How does it stay accurate when regulations change annually?

Re-indexing on publication updates every answer at once, and academic year is indexed as metadata so a returning student can be given the rules that applied to their cohort rather than the current ones.

Can staff and students share the same system?

Same retrieval layer, different scopes. Staff-only material — moderation guidance, internal procedures — is excluded from student retrieval entirely rather than hidden at the presentation layer.

Why not just fine-tune a model on our documents?

Fine-tuning teaches style and format well but is a poor way to store facts — it cannot cite sources, and updating a single document means retraining. RAG updates the moment you re-index and can always show its working. Most projects that think they need fine-tuning need better retrieval.

How accurate can we expect it to be?

That depends on your corpus, and we will measure it rather than promise a number. The evaluation harness gives you a retrieval accuracy figure on your own documents before anything reaches users — if it is not good enough, we tune until it is.

RAG Systems · Education

RAG Systems for Education

Course knowledge is spread across documents nobody reads end to end.

Handbooks, module guides, regulations, and reading lists contain every answer students and staff need, in a form nobody consumes linearly. Retrieval over that corpus turns it into something answerable, scoped per programme so nobody gets another course's rules.

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Every claim: GroundedEvery claim
Search strategy: HybridSearch strategy
Retrieval accuracy: MeasuredRetrieval accuracy

Typical stack

LlamaIndex
LangChain
Pinecone
pgvector
Cohere Rerank

Use cases

What this handles for education providers

Answer regulation and assessment questions from live handbooks with citations
Search across module guides, reading lists, and past materials semantically
Scope retrieval by programme, year, and campus so answers are actually applicable
Give administrative staff the same source of truth students are given

One answer to any policy question, drawn from the current document rather than institutional memory.

Context

The education problem underneath it

Education providers field the same questions thousands of times a year — deadlines, requirements, fees, timetables — while teaching staff lose hours to administration. AI systems grounded in your own handbooks and course materials answer the repeat questions and take the paperwork off academic staff.

Student enquiries spike at enrolment and deadlines, overwhelming small admin teams
Course information is spread across handbooks, portals, and PDFs nobody reads
Enrolment and document verification are manual and slow at exactly the wrong time
Teaching staff spend disproportionate time on administration over instruction

What we build

Inside a RAG system

Most failing RAG deployments fail at retrieval, not generation. We build the evaluation harness first: a set of real questions with known-correct source passages, so retrieval accuracy is a number we can improve rather than a feeling. Chunking strategy, hybrid search, and reranking then get tuned against that number.

Document parsing that preserves tables, headings, and layout structure
Chunking strategies tuned per document type rather than a fixed token count
Hybrid dense and sparse retrieval with a cross-encoder reranking pass
Citation grounding that ties every claim to a specific retrieved passage
Retrieval evaluation harness with recall and precision tracked per release
Incremental re-indexing so updated documents are searchable immediately

Deliverables

What ships with the engagement

Indexed corpus with a documented chunking and embedding strategy
Golden evaluation set of questions with verified source passages
Retrieval quality report benchmarked before and after tuning
Query API or chat surface, depending on how you will consume it
Runbook for adding new document types to the pipeline

FAQ