NCS London's Retrieval-Augmented Generation (RAG) solutions help you build AI applications that retrieve relevant information from your business data and use it to generate context-aware responses.
Overview
Valuable knowledge is often spread across documents, databases and business applications, making the right information hard to find when it is needed.
Our RAG solutions connect approved data sources to AI applications that retrieve relevant information and generate context-aware responses. We help you assess the use case, connect suitable sources and design a solution that fits your technology environment and security requirements.
Business Opportunities
Make business information easier to find and use
When employees spend too much time searching through policies, documents and internal knowledge bases, productivity suffers. RAG grounds answers in your own approved information, so your teams can:
What We Offer
Connect relevant documents, databases and approved business information sources, then design retrieval processes that supply the right context to the language model, improving the usefulness of generated responses.
Develop business-focused assistants that help employees query internal knowledge, understand documentation and access relevant information through conversational interfaces.
Incorporate appropriate data permissions, source references, response testing and monitoring to help manage inaccurate answers and protect sensitive information.
Business Outcomes
Faster Access to Knowledge
Spend less time searching across disconnected information sources.
More Informed Decisions
Give employees relevant business context when they need it.
Reduced Repetitive Work
Automate parts of internal research and information retrieval.
Greater Control Over AI Outputs
Evaluate answer quality, source relevance, permissions and reliability.
Our Approach
The result: A RAG solution grounded in your own knowledge, tested for accuracy and ready to improve as your content grows.
Use-Case and Data Assessment
Identify the business problem, intended users, information sources and expected outcomes.
Knowledge and Retrieval Design
Assess data quality, document structure, access permissions and retrieval requirements before selecting the appropriate architecture.
Solution Development and Integration
Build and integrate the RAG application with relevant systems, ensuring that data access and security requirements are addressed.
Testing and Optimisation
Evaluate answer relevance, factual grounding, retrieval quality, response time and failure scenarios.
Deployment and Continuous Improvement
Monitor performance, incorporate feedback and refine the solution as your information landscape and business needs evolve.
Why NCS London
RAG success depends on more than connecting a language model to a collection of documents. Data quality, information architecture, access permissions and evaluation all influence whether the solution delivers value in everyday operations.
NCS London brings a consulting-led approach to AI solution development, helping you assess feasibility, establish the right technical foundations and integrate the solution into your business environment.
RAG solutions combine information retrieval with generative AI. They retrieve relevant content from selected data sources and provide it to a language model to generate responses informed by that information.
A standard chatbot may rely primarily on its training and configured instructions. A RAG-based chatbot can retrieve relevant information from approved business sources at query time, helping it respond with organisation-specific context.
Yes, depending on the systems, interfaces, data formats and security requirements involved. An initial assessment establishes which sources can be connected and how information should be retrieved.
We can use relevant source retrieval, citations, permission controls, evaluation datasets, confidence thresholds and appropriate fallback behaviour. These measures reduce risk but cannot guarantee that every response will be correct.
RAG is often a strong starting point when the objective is to answer questions using changing or proprietary business information. Fine-tuning is more relevant when you need to adapt a model's behaviour or output patterns. Some applications benefit from combining both approaches.
We design access controls, data handling and hosting choices around your security requirements, so information is only retrieved for authorised users.
Timelines depend on your data sources, integrations and use case. We usually start with a focused pilot before wider rollout.
Start with a conversation about your current environment, priorities and constraints. We then recommend a sensible first step, such as an assessment or scoping session, before any wider work is agreed.
Contact us
We focus on practical use cases, responsible data access and measurable outcomes, with an approach designed around your existing systems and long-term technology priorities.
Prefer to talk? Call us on 020 3326 7268
