Advanced ML Models: RAG & LLM Development
Build AI assistants that answer from your own knowledge (policies, manuals, product data) accurately, with sources, and under your security controls.
- Python
- LangChain
- LlamaIndex
- OpenAI API
- Claude API
- Llama
Why teams choose QubixCode
- Years in IT services
- 10+
- Projects delivered
- 25+
- Skilled IT experts
- 15+
- Support & maintenance
- 24/7
What is RAG (retrieval-augmented generation)?
Retrieval-augmented generation (RAG) is a technique where an AI system first searches your own documents for relevant information and then uses a large language model to write an answer grounded in those sources. It reduces hallucinations, keeps answers up to date and lets users see citations.
Our RAG & LLM Development Services
- 01
Knowledge Assistants
Internal Q&A over policies, SOPs, wikis and tickets.
- 02
Customer Support Bots
Answer customer questions from product docs with escalation to humans.
- 03
Document Q&A
Ask questions across PDFs, contracts and reports with citations.
- 04
LLM Fine-Tuning
Adapt open-source models to your tone, format or domain.
- 05
Private Deployment
Self-hosted or VPC deployments for sensitive data.
- 06
Evaluation & Guardrails
Accuracy testing, prompt-injection defence and content filters.
Technologies We Use for RAG & LLM Development
- Python
- LangChain
- LlamaIndex
- OpenAI API
- Claude API
- Llama
- Vector DB
- PostgreSQL
Why Choose QubixCode for RAG & LLM Development?
Business outcome first
We start from the decision or workflow to improve, then pick the simplest model that delivers measurable value.
Your data stays yours
Private deployments, access controls and no training on your data without consent.
Production, not demos
Evaluation, monitoring, guardrails and cost controls so AI keeps working after launch.
Integrated into your apps
AI features delivered inside your web and mobile products, CRMs and internal tools.
Our Proven Process to Build Successful IT Solutions
A clear, result-driven process so every project is delivered on time, within budget and with maximum impact.
- 01
Discovery & Consultation
We understand your business goals, challenges and requirements through detailed consultation and research.
- 02
Planning & Design
We create a strategic roadmap, wireframes and UI/UX designs for a seamless user experience.
- 03
Development & Implementation
Our developers build scalable web, app, AI and e-commerce solutions using the latest technologies.
- 04
Testing, Launch & Support
Rigorous QA for performance, security and reliability before go-live, then 24/7 support and maintenance.
RAG & LLM Development FAQs
Quick answers about our services, hiring models, timelines and support. Can't find what you're looking for?
RAG vs fine-tuning, which do I need?
Use RAG when answers must come from your changing documents and cite sources. Use fine-tuning to change a model's style, format or specialised behaviour. Many solutions combine both.
How do you stop the AI from making things up?
We ground answers in retrieved sources, require citations, tune retrieval quality, add 'I don't know' behaviour and evaluate on real questions before launch.
Can the assistant respect user permissions?
Yes. Retrieval can be filtered by user roles so people only get answers from documents they are allowed to see.
Which LLM do you use?
We choose per use case: commercial models like Claude or GPT for top quality, or open-source models like Llama for private hosting and cost control.
More AI & Machine Learning services
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