Movya provides custom RAG development services to connect large language models with private documents, databases, APIs, knowledge bases, and business applications. We design and develop production-ready RAG systems that retrieve relevant business information and provide context-aware AI responses grounded in approved data sources.
Retrieval-Augmented Generation (RAG) is an AI architecture that allows a large language model to retrieve relevant information from external knowledge sources before generating a response.
Dynamically queries vector databases, enterprise search indexes, and document repositories to pull the most relevant facts for every user prompt.
Appends the retrieved private context, access constraints, and document references directly into the LLM prompt window with zero context leakage.
Synthesizes accurate, hallucination-free answers backed by verifiable source citations and page annotations in real time.
Instead of costly, slow model retraining, RAG queries your files in real time to fetch context for the AI. This architecture completely eliminates hallucination vectors by anchoring every answer in verifiable source documents. Your data remains secure on your cloud infrastructure, and updates are reflected instantly without manual fine-tuning loops.
How private documents are processed, indexed, and retrieved in real-time.
Our engineering lifecycle focuses on document mapping, robust chunk retrieval accuracy, and custom linter evaluation.
Phase 1
Analyze your proprietary data sources (PDFs, Notion, SQL databases, Confluence) to audit layout structures, OCR quality, table densities, and metadata requirements. We define the security classification boundaries and compliance rules (HIPAA/GDPR) before ingestion.
Audit
Phase 2
Configure advanced document parsers (e.g., LlamaParse) to extract embedded tables, images, and hierarchy. We design parent-child chunk relations, semantic splitting rules, and token overlap margins to preserve contextual references.
Structure
Phase 3
Select and fine-tune domain-specific embedding models (OpenAI, Cohere, HuggingFace). We index the chunks into high-performance vector databases (Pinecone, pgvector, Weaviate) with optimized HNSW indexes for sub-10ms query execution.
Ingestion
Phase 4
Deploy hybrid search structures merging semantic vector lookups with lexical BM25 matching. We implement Cohere or BAAI re-ranking models to filter the top-k context fragments, ensuring the LLM receives only the most relevant context.
Search
Phase 5
Establish secure connections to enterprise-grade models (GPT-4o, Claude 3.5 Sonnet, Llama 3) via private APIs or local setups (Ollama). We engineer system instructions that enforce strict grounding, forcing models to cite sources and refuse to answer if facts are missing.
Synthesis
Phase 6
Set up automated evaluation suites using RAGAs to score faithfulness, answer relevance, and context recall. Integrate real-time observability tools (Arize Phoenix, LangSmith) to track query drift, model latency, and token consumption.
Evals
Explore custom architectures implemented by our engineers to index documents, configure databases, and execute secure queries.
Chat directly with your internal files.
Connect complex PDF manuals, technical guidelines, and onboarding documents to a responsive chat interface. Enable employees and customers to find precise answers backed by page citations.
Modernize static search directories instantly.
Replace keyword matches with intelligent semantic search. Enable your users to retrieve relevant help center articles or wiki guidelines even when using natural phrasing and typos.
Query databases using plain english statements.
Convert text questions into secure SQL queries on structured systems. Empower business leaders to extract real-time database numbers and reports without writing database commands.
Connect dynamic document streams securely.
Ingest live chats, webhooks, and updated project files on the fly. Ensure your model references up-to-the-minute data logs without needing manual pipeline re-indexing.
Combine keyword search with semantic intent.
Retrieve data using combined vector embeddings and traditional BM25 search logic. Optimize your accuracy rate when users look up exact model numbers or custom business acronyms.
Track accuracy and eliminate hallucination.
Run continuous automated benchmarks against query drift and token costs. Track latency metrics and verify response grounding index scores before outputs reach users.
See how different sectors implement secure retrieval networks to ground outputs and drive efficiency.
Audit contracts, search legal precedents, and check compliance drafts against guidelines. Ensure complete accuracy with verified source document link citations.
Access clinical trials and institutional medical wikis to assist diagnostic decisions. Keep sensitive health information secure under private cloud infrastructure.
Connect scattered Notion pages, Slack histories, and system databases. Enable your staff to retrieve internal operational documents in seconds.
Answer detailed product compatibility questions using technical manuals. Link users directly to purchase sheets based on product capabilities.
Create personalized study tools grounded strictly in textbooks and lecture notes. Prevent incorrect answers and maintain institutional standards.
Analyze quarterly earnings reports, market studies, and portfolio compliance sheets. Synthesize financial reports with exact reference annotations.
We prioritize security by hosting vector databases and language models locally or in your private cloud, keeping sensitive data within your compliance boundaries.
We utilize automated RAGAs checks and evaluation datasets to benchmark hallucination rates and verify citation accuracy before rollout.
We integrate retrieval pipelines directly into your operational stack, including web apps, mobile apps, and CRMs using modular APIs.
Ready to deploy a secure retrieval system? Let's connect to review your documents and establish a robust, low-latency pipeline built directly into your application stack.
Consult with our AI engineering experts for private model options.
Start Your RAG PipelineCommon questions about custom RAG architectures, chunking strategies, vector databases, accuracy metrics, and private data integrations.
Retrieval-Augmented Generation (RAG) is an AI architecture that retrieves relevant information from external knowledge sources and provides that context to a language model before it generates a response.
RAG development involves designing and building systems that ingest data, parse and chunk documents, create embeddings, index information, retrieve relevant context, and connect the retrieved context with an AI model.
RAG is useful when an AI application needs access to private or frequently changing information. Fine-tuning is used for different purposes, such as adapting a compatible model to specific task patterns or behavior.
Depending on the architecture, RAG systems can work with PDFs, documents, databases, APIs, knowledge bases, websites, spreadsheets, support content, and other structured or unstructured data sources.
Depending on the project, RAG systems can use technologies such as Pinecone, pgvector, Weaviate, Qdrant, or other suitable vector and hybrid search technologies.
Yes. RAG systems can be integrated with supported databases, APIs, applications, document repositories, and business systems using appropriate data-access and security controls.
Yes. RAG architectures can include synchronization pipelines, event-driven updates, APIs, webhooks, and scheduled ingestion processes so that retrieval sources can be updated as business data changes.
RAG systems can be evaluated using metrics such as retrieval relevance, context recall, answer relevance, faithfulness, citation accuracy, latency, and cost, using representative evaluation datasets.
Depending on the selected models, infrastructure, and project requirements, RAG systems can be deployed in private cloud or on-premise environments.
Yes. Movya is headquartered in Ahmedabad, Gujarat, India, and provides RAG, LLM, AI agent, and AI development services for businesses in India and global markets.