ENTERPRISE KNOWLEDGE / RAG

RAG systems that turn business knowledge into grounded answers.

Connect documents, databases and business systems to AI that retrieves relevant context, produces grounded responses and keeps answers tied to approved knowledge.

Build My Knowledge AI -> Explore Knowledge Systems
Connected Knowledge Sources Permission-Aware Retrieval Grounded & Verifiable Answers
knowledge://rag.pipeline.run
KNOWLEDGE
CORE SYSTEM READY
DOCUMENTS DATABASES CRM EMAIL WEB INTERNAL APPS
CONTEXT v REASONING v ANSWER ACTION

RAG DEVELOPMENT SERVICES

What is a RAG system?

Retrieval-augmented generation connects an AI model to selected business knowledge. It retrieves relevant sources, ranks and grounds the context, then produces an answer with citations and permission-aware access instead of guessing from a generic chatbot prompt.

THE KNOWLEDGE GAP

Your business data is everywhere.

PDFs
Notion
CRM
Emails
Spreadsheets
Databases
Support tickets
Internal tools
Websites
But can your AI actually understand it?
SEARCH -> INTELLIGENCE

Search finds information. Knowledge AI understands why it matters.

SEARCH
QUERY v KEYWORD v DOCUMENT v RESULT
INTELLIGENCE
QUESTION v UNDERSTAND INTENT v RETRIEVE CONTEXT v RANK RELEVANCE v REASON v GENERATE v VERIFY
RETRIEVAL-AUGMENTED GENERATION

Give AI the context it needs to reason.

USER QUESTION v QUERY UNDERSTANDING v RETRIEVAL v RERANKING v CONTEXT v LLM REASONING v GROUNDED RESPONSE
KNOWLEDGE INGESTION

Turn scattered business information into usable intelligence.

PDFDOCXCSVDATABASECRMEMAILAPIWEBSITEINTERNAL APP
SOURCEvINGESTvPARSEvCHUNKvEMBEDvINDEXvKNOWLEDGE LAYER
Raw documentsClean knowledge
SEMANTIC RETRIEVAL

Don't just match words. Find meaning.

Query

Which enterprise customers are at risk of churn?

System search
CRMSUPPORT HISTORYCONTRACTSEMAILSPRODUCT DATA
Ranking
RELEVANCE CONTEXT RECENCY AUTHORITY
STRUCTURED BUSINESS CONTEXT

Connect the relationships inside your business knowledge.

CUSTOMER
CONTRACT
PRODUCT
SUPPORT CASE
ACCOUNT OWNER
TRANSACTION
CONTEXT ENGINEERING

Better context creates better decisions.

RAW DATAvRELEVANT DATAvBUSINESS CONTEXTvTASK CONTEXTvAI REASONING
CONTEXT READY
  • USER INTENT
  • CUSTOMER
  • HISTORY
  • CURRENT STATE
  • POLICIES
  • RELEVANT DOCUMENTS
  • PREVIOUS ACTIONS
GROUNDED AI

Answers should be traceable.

AI ANSWER

Enterprise renewal risk is increasing
because of recent support activity and
contract changes.

SOURCES 01 CRM Record 02 Support History 03 Contract
GROUNDING? SOURCE TRACEAVAILABLE
PERMISSION-AWARE KNOWLEDGE

AI should know what it's allowed to know.

USERvIDENTITYvPERMISSIONSvKNOWLEDGE ACCESSvRETRIEVALvAI RESPONSE
Finance Team -> Financial Data ? Sales Team -> Sales Data ? External User -> Public Data only ?
KNOWLEDGE -> ACTION

Knowledge becomes powerful when AI can act on it.

QUESTIONvKNOWLEDGE RETRIEVALvCONTEXTvAI AGENTvREASONINGvBUSINESS SYSTEMvACTION
Customer asks questionvAI retrieves account contextvUnderstands contractvChecks CRMvGenerates responsevUpdates CRM
ENTERPRISE KNOWLEDGE USE CASES

Knowledge systems for every operating function.

01

Sales Intelligence

Research customers and surface relevant context.

02

Customer Support

Give agents immediate access to customer history and knowledge.

03

Internal AI

Search company knowledge through natural language.

04

Legal & Contracts

Retrieve and reason over contractual information.

05

Operations

Connect policies, procedures and operational data.

06

Research

Turn large information collections into actionable intelligence.

KNOWLEDGE SYSTEM ? OPERATIONAL
DOCUMENTS INDEXED 24,860 illustrative demo data
KNOWLEDGE SOURCES 08 connected systems
RETRIEVAL QUALITY 96% demo benchmark
GROUNDED RESPONSES 187 demo telemetry
ACTIVE QUERIES 03 live context
Activity Feed
  • Knowledge query received
  • -> Context retrieved
  • -> Sources ranked
  • -> AI reasoning started
  • -> Response verified
  • -> Answer delivered

RAG & KNOWLEDGE FAQ

Questions on Knowledge Systems.

Our ingestion pipelines process PDFs, Word documents, spreadsheets, Notion pages, CRM records, website content, Notion databases, and raw API data streams.

We enforce strict retrieval-grounded generation. Answers cite verified source documents, and models are restricted from speculating when source context is missing.

Yes. Role-based access control (RBAC) ensures users only retrieve answers from documents and data sources they are explicitly authorized to view.

Ingestion pipelines run on real-time webhooks, scheduled cron synchronization, or event triggers whenever underlying files or database records update.

No. We exclusively utilize enterprise APIs with zero data retention commitments, maintaining complete confidentiality of your IP.

MAKE YOUR KNOWLEDGE INTELLIGENT

Turn your company knowledge into an AI advantage.

We design knowledge systems that connect your data, context, AI agents, and business workflows.

Build My Knowledge AI -> Get an AI Knowledge Blueprint
DATAvKNOWLEDGEvREASONINGvAGENTvACTION

FAQS

RAG & Knowledge AI

Retrieval-Augmented Generation (RAG) is a technique that grounds an AI model's answers in your specific internal documents and databases, preventing hallucinations.

No. Your data is retrieved dynamically at query time and injected into the context window. It is not used to train the base model.

Yes, enterprise RAG systems enforce user-level permissions, meaning the AI only retrieves documents the specific user is authorized to see.

Fine-tuning is for teaching a model a specific tone or format. RAG is for giving a model access to accurate, up-to-date factual information.

Yes, RAG can ingest PDFs, internal wikis, Slack histories, and databases to create a unified search experience.

Disclaimer: AI systems can produce incorrect outputs. High-impact actions may require validation, permissions or human approval. Third-party API availability can affect workflow execution.