Devionary
Generative AI

Create, Innovate, and Scale with GenAI

Move beyond the chat interface. We integrate state-of-the-art Large Language Models (LLMs) directly into your applications to automate content creation, summarize vast information, and provide intelligent answers.

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Your Data, Your AI

Generic AI models are powerful, but they don't know your business. They hallucinate facts and lack context. The real value comes from grounding these models in your own data.

We specialize in Retrieval-Augmented Generation (RAG). This technique allows AI to "read" your internal documents—PDFs, wikis, databases—before answering a question. The result is an AI that speaks your language, follows your guidelines, and cites its sources, all without the massive cost of training a model from scratch.

At Devionary, we build secure GenAI applications. We handle the complexity of vector databases, prompt engineering, and model orchestration so you can focus on the user experience.

Our GenAI Capabilities

Custom AI Chatbots

Intelligent assistants trained on your support docs. They handle complex queries, reduce ticket volume, and operate 24/7.

RAG Implementation

Connecting LLMs to your private data (Notion, Google Drive, SQL) using vector databases like Pinecone or Weaviate.

Content Generation

Automating the creation of blog posts, product descriptions, emails, and social media copy in your brand voice.

Summarization Engines

Instantly distilling long reports, meeting transcripts, and legal contracts into concise executive summaries.

Image Generation

Integrating models like DALL-E 3 or Stable Diffusion to generate custom assets for marketing and design.

Code Copilots

Building internal developer tools that understand your codebase and suggest improvements or write unit tests.

GenAI tech stack

Models (LLMs)

  • • GPT-4o (OpenAI)
  • • Claude 3.5 Sonnet (Anthropic)
  • • Llama 3 (Meta / Open Source)
  • • Mistral Large

Orchestration

  • • LangChain / LangGraph
  • • LlamaIndex
  • • Vercel AI SDK
  • • Microsoft Semantic Kernel

Vector Databases

  • • Pinecone
  • • Weaviate
  • • Supabase (pgvector)
  • • ChromaDB

Related Services

AI Agents

Autonomous AI

Data Engineering

Prepare your data

Python Development

Core AI language

FAQ

GenAI Questions Answered

Common questions about implementing Generative AI in your business.

ChatGPT is a general-purpose tool. A custom GenAI solution is built specifically for your business, integrated with your internal data (via RAG), and designed to follow your specific brand guidelines and security protocols. It lives within your own infrastructure or secure cloud environment.
We prioritize security by using private instances of models (like Azure OpenAI) or open-source models (like Llama 3) hosted on your own servers. We ensure that your data is never used to train public models and implement strict access controls and data sanitization pipelines.
Retrieval-Augmented Generation (RAG) is a technique that connects an AI model to your live data sources (documents, databases, wikis). This allows the AI to answer questions based on your specific company knowledge rather than just general internet data, significantly reducing hallucinations.
We can typically build a functional Proof of Concept (PoC) in 2-4 weeks. This allows you to validate the value proposition with real users before committing to a full-scale production rollout.

Ready to innovate with GenAI?

Let's build something that transforms your industry.

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