Generative AI Development Services That Work in Production

Retrieval-Augmented Generation connects a language model to a knowledge base so that responses are based on retrieved documents rather than model training memory. The model knows what your knowledge base contains rather than what it learned during training. This makes the system accurate on your specific content, updatable without retraining, and auditable because every response can be traced to its source. For enterprise use cases where accuracy and auditability matter, RAG is the architectural baseline because it solves the two most common enterprise objections: hallucination and staleness.

Fine-tuning is the right choice when the problem is behavioral, not informational. When the model produces outputs in the wrong tone, style, or format regardless of the prompt. When it uses incorrect domain vocabulary. When it does not follow your internal structural conventions for a specific output type. RAG solves the information problem: what the model knows. Fine-tuning solves the behavior problem: how the model communicates. Many production systems use both: RAG for accurate knowledge retrieval, fine-tuning for appropriate output style.

A RAG system requires a structured collection of the documents you want the system to know: internal wikis, product documentation, policy manuals, support histories, technical specifications, or any other content the system should reference when answering questions. The documents need to be accessible digitally in formats that can be ingested, chunked, and embedded. The quality and coverage of the knowledge base directly determines the quality and coverage of the system’s answers. KrishaWeb conducts a knowledge base audit before architecture design to assess coverage gaps and quality issues.

For regulated industries and use cases involving sensitive data, KrishaWeb deploys GenAI systems in client-controlled infrastructure: private cloud, on-premise, or isolated cloud environments where data does not leave the client’s security perimeter. We do not use client data to train models for other purposes. Every deployment in a regulated environment includes a data handling specification that documents where data flows, how it is processed, and what access controls govern each component. HIPAA, GDPR, and sector-specific compliance requirements are addressed in the architecture design phase, not after deployment.

A well-scoped RAG system with a defined knowledge base and clear use case typically takes ten to fourteen weeks from architecture decision to production deployment. A fine-tuned model build typically adds four to six weeks for data preparation and training. An agentic system build typically takes fourteen to eighteen weeks because of the additional complexity in tool design, safety boundary testing, and orchestration framework configuration. The data preparation phase is often the longest and most variable element. We set a specific timeline in the architecture session based on your data state and system requirements.

Updating a RAG knowledge base after deployment is straightforward: new documents are ingested, chunked, and added to the vector store. The system reflects updated content in its responses without retraining the model. This is one of the primary advantages of RAG architecture over fine-tuning for knowledge-intensive use cases: the knowledge is decoupled from the model, so updates are operational rather than engineering tasks. KrishaWeb provides a knowledge base update runbook at handover so your content team can add, remove, and update documents without requiring engineering support.

KrishaWeb has delivered 2,400+ projects across 42+ countries with a 98% client satisfaction rate, verified by 54 independent reviews on Clutch (4.9/5). Our GenAI team combines 17+ years of software delivery experience with hands-on expertise in RAG systems, LLM fine-tuning, and enterprise AI integration. Clients consistently cite our discovery-first approach and production-ready architecture as key differentiators.