LLM and RAG systems that answer from your data — and show their sources
Retrieval-augmented generation (RAG) connects a large language model to your own documents and databases so it answers from verified sources rather than from its training data. Sysvoy builds RAG pipelines and LLM applications end to end — ingestion and chunking, hybrid search, model selection, citations, evaluation and access control — so answers are grounded, traceable and safe to put in front of customers or staff.
The path a question takes
Documents are parsed and split, embedded, searched, and the answer cites the passages it came from.
- Ingest
- Chunk
- Embed
- Retrieve
- Answer
Where LLM & RAG development projects stall
And what we do differently.
Badly parsed documents
Most wrong answers trace back to broken parsing and careless chunking. We treat ingestion as the core of the project, not a setup step.
Vector search on its own
Pure semantic search misses exact terms like part numbers and case names. Hybrid retrieval with re-ranking catches both.
No way to measure quality
Without an evaluation set, every change is a guess. We measure retrieval and faithfulness before and after each change.
What we build
Knowledge assistants
Assistants over your wikis, policies, tickets and shared drives that answer staff questions and cite the exact document.
Domain research tools
Search and analysis for specialist content such as case law or clinical documents — the kind of work behind midpage.ai.
Document Q&A and extraction
Ask questions of long PDFs, contracts and reports, or pull structured fields from them, with page-level citations.
Search upgrades
Hybrid semantic and keyword search for catalogues and content libraries, including Typesense-based product search.
LLM features in your product
Summaries, drafting, classification and chat inside an existing SaaS product, behind your own permissions.
How we deliver
From the first workshop to production. How we work
Ingestion done properly
Parsing PDFs, tables and scans, and chunking by document structure — the step that decides most of the answer quality.
Hybrid retrieval
Semantic and keyword search combined, then re-ranked, so exact terms like part numbers and case names are never missed.
Evaluation before launch
A test set of real questions with expected sources, used to measure faithfulness and catch regressions on every change.
Permission-aware answers
Retrieval respects your existing access rules, so people only get answers from documents they're allowed to see.
RAG compared with fine-tuning and prompt-only approaches
| RAG | Fine-tuning | Prompt only | |
|---|---|---|---|
| Uses your latest documents | Yes, at question time | Only what it was trained on | Only what fits in the prompt |
| Cites its sources | Yes | No | Partly |
| How it's updated | Re-index documents | Retrain the model | Edit the prompt |
| Best for | Knowledge that changes and must be cited | Tone, format or narrow tasks | Small, stable context |
A good fit when
- Staff lose time searching scattered documents for answers that already exist.
- You need AI answers you can audit, with a source for every claim.
- A generic chatbot hallucinated, and you need something that won't.
What we build with
- Python
- TypeScript
- PostgreSQL
- Vector databases
- Typesense
- OCR and table parsing
- Commercial and open-weight LLMs
- AWS
- Google Cloud
Related work
midpage.ai
AI research platform that layers analysis over case law for litigators and legal teams.
midpage.ai ↗AI commerce assistant
Self-hosted AI shopping assistant, Typesense product search and HubSpot integration for a US B2B distributor.
US distributorAI security platform
AI-driven security platform and SOC tooling for a European cybersecurity company.
Client under NDA
Common questions
Not answered here? Email contact@sysvoy.com.
What is the difference between RAG and fine-tuning?
RAG looks up relevant passages from your data at question time and gives them to the model, so answers stay current and can cite sources. Fine-tuning changes the model itself, which suits style or narrow tasks but doesn't keep it up to date with changing documents. Most business knowledge problems are best solved with RAG first.
Can RAG work with scanned PDFs and tables?
Yes, with OCR and table-aware parsing during ingestion. Ingestion quality is the biggest single driver of answer quality, so we spend real effort on it.
How do you know the answers are correct?
We build an evaluation set of real questions with known sources, measure whether answers are faithful to the retrieved text, and sample answers for human review after launch.
Will our data be used to train AI models?
Not by us. We use providers' enterprise no-training terms or self-hosted models, and keep data in your environment where your policies require it.
What does a RAG system cost to run?
Running cost depends on query volume, document volume and model choice. We estimate it during discovery and design for caching and smaller models wherever they are good enough.
Have something to build?
Tell us where things stand and what you need. A short message is enough to start.