Retrieval augmented generation (RAG)
Large Language Models (LLMs) can significantly improve productivity, but they can still generate inaccurate or “hallucinated” responses. To address this, we use a RAG framework that ensures the model retrieves only relevant information from secure internal documents before generating its answer.
Building trust and accuracy in large language model outputs
Turn institutional knowledge into instant intelligence
At Kiandra, we deploy a RAG Accelerator: a proven, governance-first foundation that turns your trusted organisational knowledge into secure, explainable, and actionable AI-assisted answers.
Sensitive information stays private and under your control; data is protected within your environment.
Grounds AI answers in specific and current company data, citing sources for transparency.
The challenges we solve
We break down knowledge silos by making your organisation’s information easy to search, access, and use; saving time and helping teams find what they need instantly.
The "key person" risk
- Critical knowledge is trapped in an individual's head, leading to bottlenecks, inconsistent advice, and risk if staff move on.
Onboarding friction
- New staff take a long time to become productive in policy-heavy or complex technical roles because of the learning curve around internal procedures.
The compliance gap
- An inability to confidently answer complex questions while meeting strict audit and regulatory requirements that demand proof of source.
Stagnant data utility
- Massive volumes of unstructured data like PDFs, project docs, and emails remain untapped assets because they are too difficult to query manually.
The benefits of a RAG-powered workplace
Institutional knowledge & capability uplift
RAG turns scattered knowledge into a single source of truth supporting consistency across teams and locations. It also accelerates onboarding, allowing new hires to “ask the documentation” for guidance on policies and workflows, reducing ramp-up time by as much as 20-40%.
Dynamic & accurate knowledge updates
RAG systems enable new information to be added to a searchable database, so the AI stays up to date in its responses. This means the AI doesn’t need to be retrained every time something changes. It also means it doesn’t just make things up from what it has learned in the past.
Enterprise-grade security & traceability
Our approach preserves your existing security rules. It also shows exactly where each answer comes from, so you can prove how every response was generated – important for compliance and audits in regulated industries.
Innovation & new business models
We enable organisations to explore innovation and unlock new business models through AI-powered pilots and proofs-of-concept.
We design low-risk, targeted experiments that demonstrate automated workflows, predictive insights, and intelligent customer or staff experiences. These pilots allow leadership to validate the potential of AI, test emerging ideas safely, and make informed decisions about where to invest next.
Why choose RAG?
Strategic leaders often ask if they should fine-tune a model instead. Here is how we break down the choice for our clients
RAG accesses your latest live documents
- A fine-tuned model is limited strictly to the data available when the training was completed.
Knowledge never becomes "stale"
- RAG does not require expensive retraining to stay relevant in fast-moving industries.
Source traceability
- Every response includes direct citations to verify answers against the original file.
Lower upfront cost
- You don’t need a large investment in GPU infrastructure or expensive labelled datasets to get started.
Rapid deployment
- Can be launched as soon as your documents are indexed - weeks faster than traditional training.
Flexible Maintenance
- Updates are as simple as adding a new PDF to a folder - no data scientists needed.
Instant Redaction
- If a document is deleted or restricted, the AI instantly loses access to that information at the next query.
Private data stays private
- Your proprietary IP remains in your secure vector database; it is never "baked into" the public model weights.
RAG built for scale, reliability, and long-term business value.
We specialise in integrating your RAG solution directly into your current software and business workflows, meaning minimal disruption for your team.
We prioritise explainability. Every AI-generated response is backed by clear citations linked to your authoritative documents, making it suitable for high-stakes, regulated environments.
We have a long-standing history of delivering complex solutions in document-heavy sectors, ensuring we understand the nuances of managing millions of text chunks across diverse repositories.
Our systems are designed to last by separating the data retrieval layer from the LLM. This allows you to swap or upgrade models as technology evolves, without losing your organisational context.