RAG development services connect AI applications to the documents, knowledge bases, and systems where your answers already live, so people get relevant, context-aware responses instead of searching tool by tool. Wizr AI builds ingestion pipelines for structured and unstructured data, designs retrieval over your company knowledge, and delivers the copilot, assistant, or search experience your teams use, with contextual search and Q&A over enterprise knowledge bases. Integrations with systems such as Salesforce, ServiceNow, Zendesk, Jira, Confluence, and Google Docs bring knowledge into the flow of work. For a leading logistics SaaS firm, Wizr AI agents deflected 43% of support tickets and delivered 50% faster response times.

Custom RAG development grounds AI responses in your own approved content, so answers reflect your products, policies, and processes instead of general training data. Wizr AI designs retrieval around your knowledge sources, adds the most relevant retrieved context to the prompt, and evaluates accuracy, latency, and grounding before and after launch. RAG reduces the risk of hallucination, but it does not remove it on its own, so we add golden test cases, route low-confidence answers to human review, and keep optimizing prompts, models, and knowledge sources. For a leading US automotive firm, targeted optimization of the prompt, model, and knowledge base lifted dealer support resolution from roughly 25% to the 70% range.

Moving RAG into production usually means fixing data quality, retrieval relevance, integration, and measurement, not just the model. Wizr AI starts by establishing a baseline, then uses accuracy diagnostics to find where answers fail and optimizes the prompt, model, and knowledge base against that baseline. We connect the application to the systems your users rely on and build in security and governance so it can pass review. For an automotive client whose existing AI agent was stuck at roughly 25% resolution, a three-month monitoring and optimization program raised resolution to the 70% range and saved $2.1 million a year.

Wizr AI is SOC 2 Type II and ISO 27001 certified and supports GDPR compliance, and secures RAG solutions through a framework built on five pillars: least-privilege data access control, end-to-end encryption for data at rest and in transit, continuous monitoring and auditing, security by design, and secure data processing. Access to sensitive data is restricted through robust access controls and least-privilege principles, helping minimize exposure. Once in production, our AI governance services monitor risk dimensions including PII exposure, policy violation, hallucination, and unauthorized actions, with human oversight where your business requires it.

Yes. Enterprise RAG solutions work best when they retrieve from the systems your teams already use. Wizr AI integrates AI agents and agentic workflows with CRM, ITSM, collaboration, and productivity systems, including Salesforce, ServiceNow, Oracle Fusion, Zendesk, Jira, Confluence, Slack, Google Docs, Gmail, and Outlook, using APIs, SDKs, and reusable components. In Salesforce environments, we integrate RAG, vector search, and unstructured data analysis with minimal disruption to your existing setup. We can also design custom tools so AI agents securely interact with your applications and data, and build real-time or batch ingestion pipelines for sources that need them.

RAG accuracy is measured continuously against agreed test cases, not assumed after launch. Wizr AI evaluates accuracy, latency, and grounding during development, uses golden test sets and baselines, and keeps measuring after go-live. When accuracy drops, we update prompts and RAG sources, add new golden tests, and route low-confidence cases to human review. Our AI governance services track accuracy, cost, latency, risk, drift, and business outcome for every AI agent, so quality degradation doesn’t go unnoticed until it becomes a customer complaint. This is how one automotive client moved from roughly 25% to the 70% range in resolution rate, with improvements re-measured weekly.

The right approach depends on the problem. RAG suits use cases that need answers from changing business knowledge, because content can be updated without retraining a model. Fine-tuning suits cases where a model must learn a specific style, format, or domain behavior. AI agents suit tasks that need reasoning across steps and actions in business systems, and agentic RAG combines retrieval with those agents. Wizr AI offers RAG development, fine-tuning of LLMs and AI models, and AI agent development, so our consultants can recommend the right mix for each use case. Read our guide to RAG vs. fine-tuning LLMs for more detail.

Agentic RAG solutions let AI agents decide what to retrieve, use tools, reason across multiple inputs, and take actions, instead of answering from a single retrieval step. Wizr AI builds AI agents and agentic workflows that orchestrate LLMs, embeddings, tools, and functions, including multi-agent workflows where specialized agents share context under a supervisory agent. Agents can securely interact with business applications through pre-built or custom tools, with human oversight where needed. This suits complex questions that span several systems or require follow-up actions, such as updating a ticket after finding the answer. Learn more about agentic RAG for the enterprise.

Enterprise RAG solutions work best where people repeatedly need accurate answers from large or changing bodies of knowledge. Common examples include enterprise search and Q&A over policies and documentation, customer support agent assist and auto-solve, IT service desk assistance that brings together knowledge from across enterprise systems, legal assistance that identifies relevant policies and guidelines, and data analyst assistance that turns natural-language questions into SQL. In pharma, Wizr’s specialized AI agents draw from existing source documents to accelerate regulatory authoring with traceability to source content, reducing authoring time by 70%. Wizr AI helps you prioritize the use case with the clearest measurable value first.

Custom RAG timelines depend on the number and quality of knowledge sources, integration complexity, security and compliance requirements, and how much of the solution can reuse existing components. A single knowledge assistant over well-structured content and a multi-agent workflow across several business systems run on very different schedules, so we don’t give one-size-fits-all estimates. Wizr AI starts by agreeing the use case, data sources, and success measures, so you have a realistic plan before full development begins. Pre-built Wizr AI Assembly components, which we configure to your processes, can help deliver measurable value within weeks.

The cost of RAG development services depends on the scope of the use case, the number and condition of data sources, the integrations required, security and compliance requirements, evaluation and monitoring needs, and the engagement model you choose. A focused assistant over one knowledge base costs far less than an agentic workflow across several systems. Wizr AI scopes these factors during use case assessment, so you can compare options before committing. Reusing pre-built AI Assembly components can reduce build effort, and ongoing optimization of models and workflows helps control operating cost after launch, including right-sizing models, caching, and reducing duplicated agent calls.

Look for a RAG development company that can take you from pilot to production, not just build a demo. Ask how the partner prepares and ingests your data, how retrieval respects user access rights, how accuracy and grounding are measured before and after launch, and how low-confidence answers are handled. Check for integration experience with your systems, verified security credentials such as SOC 2 Type II and ISO 27001, and real production results. For enterprises and SaaS companies alike, Wizr AI brings data engineering, RAG and agentic AI development, continuous evaluation, enterprise integrations, and AI governance, backed by results such as 43% of support tickets deflected for a logistics SaaS firm.

Talk to an AI expert on our team. Share the use case you have in mind, the knowledge sources and systems involved, your users, and your security and compliance requirements. We’ll help you assess data readiness, recommend the right RAG approach and engagement model, and define how success will be measured, so you can see how the work would run before committing. A practical starting point is often one high-value knowledge workflow, such as support, IT service desk, or internal policy search, with the program expanding once it proves its value. You can also explore our broader Enterprise AI Services.