Chat with your data
Ask questions across PDFs, wikis, help centres, tickets and databases in plain language.
RAG & LLM DEVELOPMENT
AI that answers from your own data, accurately
We build retrieval-augmented generation (RAG) systems that let your team and customers search and chat with your documents, knowledge base and databases, with cited answers and access control built in.
Top rated on Freelancer- 20+ startups shipped
- You own 100% of the code
Overview
Turn your documents into answers your team can trust
Perfect for
- Support teams answering repetitive questions
- Companies with large document libraries
- SaaS products adding AI search or a copilot
Your company’s knowledge is spread across PDFs, wikis, help centres, tickets and databases. People waste hours searching for answers, and generic AI chatbots cannot help because they have never seen your data.
Retrieval-augmented generation (RAG) fixes that. We connect a language model to your own content, so it finds the most relevant passages first and answers from them, with citations to the original sources. The result is AI search and chat that is accurate, current and verifiable.
Quality comes from the details: how documents are split and embedded, how search combines keywords and meaning, how results are re-ranked and how access rules are enforced. We tune each of these to your content, measure accuracy with evaluation sets and can host everything privately in your own cloud.
Problems we solve
Sound familiar?
- 01
Staff spend hours searching across tools for the same answers.
One AI assistant that searches all your sources and answers with citations.
- 02
Your support team answers the same questions every day.
A RAG-powered help assistant that resolves common questions and hands off the rest.
- 03
A generic chatbot invents answers about your products.
Answers grounded in your own documents, with sources users can check.
- 04
Sensitive documents must stay restricted.
Permission-aware retrieval, so people only get answers from content they are allowed to see.
What’s included
Everything you need, nothing you don’t
Cited, grounded answers
Every answer links to its sources, so users can check it and trust it.
Smart retrieval
Chunking, embeddings, hybrid search and re-ranking tuned to your content for higher accuracy.
Access control
Users only get answers from documents they are allowed to see.
Always up to date
Automatic syncing keeps the knowledge base current as your documents change.
Measured quality
Evaluation sets and analytics track accuracy, gaps and cost over time.
Deliverables
- Data audit and source mapping
- Ingestion and embedding pipeline
- Vector search and retrieval tuning
- Chat or search interface
- Permissions and security
- Evaluation, monitoring and 30 days of free support
Tools and technologies
- OpenAI
- Anthropic Claude
- Open-source LLMs
- pgvector
- Pinecone
- Node.js
- Laravel
- Next.js
Process
How your project runs, week by week
Clear milestones, weekly progress updates and structured feedback rounds. Timelines are confirmed after a free discovery call, once we have reviewed your requirements.
- 1Days 1 to 3
Data audit
We map your sources, formats, access rules and the questions people actually ask.
- 2Week 1 to 2
Pipeline and retrieval
Ingestion, chunking, embeddings and hybrid search tuned on your content.
- 3Week 2 to 3
Interface and permissions
Chat or search interface, access control and integrations with your tools.
- 4Week 3 to 4
Evaluate and launch
Accuracy testing on real questions, launch, monitoring and 30 days of free support.
Pricing
Starting at
$2,000
Production Sprint: a working RAG system on your data in 2 to 4 weeks.
- Fixed quote, no hourly billing
- 30 days free post-launch support
- Free design concept before you commit
What affects the price
- Number and type of data sources
- Volume of documents and how often they change
- Permission and compliance requirements
- Hosting in your own cloud versus managed services
Production Sprint
from $2,000
2 to 4 weeks to a launch-ready product.
Growth Partner
from $1,500 / month
An ongoing team after launch.
FAQs
RAG & LLM Development questions, answered
Retrieval-augmented generation connects a language model to your own data. The system finds the most relevant content first, then the model answers from it, which makes answers more accurate and up to date.
Still have questions? Email our team.
Related services
All servicesReady to start your RAG & LLM Development project?
Book a free 30-minute strategy call. You will leave with a clear plan, a fixed quote and a confirmed timeline, whether or not you work with us.