RAG lets an assistant answer from your documents rather than from whatever it read on the internet. We build the whole pipeline: your files are cleaned, chunked and embedded into a vector database, then retrieved at question time so the model answers with your content and cites where it came from.
RAG Services We Offer
The unglamorous engineering that decides whether an AI answer is trustworthy.
Document Ingestion Pipelines
Your knowledge arrives as PDFs, spreadsheets, wiki pages and scans. We turn all of it into something searchable.
- PDF, Word, Excel and web pages
- Text extraction from scans
- Automatic re-indexing on updates
Vector Search & Embeddings
The retrieval layer decides answer quality far more than the model does, so it is where we spend the effort.
- Chunking tuned to your content
- Pinecone, Qdrant and pgvector
- Hybrid keyword and semantic search
Grounded Answers With Citations
Every answer points at the passage it came from, so your team can check it in one click instead of trusting it blindly.
- Each answer linked to its source
- A clear refusal when it is not known
- Confidence and coverage reporting
Private & On Premise Deployment
For contracts, patient records or anything regulated, the whole pipeline can run inside your own environment.
- Your data stays in your account
- Open source models where required
- Access control per user and role
Enterprise & Site Search
Search that understands the question rather than matching words, returning an answer instead of ten blue links.
- Semantic search across all content
- Plain language queries
- Answers with the source alongside
Evaluation & Continuous Tuning
We measure answer quality against a real question set, so improvements are proven rather than assumed.
- Answer quality test sets
- Retrieval accuracy measurement
- Ongoing chunking and prompt tuning
Our RAG Process
Get retrieval right and the model looks brilliant. Get it wrong and no model saves you.
Map
We find where your knowledge actually sits, in files, wikis, tickets and the website, and what is worth answering from.
Prepare
Documents are cleaned, split and embedded so the right passage can be found in milliseconds.
Connect
Retrieval is wired to the model and into your app, chatbot or internal search, with access rules applied.
Evaluate
Answers are tested against a real question set and retrieval is tuned until the accuracy holds up.
RAG Technologies We Work With
Hosted or fully self hosted, depending on how sensitive your documents are.
- OpenAI Embeddings
- Claude
- LangChain
- LlamaIndex
- Pinecone
- Qdrant
- pgvector
- Elasticsearch
- Python
- FastAPI
- Node.js
- Docker
What You Get
The difference between an assistant your team trusts and one they quietly stop using.
Answers From Your Documents
Responses are built from your own material, so the prices, policies and product details are the real ones.
Sources You Can Check
Every answer cites the passage behind it, which turns a guess into something your team can verify.
Fewer Made Up Answers
Grounded retrieval plus a proper refusal path is what stops a model inventing a confident wrong answer.
Always Current
Update a document and the index follows, so the assistant stops quoting last year's policy.
Your Data Stays Yours
Private deployment options where nothing sensitive leaves your own infrastructure or account.
Search That Understands
People ask in their own words and still find the paragraph they needed, without knowing the keyword.
Sitting on documents nobody can search?
Point us at your handbooks, contracts or support archive. We will build a small proof of concept on your real files so you can judge the answers yourself.
Get in touch
We are here to help you with your queries.