NLP Consulting: Custom Natural Language Processing Solutions for the Messiest of Text Data Problems

As NLP consultants with 20+ years of experience, we partner with businesses globally to build robust, scalable, end-to-end NLP solutions for complex language and text data challenges—from chatbots to data extraction for insights.

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Custom Natural Language Processing Solutions We Help With

Select NLP development problems our NLP consultants help with

Predictive Analytics

Predict future outcomes based on historical data using artificial intelligence and machine learning techniques. Predict fraud events, machine failures, customer churn, and more by leveraging your own data. Decrease human errors, increase efficiency, and boost employee productivity. Linear regression models to Deep Neural Networks.

LLM Fine-Tuning


Find the best prompts and get fine-tuned models for your generative AI and LLM tasks. We craft prompts, fine-tune LLMs with additional data, and evaluate models against ground truth data for optimal accuracy. GPT-4, Cohere, Claude & more.

Information Extraction

Get highly targeted information extracted from PDFs, raw unstructured text data, word documents, and more using advanced text mining and machine learning techniques. Reduce the amount of manual work needed to extract pertinent information and make your employees 10x more efficient.

Text Classification

Get custom NLP text classifiers for sentiment analysis, email organization, spam detection, ticket routing, and others. From applications with little data to high volumes. BERT, BioBERT, Flan-T5, GPT-4, BART, and more.

Recommendation Systems

Get hyper-personalized and customized recommendations for content, products, people, and more. Improve content discovery, boost revenues, and increase platform stickiness. From content-based filtering and collaborative filtering to LLM-based recommenders.

Entity Recognition

Automate the recognition of key data elements from large amounts of unstructured text data—such as names, organizations, locations, and dates to streamline data processing, enhance search functionality, and gain deeper insights.

Voice of Customer Analysis

We connect the dots and analyze customer feedback across surveys, reviews, support tickets, and social media to uncover key insights into needs, preferences, and pain points, driving actionable business decisions.

LLM Search

Enable employees and customers to quickly find organization-specific documents and information they need with high precision using expressive natural language queries.

Custom AI Chatbots

Get customized AI chatbots that can answer organization-specific questions, respond in natural language, and complete various task requests, all from a single user interface. Think: highly specialized co-pilot without compromising data and user privacy.

High-Performance Document Search, Discovery, and Organization

Get high-performance and customized search systems for your company’s massive unstructured data sources. From PowerPoint to legal documents, from social media comments to employee feedback data, all from a single search interface.

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Example Solutions We’ve Developed

Examples of NLP development work we’ve done as NLP consultants for different clients. These natural language processing solutions are highly customized and developed from scratch to cater to the client’s unique needs, business challenges, and infrastructure constraints.

Information Extraction - NLP Consulting Services

Customizable information extraction engines for clinical notes, claims data, medical literature, and more.

ai chat assistant - NLP Consultant

Privacy-protecting, on-premise AI chat assistant for tasks such as question answering, document search, and more.

High–efficiency recommendation engines for products, people, content, jobs, and job candidates.

High–accuracy categorization engines for tickets, spam content, cyber-attacks, and more.

In Our Customer’s Own Words…

Select NLP consulting testimonials.

  • Working with Opinosis Analytics has been a highly positive experience. Their collaborative approach, combined with a strategic mindset, ensured that we were aligned every step of the way. Opinosis took the time to understand our unique business challenges, delivering tailored AI solutions that met our needs and helped us mitigate risks. Their open communication, reliability, and professionalism were exceptional throughout the project. We’re extremely satisfied with the outcome and would absolutely recommend Opinosis Analytics to others.
    Annie Quan
    Centeva (Federal Contractor)
  • Dr. Ganesan’s knowledge of Natural Language Processing (NLP) and text mining methodologies and their application to solve real-world problems is very impressive. She is able to understand the business goals and then apply and recommend the algorithms that would be most effective. She is also able to plan and estimate her work very well, which is very helpful and reassuring, especially when one is exploring an unfamiliar territory. Her work is of very high quality. Her communication skills are excellent and she is able to explain and support her work and her recommendations very well.
    Anupam Singh
    113 Industries (VoC & Market Research)
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Common Industries We’ve Served

Some of the industries we’ve served with our Natural Language Processing, Text Analysis, LLM, and Machine Learning consulting work

Healthcare

NLP & Machine Learning in Healthcare

We’ve worked with hospitals, vendors serving clinics, hospitals and big pharma to build models that extract, redact, and find specific information from highly unstructured data, intelligently integrate disparate data, and extract insights.

Manufacturing

NLP in Manufacturing

We’ve worked with CPG and parts manufacturing companies to improve their AI deployment strategy, better evaluate models, and perform online evaluations.

Government

NLP in Government Services

We’ve worked on NSF grants and civilian agency AI problems to optimize government operations and conduct cutting-edge research on specific topics.

Technology

NLP in SaaS, Internet & Technology

We’ve worked with e-commerce companies, SAAS companies, and software development companies to develop highly customized categorization engines, recommendation systems, search systems, chatbots, and more.

Energy

NLP & Machine Learning in the Energy Sector

We’ve partnered with energy companies to optimize operations by enhancing proposal writing, streamlining energy transactions, improving structural code analysis, and developing advanced search and Q&A systems.

Legal

NLP in Legal

We’ve worked with legal startups, and directly with attorneys to better classify documents, extract pertinent information, and summarize large documents.

Marketing

NLP in Marketing

We’ve worked with marketing agencies and market research companies to augment their workflow with AI.

Custom NLP Consulting for Any Industry

While these are examples of industries we’ve worked with, we collaborate with subject matter experts within each industry. We are comfortable working with others across various industries, including finance, real estate, education, and more.

Bring Order and Meaning to Your Text Data with Our Custom NLP Solutions

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Frequently
Asked
Questions

Common questions about our Natural Language Processing consulting services

Natural language processing, often referred to as NLP, is about helping computers understand human language. While this was the original academic definition, in the industry,  NLP is used to refer to any method that deals with the processing, analysis, and retrieval of any textual data—it doesn’t have to be spoken language.

Due to the messy, scattered, and unstructured nature of text data, it takes a wealth of NLP knowledge and the right thought process to get value from NLP initiatives. With our extensive experience in the field, years of teaching NLP to practitioners around the world, and helping clients with text data problems, we’ve mastered what it takes to get results from NLP projects.

From providing guidance around building text classification models to helping companies generate data for their NLP solutions, we’ve solved all types of problems for our clients.

Get in touch to see how we can help with NLP consulting.

NLP is a broad field within AI encompassing the study and development of algorithms for processing natural language.

LLM, or Large Language Models, is a type of NLP application that can be used for tasks like text generation and specific task completion, such as categorization and summarization. LLMs are trained on vast amounts of data, such as Web data, book corpus, Wikipedia, etc., resulting in powerful models that can be used for various language-related tasks. GPT-3, Cohere, and PaLM are examples of LLMs for commercial use cases.

Bottom line: LLMs are a subset of tools within the broader domain of NLP.

As an NLP services company, the LLM tools we use are highly task-dependent. But for previous clients, we’ve used tools such as:

  • BERT
  • BioBERT
  • Flan-T5
  • LLAMA
  • GPT 4/3.5/o

An NLP expert (known as NLP consultant or NLP developer) working for an NLP services company or any AI consulting company is a highly trained individual who solves large-scale language and text data-related automation problems. They typically have a computational degree, a strong background in data science, and experience working on language-related projects.

An NLP consultant can work with your company in several ways. First, they will provide strategic guidance around your NLP projects. For example, at a strategic level, we help many clients frame NLP projects such that the requirements are crystal clear. In addition to clarity around requirements, we also help our clients better understand the types of data needed for a project, help them through data acquisition, and help prepare clients for the implementation phase. This is a critical step in our engagement as it can make or break a project. At a strategic level, we also guide our clients fully through implementation, which involves solution design, development, and testing.

NLP consultants will also implement the whole solution for your organization if you choose a done-for-you engagement. The consultant starts with requirements clarification, data gathering, and formatting and will then design, develop, and test the solution until the completion of the project. Once the solution is of acceptable quality, the NLP consultant will transition the solution over to the client. In our engagement, we go an extra step, where we have clients test the solution with their users and help iteratively improve the solution. Further, we also provide advisory services post-implementation to ensure that the NLP solution is fully integrated with your business systems.

The combination of tools we use for our Natural Language Processing consultancy projects often differs by project, but here’s a list of common ones:

  • Programming language: Python
  • NLP processing: spaCy, NLTK
  • Deep learning: Pytorch, TensorFlow
  • SLM: BERT, RoBERTa, XLNET, Flan-T5.
  • LLM: LLAMA, ChatGPT (GPT 3.5, 4, 4o)
  • Retrieval: ElasticSearch, Pinecone
  • Productionization platforms: Azure, AWS

Through our NLP consultancy, we’ve helped with various NLP projects over the years, but here are some highlights:

-Text classification of patent applications for a legal tech

-Sentiment analysis for consumer market research

-Sentiment analysis for patient experience transformation

-Chatbot for document search & retrieval for government use cases

-Produce recommendation system for an online grocery startup

-Candidate and job matching system for an HR software startup

-Speech recognition optimization and cost reduction through open-source libraries

Absolutely not. It all depends on the problem at hand. There are many ways to realize the power of large language models. You can use it by prompting it to complete tasks or it can be used behind the scenes to help with downstream tasks within other NLP methods. Depending on the problem, you may not need LLMs at all. For example, surface-level keyword searches may not require LLMs behind the scenes. Certain recommendation systems can easily get away with traditional machine learning methods without any LLMs.

Bottom line: If the task at hand is relatively simple and doesn’t involve intricate language understanding, a smaller model or alternative approach might be more appropriate than a large language model. However, for complex NLP tasks, especially in diverse and large-scale datasets, leveraging the capabilities of an LLM can be helpful.

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