Language understanding and dialogue systems that work across every channel.
Natural language processing (NLP) is how software reads, classifies and responds to human language. We build the underlying capabilities — intent and entity recognition, extraction, dialogue state, multilingual handling — so the same conversation logic can power chat, voice, email and internal tools.
- Language task design
- Classification and extraction models
- Dialogue management layer
- Multilingual support
- Text analytics outputs
Where this helps
What we deliver
How it works
- 01
Sample the language
We collect representative text or transcripts and review how people actually phrase requests, including slang, typos and mixed languages.
- 02
Define the schema
Intents, entities and labels are defined with examples, then tested for how consistently people can apply them.
- 03
Build and compare
We compare prompted LLMs, smaller trained classifiers and hybrid approaches on accuracy, speed and cost.
- 04
Integrate with channels
The language layer is exposed as a service that chat, voice, email and back-office systems can call.
- 05
Monitor and relabel
Low-confidence and misclassified cases are reviewed and fed back to keep accuracy up as language shifts.
Design decisions we make with you
LLM or trained classifier
Large models handle open-ended language with little setup; small trained models are faster and cheaper at high volume. Many systems route between them.
Shared versus channel-specific logic
A shared language layer keeps behavior consistent. Channel experiences are built on top — see [AI Chatbots](/ai-services/ai-chatbots) and [AI Voice Agents](/ai-services/ai-voice-agents).
Languages and locales
Which languages are fully supported, which are translated, and how quality is checked for each.
Confidence thresholds
When the system acts on its interpretation, when it asks a clarifying question, and when it sends text to a person.
Sensitive content
Detection and handling of personal data, complaints and urgent issues before text is stored or processed further.
Applications
Related capabilities
- AI ChatbotsText chat assistants for customer support, internal help desks and product experiences, grounded in your knowledge and connected to your systems.
- AI Voice Agents & ReceptionistsPhone agents that answer calls, understand callers in natural speech, book, route and take messages — and transfer to a person when needed.
- Generative AI & LLM DevelopmentApplications built on large language models that are grounded in your data, tested against real cases and costed before launch.
- AI IntegrationConnect AI models and assistants to your CRM, ERP, databases, knowledge stores and APIs — with identity, logging and fallbacks that production systems need.
Questions buyers ask
NLP is the capability; a chatbot is one product that uses it. The same language layer can classify emails, extract data from documents, and power both chat and voice conversations.
LLMs are now the core of most NLP work, but the engineering around them still matters — task definitions, evaluation, confidence handling, cost at volume and sometimes smaller specialized models.
Current models cover many languages well, but quality varies. We test each language you need on your own content and recommend where human review is required.
Yes. The language layer is delivered as a service your existing platforms can call, so you do not have to replace them. See AI Integration.
Discuss this capability with an engineer.
Tell us about the workflow or product. We reply with questions, a suggested first step and who would work on it.