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Your documents, finally answerable.
Manuals, policies, contracts and specs hold the answers your team and customers need — buried in PDFs nobody wants to search. We build retrieval systems that answer questions in plain language, grounded in your own documents and citing where the answer came from.
Why RAG & Document AI?
Retrieval-augmented generation is a simple idea done carefully. Rather than hoping a language model knows about your business, the system first searches your own documents for the passages that actually answer the question, then writes the answer from those passages — and can show which document it came from. Update the document and the answer updates. There's no retraining cycle.
Most of the engineering is in retrieval quality, which is where projects quietly fail. How documents are split, which embedding model is used, whether results are reranked, how tables and diagrams are handled — these decide whether answers are accurate or confidently wrong. We build evaluation sets from real questions and measure retrieval before anyone judges the writing. You can see the approach running live on AVYANTA's site.
Best for
Grounded
answers from your docs, with citations
No retraining
update a document, answers update
Private
can run entirely in your environment
Live proof
shipped for a safety-tech manufacturer
RAG & Document AI solutions we deliver.
Document Ingestion Pipelines
PDFs, Word files, spreadsheets and scans processed, chunked and indexed — including tables and structure.
Semantic Search
Meaning-based search, so the right passage surfaces even when the wording doesn't match.
Grounded Q&A Assistants
Plain-language answers written from retrieved passages, with citations back to the source document.
Retrieval Evaluation
Test sets built from real questions, measuring whether the right passage is actually being found.
Access-controlled Knowledge
Permissions respected in retrieval, so people only get answers from documents they're allowed to see.
Private & On-premise Options
Open-source models and self-hosted vector stores when documents cannot leave your infrastructure.
Why teams choose RAG & Document AI.
Answers you can verify
Citations to source documents mean anyone can check the answer instead of trusting it blindly.
Always current
Update the document and the system answers from the new version — no retraining, no model refresh.
Cheaper than fine-tuning
For knowledge, retrieval beats fine-tuning on cost, update speed and traceability. We'll say when fine-tuning is genuinely warranted.
Retrieval measured, not assumed
We evaluate whether the right passage is found before judging the answer, because that's where accuracy is actually won or lost.
Your data can stay yours
Open-source models and self-hosted vector stores keep sensitive documents inside your own environment.
Handles real business documents
Tables, headers, appendices and scanned pages — the messy formats your knowledge actually lives in.
The tools we pair
with RAG & Document AI.
We pick the right stack for your project — not the one we're most comfortable with.
Retrieval
Models
Pipeline
What a RAG system actually costs.
RAG running costs are modest and predictable: a one-time embedding cost per document, a vector store, and model usage per question. Here's the breakdown — click any provider for current live pricing.
Embeddings
One-time per doc
Generated once when a document is indexed. Open-source models make this effectively free.
View plans →Vector database
Low monthly
Self-hosted (ChromaDB, pgvector) costs nothing extra; hosted options have free tiers.
View plans →Model usage
Per question
Only the retrieved passages go to the model, which keeps cost per answer low.
View plans →OCR (if needed)
Per page
Only for scanned documents requiring image-to-text conversion first.
View plans →Build & evaluation
One-time
Ingestion pipeline, retrieval tuning, evaluation set and integration.
View plans →Because only relevant passages are sent to the model, cost per question stays low even with a large document set. Self-hosting the embeddings and vector store can reduce the recurring cost to almost nothing beyond your existing server.
Common questions,
honest answers.
Everything people usually ask about RAG & Document AI before starting a project. Still unsure which platform fits your goals? Ask us — we'll give you a straight answer, not a sales pitch.
Ask a questionWhat is RAG, in plain terms?
The system searches your documents for passages relevant to the question, then writes an answer using those passages. It's the difference between asking someone who has read your manual and asking someone who is guessing from general knowledge.
How is this different from fine-tuning a model?
Fine-tuning changes the model's behaviour and style; retrieval gives it access to facts. For company knowledge, retrieval wins on cost, on how quickly you can update it, and on being able to trace where an answer came from.
Can it show where an answer came from?
Yes, and we build it in by default. Every answer can cite the source document and section, so anyone can verify it. For technical, legal or safety content this isn't optional.
How many documents can it handle?
From a handful of manuals to very large archives. Volume mainly affects infrastructure choices; the harder variable is document quality and structure, not raw count.
Will it work with scanned PDFs and tables?
Yes, with the right pipeline. Scans go through OCR first, and tables need handling that preserves structure — otherwise rows and columns turn into meaningless text. This is where naive implementations quietly break.
What happens when we update a document?
You re-index it and the system answers from the new version immediately. That's the operational advantage over fine-tuning, where any change means another training cycle.
Can it run entirely on our own servers?
Yes. Open-source embedding models and a self-hosted vector store mean documents never leave your infrastructure — the usual requirement for legal, medical and regulated content.
How do you know the answers are accurate?
We build an evaluation set from real questions with known correct answers, and measure retrieval quality separately from answer quality. Most failures are retrieval failures, so that's what gets measured first.
Can it respect who is allowed to see what?
Yes — permissions are applied at retrieval, so a user only ever gets answers drawn from documents they already have access to. Filtering the answer afterwards is not the same thing, and not safe.
What does a RAG system cost to build and run?
The build depends on document volume, formats and integration needs. Running costs are embeddings (cheap, one-time per document), the vector store, and model usage per question. Most clients pair this with an AI chatbot as the front end. We spec the exact monthly figure before starting.
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