Turn Enterprise Knowledge Into Grounded AI.
Connect SOPs, policies, contracts, engineering standards and internal knowledge to a Retrieval-Augmented Generation (RAG) solution that retrieves relevant information, generates contextual answers and surfaces the sources behind the response.
Built for US businesses that need more than a generic chatbot — with use cases across HR, compliance, engineering, manufacturing, healthcare and municipal operations.
Based on the current approved policy, the request should follow the documented leave approval workflow and required documentation.
Retrieval-Augmented Generation, Built for Real Business Knowledge
Retrieval-Augmented Generation (RAG) combines enterprise document retrieval with AI language understanding to deliver contextual, citation-backed answers from approved business content. Instead of searching PDFs, folders and intranets manually, teams can ask questions in natural language.
Your Business Has the Answers. Your Teams Just Can't Find Them Fast Enough.
Enterprise knowledge often lives across SOPs, policies, contracts, PDFs, SharePoint, Confluence, Google Drive, help centers and internal systems. A RAG assistant turns that distributed knowledge into a conversational experience.
Make Enterprise Knowledge Searchable, Conversational and Actionable
RAG is useful when answers depend on changing, proprietary or domain-specific information. Instead of expecting a foundation model to know your internal content, retrieval connects the assistant to the knowledge your organization actually uses.
Grounded Answers
Generate responses using relevant enterprise content rather than relying only on model memory.
Source Citations
Surface the documents and sections supporting an answer to improve trust and verification.
Fresher Knowledge
Update the knowledge layer as approved policies, procedures and documents change.
Natural-Language Search
Let employees and customers ask questions without learning complex search syntax.
Role-Aware Retrieval
Design retrieval and access patterns around appropriate users, sources and workflows.
AI-Powered Workflows
Move from answers toward recommendations, escalation and next-best-action experiences.
From Your Documents to a Citation-Backed AI Answer
A production RAG assistant connects approved knowledge sources, retrieves the most relevant passages for a user question, and uses those passages to generate a contextual response.
Connect
Bring in SOPs, policies, contracts, specifications, FAQs and approved enterprise sources.
Retrieve
Find relevant passages using semantic retrieval and your chosen knowledge architecture.
Generate
Use retrieved context to create a natural-language answer grounded in approved information.
Act
Show citations, recommendations and next actions so users can verify and move forward.
RAG RAG Solutions Built Around the Work Your Teams Actually Do
Performix can design RAG experiences around specific roles, documents and workflows instead of forcing every department into the same generic chatbot.
HR & People Operations
Make policies, benefits and onboarding knowledge easier for employees to access.
- Policy assistant
- Benefits Q&A
- Employee onboarding
- Forms & procedures
Engineering & Infrastructure
Give technical teams faster access to specifications, standards and project knowledge.
- Engineering documentation
- Technical standards
- Contracts
- Permitting resources
ISO & Compliance
Make controlled documents and quality knowledge easier to retrieve and verify.
- SOP assistant
- QMS knowledge
- Audit documentation
- Corrective actions
Government & Municipal
Bring standards, zoning, public works and operational knowledge into a conversational interface.
- Municipal operations
- Zoning information
- Engineering standards
- Citizen support
When Is RAG the Better Enterprise AI Architecture?
RAG is particularly useful when the answer depends on private, changing or domain-specific knowledge that users need to verify. The right architecture depends on the business use case.
| Capability | RAG Assistant | Generic LLM | Fine-Tuned LLM |
|---|---|---|---|
| Grounded in enterprise documents | Yes | Not by default | Depends |
| Source citations | Designed for it | Not inherent | Not inherent |
| Dynamic knowledge updates | Strong fit | Requires external context | Retraining/update cycle |
| Domain-specific Q&A | Strong fit | Limited by context | Possible |
| Compliance / controlled knowledge | Strong fit | Requires controls | Requires controls |
| Knowledge retrieval | Core capability | Not inherent | Not inherent |
Build RAG for Enterprise Trust — Not Just Enterprise Answers
Enterprise RAG should be designed around the organization's security, access, governance and data requirements. Performix can architect appropriate controls and deployment patterns based on the use case and environment.
RBAC & Access
Design retrieval and application access around user roles and authorized knowledge sources.
SSO & Identity
Support enterprise identity patterns where required by the application environment.
Auditability
Plan for logging, traceability and operational visibility across AI interactions.
Governance
Establish approved sources, ownership, update processes and AI usage policies.
Deployment Options
Evaluate cloud, VPC or other deployment patterns based on security and integration needs.
Responsible AI
Include human oversight, source grounding and appropriate escalation for high-impact workflows.
RAG Is More Than Retrieval. It Is Your Enterprise AI Knowledge Layer.
Modern enterprise RAG can connect retrieval, generative AI, agents, orchestration, APIs and business systems to create a more capable AI experience.
Generative AI
Natural-language generation grounded in retrieved business context.
RAG
Retrieval pipelines that connect AI responses with enterprise knowledge.
AI Agents
Extend assistants toward multi-step workflows and task-oriented interactions.
Agentic AI
Design AI workflows that reason over context and coordinate appropriate actions.
Enterprise APIs
Connect assistants to applications, systems and business workflows.
AI Governance
Build governance, observability and responsible-use practices into the architecture.
RAG Implementation Cost Depends on the Business Problem
A RAG proof of concept and a production enterprise knowledge system are very different projects. Scope, data sources, integrations, security requirements, compliance needs and multi-tenant architecture all affect implementation effort and cost.
Proof of Concept
Typical scope from the supplied planning brief.
- Focused use case
- Selected knowledge sources
- Initial retrieval architecture
- 6–10 week planning range
Production RAG
For production-grade enterprise deployments.
- Multiple knowledge sources
- Security & access controls
- Application integration
- Monitoring and operations
Enterprise Agentic RAG
Range depends heavily on sources, compliance and multi-tenant requirements.
- Agentic workflows
- Complex integrations
- Enterprise governance
- Advanced automation
These are planning ranges from the supplied landing-page brief, not fixed Performix quotes. Final pricing should be determined after demo and technical scoping.
How Performix Can Help You Build a RAG Assistant
Define the business problem, users, knowledge sources and success criteria.
Design retrieval, AI, integration, security and governance requirements.
Connect knowledge, build the assistant experience and integrate required systems.
Measure usage, answer quality and business outcomes, then improve continuously.
Questions Enterprise Buyers Ask About RAG
Different RAG Challenges. Different Outcomes.
The value of RAG depends on what the organization is trying to improve — from finding trusted information faster to giving technical teams better access to knowledge.
Our challenge was less about finding information and more about trusting that people were using the right version. Performix brought the retrieval layer into the conversation in a way that made sense for our existing knowledge base. The ability to surface relevant documents alongside an answer gave our teams much more confidence in the information they were using.
We had years of professional knowledge sitting across documents that were difficult to search consistently. The Performix team took a practical approach to the RAG architecture and focused on how people would actually use it. Being able to ask a question naturally and trace the response back to its source made the experience far more useful than a conventional document search.
We initially looked at RAG as a way to improve internal knowledge access. What surprised us was how quickly it connected to larger AI opportunities. Performix helped us see how grounded retrieval could become the knowledge layer for recommendations and future workflow automation, rather than treating RAG as just another search interface.
Not Sure Which AI Approach Fits?
RAG is one part of an enterprise AI strategy. Explore the related Performix services to see which path best matches your business challenge.
Artificial Intelligence
Explore the broader AI capabilities and solutions Performix offers for business applications and transformation.
Explore AI → Strategy & ImplementationAI Consulting
Start with the business problem, evaluate the right AI approach and define a practical path from discovery to deployment.
Explore AI Consulting → Beyond AnswersAgentic AI & AI Agents
When your use case needs AI to reason through tasks, coordinate steps or take action, explore the agentic approach.
Explore AI Agents → Trust & ControlAI Governance & Compliance
Build governance, risk and responsible-use considerations into your AI architecture as your deployments grow.
Explore AI Governance →Ready to Turn Your Enterprise Knowledge Into an RAG Solution?
Bring your SOPs, policies, contracts, engineering documentation or internal knowledge to a conversation with the Performix team. We will help identify where RAG can create measurable business value.
Book a Demo →Prefer to send a message? Contact Performix →
See Performix RAG in Action
Connect your enterprise knowledge to AI and get grounded, source-backed answers from the information your teams already use.
Drop a Request To Get a RAG Demo →