NLP and Generative AI
If a significant part of your business involves text, documents, communications, or knowledge, NLP and generative AI deserve serious attention. The field spans everything from traditional techniques for classification, extraction, and search, to large language models for more open-ended tasks.
We help you identify where these techniques create genuine value, choose the right approach (often not the largest model), and deploy something your team can depend on.
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
- LLM integration and deployment
- Automated content generation
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
- Text summarisation and topic modelling
- Sentiment, feedback, and conversation analytics
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
- Context and prompt engineering
- LLM evaluation and testing
- AI safety and guardrails
- AI compliance and regulatory readiness
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.
Chatbots & AI Assistants
Build context-aware conversational agents that handle complex queries, maintain coherent dialogue across turns, and integrate with your existing systems. Designed for customer support, internal knowledge access, and automated workflows, with seamless handover to human agents where needed.
LLM Fine-Tuning & Customisation
Adapt pre-trained language models to your specific domain, vocabulary, and use case. From retrieval-augmented generation to parameter-efficient fine-tuning with LoRA and QLoRA. The right approach depends on your data, performance requirements, and cost constraints.
RAG Systems & Knowledge Management
Build retrieval-augmented generation systems that ground model outputs in your actual documents and data. Vector databases, embedding pipelines, and retrieval architectures that give models access to the right knowledge at query time, reducing hallucinations and enabling accurate, source-grounded responses at scale.
Agentic AI & Autonomous Workflows
Design and build multi-agent systems that plan, act, and adapt across complex, multi-step tasks. Using LangGraph and agentic frameworks to develop autonomous workflows that handle tool use, dynamic decision-making, and longer-horizon task execution. These take significantly more care to make production-reliable than simpler LLM applications.
LLM Integration & Deployment
Connect large language models to your existing data, systems, and workflows. We handle the engineering of integration, performance optimisation, and reliable deployment using LangChain, LangGraph, and modern orchestration tools to build robust production systems.
Context & Prompt Engineering
Design the context and prompting architecture that makes LLM systems reliable. This includes prompt structure and few-shot design, but the harder problem is often context engineering: deciding what information to inject at runtime, how to manage context length, and how to dynamically assemble the right inputs for each call.
LLM Evaluation & Testing
Establish rigorous evaluation frameworks for LLM-powered systems. Benchmark development, automated test suites, red-teaming for failure modes, hallucination assessment, and ongoing monitoring that gives you confidence your system is performing as intended before and after deployment.
AI Safety & Guardrails
Build the control layer that keeps LLM systems reliable and on-policy in production. Output validation, structured output enforcement, guardrail pipelines that intercept harmful or off-policy responses, and human-in-the-loop escalation patterns. Particularly important in healthcare, legal, financial, and other regulated environments.
AI Compliance & Regulatory Readiness
Build AI systems that meet the legal and regulatory requirements of your industry. GDPR-compliant data handling in training and RAG pipelines, HIPAA-safe deployment for healthcare AI, EU AI Act conformity assessment for high-risk systems, and security hardening against threats such as prompt injection. Compliance built in from the start is substantially cheaper than retrofitting it.
Information Retrieval & Search
Design and build document retrieval and enterprise search systems that surface the right content reliably. Hybrid lexical-neural search architectures, relevance tuning, and full search engine development, grounded in deep expertise in information retrieval research and applied IR system design.
Document Analysis & Processing
Extract and interpret information from complex documents of all kinds. Financial reports, legal contracts, medical records, and technical documentation analysed to pull out structured data, answer specific questions, and flag relevant sections.
Named Entity Recognition
Identify and classify people, organisations, locations, dates, and domain-specific entities in text with high accuracy. Trained on your specific vocabulary and document types for performance that general-purpose NER systems cannot match.
Text Mining & Extraction
Extract structured information from unstructured text. Relationship extraction, key phrase identification, and pattern discovery applied to contracts, reports, correspondence, and web content to surface the specific information your business needs.
Content Classification & Categorisation
Automatically organise large volumes of text into meaningful categories at scale. Classifiers trained on your specific categories and subject matter, enabling automated tagging, intelligent routing, support ticket prioritisation, and intent detection across documents and communications.
Text Summarisation
Automatically distil long documents, conversation threads, and reports into concise, accurate summaries. Extractive and abstractive approaches applied to meeting notes, research papers, customer calls, and operational reports, reducing reading time without losing the signal.
Question Answering Systems
Build systems that respond to natural language questions with accurate, grounded answers from a specific knowledge base. Customer self-service, internal knowledge management, and document Q&A, with evaluation frameworks to verify accuracy and guard against hallucination.
Sentiment & Feedback Analysis
Analyse free-text feedback and customer communications at scale to identify patterns, issues, and opportunities. Aspect-based sentiment analysis, trend tracking over time, and real-time monitoring applied to reviews, support interactions, and surveys to give you a continuous, objective read on perception.
Conversation Analytics
Extract structured insight from conversation data: customer support transcripts, sales calls, chat logs, and voice-of-customer records. Intent analysis, topic modelling, agent performance signals, and dialogue pattern mining that inform service quality, retention, and product decisions.
Automated Content Generation
Generate consistent, on-brand text content at scale using language models tailored to your domain. Product descriptions, report sections, summaries, and personalised communications, with proper evaluation to ensure output quality rather than just volume.
Specialist Machine Translation
Custom neural machine translation for domains where general-purpose tools fall short. Medical, legal, financial, and highly technical content where standard MT systems make costly errors, requiring domain-specific models that understand your vocabulary, tone, and terminology.
Text Analysis & Topic Modelling
Discover themes, structures, and patterns across large collections of documents. Unsupervised topic modelling, document clustering, and corpus-level analysis applied to research literature, customer feedback archives, and internal knowledge bases to surface insights that are not visible document by document.
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 below give a sense of what each typically involves.
Typical scope
A few weeks, depending on the specifics.
What this might include
- Opportunity assessment across your text and document workflows, with prioritised use cases
- Use case feasibility against your data and accuracy requirements
- Architecture recommendation (off-the-shelf, RAG, fine-tune, or custom)
- Indicative cost, timeline, and risks for a follow-on build
- Proof of concept on a slice of the use case where uncertainty is high
Typical scope
A few weeks for evaluation work or a tightly scoped pipeline; weeks to months for RAG, agentic, or fine-tuning builds, depending on data, integration, and reliability requirements.
What this might include
- Working system against an agreed accuracy or quality bar (RAG, chatbot, classification, extraction, or similar)
- Evaluation framework and test suite, including red-team cases for the failure modes that matter
- Fine-tuning or prompt-and-context architecture tailored to your domain and data
- Integration with your existing systems and data sources, plus guardrails or compliance layers where required
- Documentation and handover sessions for your team
- For LLM evaluation, agentic systems, or custom NLP research, deliverables shift to fit the actual work
Typical scope
A defined block of advisory hours, or retained advisory across a phase, depending on the scope of the question.
What this might include
- Written technical review of an existing system, including hallucination and reliability assessment
- Strategic brief on architecture (RAG vs fine-tune vs off-the-shelf), tooling, or vendor selection
- Recommendations document with concrete next steps
- Workshop sessions with your team on NLP and generative AI strategy or implementation
- Optional ongoing review cadence
Each one above sketches what that mode typically involves, not a fixed menu of packages. Many engagements combine more than one, or sit between them. If your situation looks different, get in touch and we will talk through what fits.
Is this for you?
Who this is for
This service 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 require 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 you are not sure whether your situation warrants a custom solution or a well-configured off-the-shelf tool, we will tell you 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. Your starting point on the site usually maps cleanly 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
Not sure which of these fits your situation? Book a free introductory call and we will talk through what you have in mind.
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.
Ready to get started?
Let's talk about your NLP and Generative AI needs.