AI Integration

An AI Studio for RAG Systems That Give Your Business a Brain.

We build retrieval-augmented generation systems that connect large language models to your business data, so your AI gives accurate, context-aware answers instead of generic ones.

Whether it's a knowledge base, a document library, or a product catalogue, we structure your data and build the pipeline that makes it instantly queryable through natural language.

An AI Studio for RAG Systems That Give Your Business a Brain.

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What we bring to the table

RAG systems engineered to make your business data intelligent and instantly accessible

Custom RAG Pipeline Development

End-to-end retrieval and generation systems built around your data sources and use case.

Document & Knowledge Base Ingestion

Structuring and indexing your existing documents, PDFs, and content for accurate retrieval.

Vector Database Integration

Connecting your data to vector stores like Pinecone or Qdrant for fast, semantic search.

LLM Integration

Connecting retrieval pipelines to the right language model for your accuracy and cost requirements.

Internal Knowledge Tools

Giving your team instant access to company knowledge through a natural language interface.

Accuracy & Hallucination Control

Grounding every response in your actual data so the system doesn't make things up.

Why Konvoy Studio Is the Right Team to Build Your RAG System

RAG Systems EXPERTS

Konvoy×RAG Systems

EXPERTISE

5+ Years Active

QUALITY

Zero Compromise

A language model without access to your data gives generic answers. RAG fixes that, but only when the pipeline is built properly. Poor chunking, weak retrieval, and wrong model choices all produce confident answers that are simply wrong.

At Konvoy Studio, we build RAG systems with the full stack in mind. Data preparation, embedding, vector storage, retrieval logic, and LLM integration are all handled by a team that has shipped these systems in production.

If you want AI that actually knows your business and gives accurate answers from your data, let's build the right system for your use case.

Global Footprint

World Domination5%

Active Client Region

Untapped Potential

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Trusted byfounders.

Here's what our clients say about our collaboration. Their satisfaction and meeting expectations are our top priorities, ensuring the best experience possible.

Working with Konvoy was smooth start to finish. They listened, understood what I wanted, and delivered better than I imagined.

Jo Edwards

Developer — Shape

Working with Konvoy Studio was an absolute pleasure. Their creativity, attention to detail, and high quality work exceeded every expectation.

Jeremy Bennett

Podcaster — Studio-X

Working with Konvoy was smooth start to finish. They listened, understood what I wanted, and delivered better than I imagined.

Mark Zicha

Founder — Wacky Puppies

Konvoy Studio delivered outstanding work with clear communication. The final product exceeded every expectation.

Milan Patel

Career & Life Design Coach

Architectural integrity was maintained through every phase of implementation into elegant modules without bloat.

Mary Camacho

Executive Director — CEO at Holo

Visual project design reference for client Jeremy Bennett

Visual project design reference for client Mark Zicha

Visual project design reference for client Milan Patel

Visual project design reference for client Mary Camacho

FAQs

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Questions we get asked the most.

RAG stands for retrieval-augmented generation. It connects an AI language model to your own data so it can give accurate, specific answers instead of relying on general training knowledge alone.

PDFs, Word documents, spreadsheets, website content, knowledge bases, product catalogues, internal wikis, and most other structured or unstructured text sources.

We work with Pinecone, Qdrant, Weaviate, and pgvector depending on your infrastructure and requirements.

We integrate with OpenAI, Anthropic, Google, and open-source models via Ollama or Hugging Face, depending on your accuracy, cost, and data privacy requirements.

Through proper chunking strategies, retrieval tuning, and grounding techniques that constrain the model to only answer from retrieved context.

Simple RAG pipelines can be delivered in two to three weeks. More complex systems with multiple data sources and custom interfaces take four to eight weeks.