Service 03 — Generative AI, RAG & LLM Solutions
Generative AI grounded in your data.
Keap Technologies builds retrieval-augmented generation (RAG) and large language model (LLM) applications that answer from your own documents and data, with a source citation for every answer. We design private knowledge assistants, document Q&A, multilingual search and AI agents that run in your cloud or an EU region — for regulated teams in Cyprus, EMEA and the US.
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What we build
01
Knowledge assistants
A private, ChatGPT-style assistant over your policies, procedures and product documentation — answers cite the exact source passage.
02
Document Q&A & extraction
Ask questions across thousands of contracts, filings or reports, and extract structured data from them at scale.
03
Multilingual search
Semantic search that works across Greek and English, so users find the right document whichever language it was written in.
04
AI agents & workflow automation
LLM agents that complete multi-step tasks — triage, drafting, data lookup — with approval steps and full audit logs.
05
Private & self-hosted LLMs
Open-weight models deployed in your own infrastructure when data must never leave it.
06
Evaluation & guardrails
Test sets, faithfulness metrics and guardrails that measure quality before and after launch.
What is retrieval-augmented generation (RAG)?
Retrieval-augmented generation (RAG) is an architecture in which a large language model answers a question using passages retrieved from a trusted knowledge base, instead of relying only on what it memorized during training. The approach was introduced by Lewis et al. in 2020 and is now the standard way to put LLMs to work on private, fast-changing information.
For a business, RAG has three practical advantages: answers stay current without retraining a model, every answer can be traced back to a source document, and access to information can follow the same permissions as the underlying systems.
RAG vs fine-tuning vs prompting: which do you need?
Most enterprise use cases start with RAG. Fine-tuning changes how a model writes or behaves; RAG changes what it knows. Many production systems combine both.
| Approach | Best for | Keeping knowledge current | Traceability |
|---|---|---|---|
| Prompting only | General writing and reasoning tasks | Not applicable — no private knowledge | Low |
| RAG | Answering from private, changing documents | Re-index documents; no retraining | High — cites sources |
| Fine-tuning | Specialist tone, format or narrow tasks | Requires retraining | Low |
| RAG + fine-tuning | High-volume specialist assistants | Re-index plus occasional retraining | High |
How we build production RAG
- 01Source audit — which documents are authoritative, who may see them, and how often they change.
- 02Ingestion — parsing, cleaning and chunking that preserves headings, tables and permissions.
- 03Hybrid retrieval — keyword and vector search combined, with re-ranking to put the best passages first.
- 04Grounded generation — the model answers only from retrieved passages, cites them, and says when it doesn't know.
- 05Evaluation — a test set of real questions scored for faithfulness, answer relevance and citation accuracy.
- 06Guardrails & monitoring — logging, feedback capture and alerts so quality is measured in production, not assumed.
Private and compliant by design
Regulated teams can't send confidential data to a model they don't control. Keap Technologies deploys RAG systems inside your cloud account or an EU region, uses enterprise model endpoints configured so your data is not used for training, or self-hosts open-weight models when data must never leave your infrastructure.
Access controls mirror your source systems, every question and answer is logged for audit, and transparency obligations under Article 50 of the EU AI Act — telling people they are interacting with an AI system — are built into the interface. Read our guide to RAG for regulated industries.
Greek and English, side by side
Many organizations in Cyprus and Greece keep records in both Greek and English. Keap Technologies builds multilingual retrieval so a question in one language finds relevant documents written in the other.
Where self-hosting matters, open Greek-language models are now an option: Meltemi, released by the Institute for Language and Speech Processing (ILSP) of the Athena Research Center in 2024 as the first open Greek LLM, and Llama-Krikri, released by ILSP in February 2025.
Frequently asked questions
Is RAG better than fine-tuning an LLM?
For answering questions from private, changing documents, yes: RAG keeps knowledge current by re-indexing documents, and it can cite the source of every answer. Fine-tuning is better for teaching a model a specialist style or narrow task. Many production systems combine both.
Can a RAG assistant run without sending our data to OpenAI?
Yes. Keap Technologies can deploy open-weight models inside your own infrastructure, or use enterprise model endpoints hosted in an EU region and configured so your data is not used for training. The right option depends on your data sensitivity and regulatory obligations.
How do you reduce hallucinations?
By grounding every answer in retrieved passages, requiring citations, instructing the model to decline when the sources don't contain the answer, and measuring faithfulness on a test set of real questions before and after launch.
What do you need from us for a RAG proof of concept?
A representative set of documents, a list of real questions your users ask (with the answers an expert would give), and clarity on who may see what. With those, we can measure quality from the first iteration.
Keep exploring
Let's build what's next.
Tell us about your data and the decision you want to improve. We'll come back with a clear plan.