AI Health Insight Engine for Elderly Care
Longitudinal wellbeing data turned into plain-language health observations, with an evidence-constrained architecture that keeps every output safe for elderly users.
NLP and Generative AI
If a significant part of your business involves text, documents, communications or knowledge, NLP and generative AI are worth taking seriously. The field spans traditional techniques for classification, extraction and search, through to large language models for more open-ended tasks.
We help you identify where these techniques are worth using, choose the right approach, and deploy something that improves how the business operates.
Overview
Common ways NLP and generative AI are used
Most NLP and generative AI projects fall into one of the broad areas below. Each covers a set of specific capabilities listed further down the page.
Conversational and generative AI built for production
Systems that take a question, a request, or a multi-step task and produce a useful answer. Chatbots, AI assistants, RAG over your own documents, agentic workflows, and automated content generation. The hard part is making them reliable enough to put in front of customers or staff.
Some examples
- Chatbots and AI assistants
- RAG systems and document Q&A
- Agentic AI and autonomous workflows
Extracting and analysing text at scale
Where the value is in pulling structured signal out of unstructured text: documents, support tickets, customer feedback, transcripts, contracts. Often higher value and lower risk than generative work, and frequently better solved with smaller, more targeted models than a large language model.
Some examples
- Document analysis and processing
- Named entity recognition and text extraction
- Content classification and categorisation
Customising, evaluating, and safely deploying language models
The work of taking a foundation model and making it fit your domain, your data, and your risk profile. Fine-tuning, prompt and context engineering, evaluation frameworks, guardrails, and the compliance work needed in regulated environments.
Some examples
- LLM fine-tuning and customisation
- LLM evaluation and testing
- AI safety and guardrails
Our services
What this looks like in practice
Below are the specific capabilities and use cases that sit within those broad areas. Some span more than one. The list is not exhaustive. If your needs are different or more specific, just get in touch.
Working with us
How we work with you
Most NLP and generative AI work fits one of three modes. Scope and deliverables vary; the examples sketch what each typically involves.
Many engagements use more than one of these.
If it is not clear which of these fits, book a free intro call and we can work it out.
Is this for you?
Who this is for
This is most valuable in organisations where text is central to operations: customer support, legal and compliance, healthcare documentation, publishing, financial research, or any domain where unstructured text is a significant part of the workflow.
It is also well suited to businesses looking to build AI assistants, document Q&A systems, automated content pipelines, or agentic workflows that handle multi-step tasks. These need careful scoping and evaluation, particularly around accuracy, domain specificity and reliability in production.
You do not need deep technical expertise internally to get value from this work. You do need a clear use case, realistic expectations, and a willingness to test thoroughly before deployment. If it is not clear whether a custom solution or a well-configured off-the-shelf tool is enough, we will say so directly.
When something else fits better
NLP and generative AI, AI and machine learning, and data science all overlap, and many engagements draw on more than one. The starting point on the site usually maps to one of the following:
- Predictive systems built on numeric, structured, or sensor data rather than text: AI & Machine Learning
- Statistical analysis or analytical exercises on existing data, where the deliverable is insight rather than a deployed system: Data Science & Analytics
- Sizing up where AI fits at all, without a specific use case yet: Getting Started with AI
In practice
See it in practice
A selection of engagements that involved this type of work.
FAQ
Common questions
What is the difference between NLP and generative AI?
NLP is the broader field covering all techniques for understanding and processing language: classification, extraction, search, sentiment, translation. Generative AI and LLMs are a subset focused on producing text. Many NLP use cases do not need a large language model at all; a well-configured classifier or extraction pipeline is often faster, cheaper, and more reliable. We will recommend the right approach for your use case.
Should we build on an existing LLM or build from scratch?
In almost all cases, building from scratch is not the right approach. Existing foundation models provide a capable starting point. The real decision is between using them out of the box, fine-tuning for your domain, or building a RAG system around your data. We recommend the right architecture based on your use case, data, latency requirements, and budget.
What is the difference between RAG and fine-tuning?
RAG grounds the model's responses in documents you supply at query time, without changing the model itself. Fine-tuning adjusts the model's weights using your data, making it more specialised at a fundamental level. RAG is often faster and cheaper to implement and easier to keep current. Fine-tuning is better when domain-specific language needs to be deeply embedded. A combination frequently works best.
How do you handle hallucination?
Hallucination is a real risk and one we take seriously. The main mitigations are RAG to ground responses in your documents, structured outputs, output validation, and confidence scoring. For high-stakes applications we also design human-in-the-loop review steps. We do not deploy without proper evaluation frameworks in place.
Do we need a large volume of labelled training data?
Not always. Modern pre-trained models can be effective with limited labelled examples, particularly for classification and extraction tasks. For some use cases, well-configured off-the-shelf tools are sufficient. We start from what you have and recommend the most cost-effective path.
Are LLMs suitable for regulated industries like healthcare, legal, or finance?
Yes, with the right architecture and safeguards. Compliance, data residency, and auditability requirements shape the design significantly. We have specific experience building compliant LLM applications including GDPR-aligned RAG pipelines and HIPAA-safe healthcare applications.
Let's Talk
Need language AI that holds up in production?
Let’s talk about the text, the risk and what a reliable system would actually require.