For teams that need people to find useful answers in their business knowledge.
Help people find useful answers in your business knowledge.
I build retrieval-based AI features that use relevant documents and data to support answers. The work includes source preparation, retrieval quality, access rules and evaluation of the answers people receive.
RAG, or retrieval-augmented generation, retrieves relevant information from your sources before a model answers. Grounding reduces some errors but does not eliminate incorrect answers, so evaluation remains part of the work.
Share the documents or data people need answers from, and where those answers should appear.
- OpenAI
- Claude
- Embeddings
- Vector search
- Next.js
- PostgreSQL
- AWS
What RAG actually solves
A RAG system gives an LLM something it doesn't normally have: specific, current, internal information from your own data and a way to ground its answers there.
Ground answers in your data
Retrieve from your documents before generating an answer. Grounding can reduce some errors; it does not eliminate incorrect answers, so evaluation still matters.
Keep answers current
Update the underlying documents without retraining retrieval picks up new context automatically.
Cover internal knowledge
Make internal documentation, policies, and tickets answerable through the same assistant.
Cite what the answer came from
Useful when the answer needs to be defensible sales, support, regulated workflows.
Who needs RAG
RAG fits when a model by itself isn't enough because the answer has to come from your data.
Companies answering questions over internal documents
SaaS products that need a smart, grounded assistant
Healthcare, legal, or finance teams handling structured knowledge
Support and operations teams reducing manual lookups
Engineering teams building AI features over their own APIs and content
How a RAG system works
The pipeline a production RAG system is usually built from the order matters.
Document ingestion
Take the source documents PDFs, web pages, knowledge bases, tickets and turn them into a clean corpus.
Chunking
Split documents into chunks that retain enough context to answer a question, but stay focused enough to be precise.
Embeddings
Generate vector representations for each chunk using a model appropriate for the corpus and the queries.
Vector search
Index embeddings in a vector store with metadata so retrieval can apply the right filters.
Retrieval
At query time, embed the query and retrieve the most relevant chunks optionally filtered by tenant, document type, or date.
Reranking
Where useful, apply a reranking pass to put the best candidates at the top of the context window.
Context construction
Format the retrieved chunks so the model sees citations, source labels, and the right amount of context.
Generation
Generate the answer with the constructed context, then surface citations in the response where applicable.
RAG architecture
The components that make up a production RAG system and the engineering decisions behind each.
Ingestion pipeline
Document loaders, parsers, and OCR (when needed) that turn raw sources into normalised text.
Chunking strategy
Fixed-size, semantic, or document-aware chunking chosen by document type and answer shape.
Embedding model
A model with the right balance of recall, latency, and cost for the corpus and the queries.
Vector store
Hosted or self-hosted vector search with the right recall, metadata filtering, and operational guarantees.
Metadata and access control
Tenants, document types, dates, and permissions are passed into retrieval so answers stay scoped.
Reranking
Optional rerankers improve precision where the first-pass retriever is too permissive.
Citations
Surface the source chunks so the answer is verifiable and the user can audit the answer.
Evaluation
Test sets, hallucination checks, and answer-quality metrics run continuously as the system evolves.
RAG work is mostly engineering work. The model choice matters, but most of the answer quality comes from the pipeline.
Building RAG into existing SaaS
Most RAG work doesn't start from zero it sits alongside the systems a product already runs.
RAG over product content
Retrieval over the product's content, documentation, or help-centre articles.
RAG over internal documentation
Internal wikis, runbooks, and policies turned into an answerable assistant.
RAG over customer data
Tickets, accounts, and conversations retrieved into the existing product for support workflows.
RAG over audio and video
Transcripts and captions fed into the same retrieval pipeline as text where it makes sense.
Production considerations
What makes a RAG system survive production rather than just look good in a demo.
- Hallucination mitigation and refusal behaviour
- Per-tenant access control on retrieval
- Re-indexing and incremental updates
- Latency and cost budgets per query
- Evaluation and quality monitoring
- Citations and source attribution
- Logging and observability for retrieval
- Failure modes and recovery
Relevant work
A real product where retrieval grounds the user experience.
Skannr grounded AI booking
A conversational entry point grounded in provider data, scan availability, and booking context.
Next.js · FastAPI · OpenAI · PostgreSQL · AWS · retrieval
Build Something SimilarCommon RAG project shapes
RAG projects usually start from one of these a useful starting point for a brief.
RAG over company documents
An assistant grounded in HR docs, policies, runbooks, or product knowledge.
RAG over customer support history
Retrieval over historical tickets and answers to support agents in real time.
RAG over product content
Documentation, help-centre, and changelog retrieved into the existing product.
RAG over research data
Domain-specific corpora legal, scientific, financial turned into an answerable assistant.
RAG over multi-tenant data
Retrieval that respects tenant boundaries and access controls from the first query.
Frequently asked questions
Answers to the most common questions about RAG systems.
Retrieval-augmented generation. The model generates an answer using information retrieved from your own data, so the answer is grounded not generated from general knowledge alone.
Fine-tuning changes the model's behaviour using examples; RAG keeps the model the same and feeds it the right information at query time. They solve different problems and are often combined.
It depends on the corpus, the queries, and the constraints. The right choice is a trade-off between recall, latency, cost, and where the model runs.
It depends on scale, latency, and operational requirements the same pipeline works against hosted and self-hosted stores. The choice usually follows the operational story.
Test sets with real queries, retrieval precision/recall, hallucination checks, and qualitative review. Evaluation runs continuously as the corpus and traffic change.
A focused RAG prototype over a single corpus can land in a few weeks. Production RAG with monitoring, multi-tenant access, and continuous evaluation is scoped project-by-project.
Yes citations are usually surfaced in the answer so the user can verify it. This is one of the practical benefits over models without retrieval.
Discuss a knowledge workflow.
Share the sources people need to use and where answers should land. We can then assess retrieval quality, access and evaluation.
You don't need to know the technical solution first. Describe the problem I'll help identify the right path.