Natural language processing services build software that reads and understands human language, such as emails, documents, reviews and chat messages, so it can classify them, extract key details, measure sentiment and summarise them automatically.
Emails, tickets, contracts, reviews and chat logs hold valuable insight that is hard to analyse manually. Our NLP solutions read, classify, summarise and extract information from text at scale, so your teams can respond faster and make better decisions.
Turn unstructured text into actionable insights with AI-driven language understanding. At Prominent AI Services LLP, we deliver natural language processing end to end, from discovery and data preparation to deployment, monitoring and support, so your teams get dependable results quickly and securely.
Turn unstructured text into actionable insights with AI-driven language understanding.
Ideal for: Customer Support, Legal, HR, E-commerce. We validate each idea with a measurable baseline, iterate on features and model architecture, and harden the winning model for scale with automated testing, versioning and performance monitoring.
We use transformer models such as BERT and its multilingual variants for classification and extraction, large language models for summarisation and question answering, and spaCy for fast entity recognition. Models are fine-tuned on your own labelled examples when needed (see model fine-tuning and training data services). We build in Python with Hugging Face and PyTorch, and deploy on Azure or AWS.
Choose a fixed-scope project, or hire machine learning developers and Python developers to join your team. For chat-based solutions, see conversational AI integration; for voice, see speech and audio AI.
Drowning in documents or messages? Send us a few examples, and we will show what NLP can extract or automate. Start your AI journey.
Common questions about our Natural Language Processing services.
Yes. We use multilingual models and can fine-tune them for Hindi, Gujarati and other regional languages alongside English.
Yes. Named entity recognition and document classification can pull parties, dates, amounts and clauses into structured data automatically.
We can deploy models in your own cloud or on-premise environment so sensitive text never leaves your infrastructure.
Cost depends on the number of document types, the fields to extract and the accuracy required. A proof of concept on one document type is a small, fixed-price project; production systems are quoted after it.
A proof of concept usually takes three to six weeks. A production solution with integrations and monitoring typically takes two to three months.
Accuracy depends on the task and the quality of the documents. We measure it on a held-out set of your own examples and agree a target before launch. Uncertain results can be sent to a person for review.
Not always. Large language models can often handle a task with only a few examples. For high accuracy on specialised documents, we help you label a few hundred examples to fine-tune a model.
