FOR IMMEDIATE RELEASE

Indore, India, September 1, 2026 — Ment Tech Labs has expanded its RAG development services for enterprises that want AI systems to answer questions using trusted business information rather than relying only on a model’s general training data.

Retrieval-Augmented Generation, commonly known as RAG, connects a language model with selected company sources such as documents, databases, APIs, knowledge bases, support records, and internal applications. When a user asks a question, the system retrieves relevant information and supplies it to the model before an answer is generated. This approach can make responses more current, traceable, and useful for everyday business work.

As organizations compare RAG companies, the quality of the retrieval layer has become an important consideration. A polished chatbot interface cannot compensate for weak document preparation, poor search relevance, missing permissions, or inadequate evaluation. Ment Tech Labs addresses these areas through a production-focused service covering architecture, data preparation, retrieval engineering, model integration, testing, deployment, and continued optimization.

The service includes:

• RAG consulting and architecture design
• Custom RAG application development
• RAG-powered chatbot development
• Data pipeline engineering
• Hybrid retrieval and reranking
• Multimodal RAG for PDFs, images, tables, and charts
• Agentic RAG and GraphRAG development
• Enterprise connectors for CRMs, ERPs, APIs, and databases
• RAG evaluation and performance optimization

“A useful RAG system is not judged only by how fluent its answers sound. It must retrieve the right information, respect source permissions, show where an answer came from, and perform reliably under real business conditions,” said a spokesperson for Ment Tech Labs. “Our work starts with the knowledge and workflow the system needs to support, then builds the technical stack around those requirements.”

Ment Tech Labs follows a six-stage development process. The team first maps the use case, intended users, and approved data sources. It then cleans and structures the knowledge, builds the retrieval layer, connects the selected language model, evaluates the system using real queries, and deploys it with monitoring for ongoing improvement.

The company takes a model-agnostic approach and can integrate systems with OpenAI, Claude, Gemini, Llama, Mistral, and other suitable models. Its technology stack can include vector databases, embedding models, orchestration frameworks, cloud platforms, APIs, and observability tools selected according to each project’s data, security, scale, and performance requirements.

Security controls can be designed around the sensitivity of the information involved. Depending on the use case, this may include user-level permissions, source-level access rules, encryption, audit logs, private deployment, secure credentials, and monitoring. Evaluation can measure retrieval relevance, answer accuracy, groundedness, latency, and citation quality before and after release.

The services are intended for organizations building enterprise knowledge search, document question-answering tools, support assistants, internal copilots, research applications, and workflows that need accurate responses from multiple information sources.

Businesses reviewing RAG companies can learn more about Ment Tech Labs’ capabilities at:

https://www.ment.tech/rag-development-services/

About Ment Tech Labs

Ment Tech Labs is a technology development company that builds AI systems, enterprise software, blockchain platforms, and digital products for businesses across multiple markets. Its RAG development work focuses on connecting business knowledge with secure retrieval, suitable language models, and production-ready applications.

Media Contact

Ment Tech Labs
Email: [[email protected]](mailto:[email protected])
Phone: +91-74798-66444
Website: https://www.ment.tech/

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