LLM integration and RAG that make AI answer from your own data
Rinaztec provides LLM integration and retrieval-augmented generation (RAG) development for businesses worldwide. We connect large language models such as GPT, Claude, Gemini or open-source models to your documents, databases and products, so answers come from your information with sources attached. We fine-tune a model only when it clearly beats a well-built RAG system, and we test accuracy before anything reaches your users.
- PRICING
- Fixed quote
- TIMELINE
- 3-6 weeks
What is RAG, and when do you fine-tune?
Retrieval-augmented generation (RAG) means the system first searches your own content for the passages that answer a question, then gives only those passages to the language model to write the answer. It keeps answers current and checkable without retraining. Fine-tuning changes the model itself, and it suits a fixed style, format or narrow task, not facts that change often.
LLM work we do
AI knowledge base
Search and answers across your policies, manuals, wikis and tickets, with links to the exact source passage.
AI features inside your product
Summaries, smart search, drafting and copilots built into your SaaS or internal tool through your own API.
Custom LLM and fine-tuning
A model tuned to your format, tone or classification task, hosted by a provider or in your own cloud.
Private and self-hosted models
Open-source models running in your infrastructure when data must not leave it.
Model evaluation and switching
Test sets that compare models on your own tasks, so you can change provider on cost or quality without a rebuild.
What's included
- Document ingestion and chunking pipeline with access control
- Vector search or hybrid search tuned to your content
- Answer generation with source citations
- Evaluation set and accuracy report
- Provider-neutral model layer
- Usage, latency and cost monitoring
- Full source code and documentation
How LLM Integration & RAG Development projects run
- 01
Use case and data (week 1)
We agree what the model should do, gather the content and write the questions it must get right.
- 02
Retrieval first (weeks 1-3)
We build ingestion and search, and measure whether the right passages are found before tuning prompts.
- 03
Generation and evaluation (weeks 2-5)
We tune prompts, compare models and report accuracy, cost and speed on your test set.
- 04
Integrate and launch
We expose it through an API or interface, add monitoring and hand over the evaluation suite.
How much does LLM integration cost?
Cost depends on how much content goes in, how messy it is, the access rules, and where the system runs. A knowledge base over a clean document set is a short project, while fine-tuning or self-hosting adds work and infrastructure. Model usage costs are separate, and we compare providers on your own workload so you know the running cost in advance.
Why businesses choose Rinaztec
In-house engineers
The people on your call are the people writing your code. No outsourcing, no hand-offs.
Preview in week one
A working preview link early and weekly demos, so you always see real progress.
Fixed, honest quotes
You approve the plan and budget before we write code. No surprise invoices.
You own everything
Full source code, accounts in your name, no lock-in and no licence fees to us.
LLM Integration & RAG Development questions, answered
Should I use RAG or fine-tuning?
For answering from your documents, RAG is almost always the right start: it is cheaper, stays current and shows its sources. Fine-tuning helps when you need a fixed format, tone or a narrow classification task. Some systems use both.
Which LLM is best for my business?
There is no single best model. We test two or three on your own tasks and pick on accuracy, speed, cost and where data must stay, and we keep the code provider-neutral so you can switch later.
Can the AI respect who is allowed to see what?
Yes. We carry your document permissions into search, so people only get answers from content they are allowed to see.
Can you run a model in our own cloud?
Yes. We can deploy open-source models on your AWS, Azure or Google Cloud account, or use a provider's regional hosting, when data must stay inside your environment.
How do you measure accuracy?
We build a test set of real questions with known good answers and score every version against it, before and after launch.
Who owns the system?
You do: code, pipelines, prompts and evaluation sets.
Tell us what you want to build
A free 30-minute call with an engineer. We'll tell you what it takes, what it costs, and whether we're the right fit, even if we're not.