Ingestion & chunking
PDF, Word, Excel, email, wikis, ticketing systems, databases — whatever the source, we extract the content and split it into meaningful chunks while preserving tables and heading structure. OCR for scanned documents is included.
AI & LLM
RAG & Enterprise Knowledge Systems
Retrieval-Augmented Generation connects the model to your own knowledge base: answers come from your documents, arrive with their sources, and update the moment a document changes — no retraining required.
What you get out of it
- Time to find the right information drops from minutes to seconds
- Answers arrive with their sources — trustworthy and auditable
- Update a document and the knowledge updates instantly; no retraining
- Support teams spend markedly less time on repeat questions
- Institutional memory lives in the system, not in individuals
pgvector
Qdrant
Weaviate
Elasticsearch
LangChain
LlamaIndex
BM25
Cross-encoder rerankers
OCR
Redis
WHAT'S INCLUDED
What this service covers
Hybrid retrieval
Vector similarity alone is not enough. We combine semantic search with classic keyword search (BM25) so exact matches — part numbers, clause references — are found reliably too.
Reranking & context selection
We rerank candidate chunks with a cross-encoder and hand the model only what is genuinely relevant. This raises accuracy and lowers token cost at the same time.
Citations & traceability
Every answer links to the document and page it rests on. Users can verify a claim, and when audit time comes, the record of which answer came from which source already exists.
Permission awareness
Retrieval is limited to the documents a given user is allowed to see. HR files do not become public; the RAG layer applies your existing authorisation model rather than bypassing it.
Hallucination control
When the sources do not contain an answer, the model is steered to say so rather than invent one. We measure answer-to-source consistency automatically and flag low-confidence responses.
PROCESS
How we run it
01
Knowledge source inventory
What information lives where, who may access it, how often it changes — we map this first.
02
Ingestion pipeline
We connect the sources and tune the conversion and chunking strategy per document type. Changes are picked up automatically.
03
Retrieval quality tuning
We build a test set from real questions, measure hit rate and tune chunk size and ranking weights against it.
04
Interface & integration
A chat interface, intranet search, a Slack/Teams bot, or an assistant embedded inside your existing application — wherever you need it.
COMMON QUESTIONS
RAG & Enterprise Knowledge Systems
From a few hundred pages of manuals to millions of documents. The architecture changes with scale — a single vector index suffices for small sets, while large corpora need partitioned indexes and stronger reranking.
Yes, though the choice of embedding model matters a great deal. We prefer multilingual models with measured strength in Turkish and compensate for morphology with hybrid search. We benchmark on your own content before committing.
In RAG, documents are never trained into the model — they are supplied as context at question time. For a user without permission the document is simply never retrieved, so it never reaches the model at all.
RELATED SERVICES
LLM Training & Fine-Tuning
Fine-tuning, LoRA/QLoRA, instruction tuning, DPO and evaluation
Local & Private LLM Deployment
Models that run on your own servers and keep data in-house
AI Adoption Consulting
Where to start, what will pay off, and what will not
Let's talk about where AI fits in your business.
In a short call we listen, then tell you plainly which approaches fit your situation and which do not. We reply within 24 hours.