
Step 5. Evaluate and deploy a model
Our experts evaluate the NLP model for accuracy, precision, and completeness, and verify its effectiveness when processing new data. After that, we deploy the model into production to process and analyze text data under real-world operating conditions.
Step 6. Monitor and update systems
Once NLP solutions are deployed, we periodically update models with new data and retrain them. Furthermore, we identify areas for improvement and maintain the accuracy and relevance of their NLP systems.
Our tech stack for NLP development
NLP & ML frameworks
Python, PyTorch, TensorFlow, spaCy, Hugging Face Transformers, NLTK, Gensim, Flair, scikit-learn
LLM workflows & orchestration
LangChain, LlamaIndex, Haystack
Speech & language APIs
OpenAI, Azure Cognitive Services, Google Cloud Natural Language, Amazon Comprehend, Whisper, Twilio
Vector databases & search
FAISS, Weaviate, Pinecone, Elasticsearch
Data annotation & labeling
Prodigy, Label Studio
Data processing
pandas, Apache Spark, Dask
Model serving & APIs
FastAPI, ONNX, TensorFlow Serving, Docker, Kubernetes
Cloud & MLOps
AWS SageMaker, Azure ML, Google Cloud, Kubernetes, Docker
Development tools
Jupyter Notebook, PyCharm, VS Code
Visualization
matplotlib, seaborn, Plotly
RAG & embeddings
OpenAI Embeddings, Sentence Transformers, Hugging Face Embeddings, FAISS, Pinecone, Weaviate
Evaluation & monitoring
MLflow, Weights & Biases, Evidently AI, LangSmith
Databases
PostgreSQL, MongoDB, Redis, S3-compatible storage
