AI CONSULTING & ENGINEERING

RAG development for enterprise knowledge.

Connect AI assistants to your documents and systems, with source-linked answers, access controls and evaluated retrieval.

Discuss your use case

BUILT AROUND YOUR BUSINESS

Kaizenix builds retrieval-augmented generation systems for teams that need useful answers from their own information. Our RAG development services cover document ingestion, search, answer generation, evaluation and enterprise integration, serving businesses across the US, Europe, APAC and Japan from Bengaluru.

A RAG application retrieves relevant source material before generating an answer. This can make business knowledge easier to use without relying only on a model's pre-existing training. Reliable answers still depend on document quality, retrieval design, permissions and evaluation. We design the workflow around those requirements and give users a way to inspect the supporting sources.

Where can enterprise RAG help?

Internal knowledge assistants. Help employees find information in policies, manuals and approved technical records. Apply the user's access permissions when selecting sources.
Document-heavy operations. Connect scanned records, tables and multilingual documents to search and extraction workflows. English and Japanese document handling can be assessed during discovery.
Customer and service support. Give support teams a searchable, maintained knowledge base with source references and escalation when the system cannot support an answer.

From assessment to a working system.

Map sources and permissions. Identify repositories, document owners, access boundaries and update frequencies. Define questions the assistant should answer and cases it should decline or escalate.
Build the retrieval pipeline. Prepare documents, evaluate OCR where needed, structure chunks and metadata, and compare keyword, vector and hybrid search with reranking where useful.
Evaluate answers and failure modes. Test retrieval relevance, source attribution, answer faithfulness and prompt-injection handling. Use a representative question set and human review, with explicit limits on what the assistant may do.
Integrate and maintain. Connect the assistant to the agreed interface and enterprise systems. Define re-indexing, deletion, monitoring, feedback and ownership so source changes can be reflected in the application.

What the engagement can deliver.

Scope, acceptance criteria, security requirements and deliverables are agreed before delivery begins.

A knowledge-source and access-control map
An ingestion, retrieval and answer-generation pilot
A documented evaluation set and quality findings
An operating plan for content freshness, monitoring and handover

Questions before you start.

Does RAG eliminate hallucinations?

No. Retrieval and citations can improve grounding, but incorrect retrieval or generation can still produce unsupported answers. We evaluate these failure modes and design source display, refusal and human review around the risk of the task.

Can RAG connect to existing company systems?

Yes, subject to the APIs, permissions and data quality available. We scope integrations with document repositories, CRM, ERP and other knowledge sources, including how access changes and deletions propagate.

Do we need a vector database?

Not necessarily as a separate product. The architecture may use an existing search platform, database vector capabilities or a dedicated retrieval service. Selection depends on scale, filtering, update patterns, cost and evaluation results.

How do you choose between RAG and fine-tuning?

RAG is generally the starting point for answers based on changing business documents. Fine-tuning addresses model behaviour or a specialized task. We compare the options against your evaluation criteria instead of assuming either is always required.