Marry the power of AI with the facts you own — for responses that are relevant, accurate, and grounded.

Let Your AI Speak Facts, Not Just Patterns

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Our Process

We follow a structured, yet informal 4-step consultation format

Knowledge Source Mapping

We identify where your most valuable content lives — PDFs, Confluence, Google Drive, Notion, Zendesk, SQL DBs, SharePoint — and define retrieval logic.

Indexing & Chunking

Your documents are broken into logical “chunks” (semantic units) and embedded using vector databases like FAISS, Pinecone, or Weaviate, so the system can understand meaning, not just keywords.

Model + Retriever Integration

We connect a retriever module (like Elasticsearch or vector DBs) with a language model like GPT, Claude, or open-source LLMs — forming the RAG loop.

Contextual Output Tuning

We add formatting rules, citation references, response filters, and escalation paths — so answers are not just smart, but business-ready.

RAG Model Development

Smarter AI Through Retrieval and Generation

  • Tech Stack Language

    Powerful coding foundations that drive scalable solutions.

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    JavaScript

    Python

    Python

    TypeScript

    TypeScript

  • Framework Extensions

    Enhanced tools and add-ons to accelerate development.

    LangChain

    LangChain

    Haystack

    Haystack

  • Cloud Services

    Secure, flexible, and future-ready infrastructure in the cloud.

    SageMaker

    AWS Sagemaker

    AWS

    AWS (Amazon Web Services)

    Google Cloud

    Google Cloud Platform (GCP)

  • LLMs & GenAI

    LLMs & GenAI

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    OpenAI

    Anthropic

    Anthropic

    google-gemini

    Google Gemini

    llama

    Meta LLaMA

    mistral

    Mistral

Tech Talk

Developer Tips & Insights

RAG Model Development

Smarter AI Through Retrieval and Generation

Our RAG Model Development services combine retrieval-based systems with generative AI models to provide accurate, context-aware, and dynamic outputs. By leveraging knowledge retrieval, vector databases, and language models, we build solutions that answer complex queries, summarize documents, and generate insightful responses. Ideal for enterprise search, knowledge management, and AI-powered assistants, RAG models enhance AI capabilities by grounding generative outputs in real-world data.

A RAG model combines retrieval-based methods with generative AI to produce responses grounded in external knowledge sources.
Use cases include enterprise search, document summarization, AI assistants, and knowledge management systems.
By retrieving relevant information from trusted sources before generating answers, RAG models reduce hallucinations and improve reliability.

Marry AI Power With Truth and Trace

Deliver search and retrieval systems that link content, workflows, and cloud data, ensuring every AI output is trustworthy, accurate, and grounded.