What is Enterprise Data Platform - Enterprise Data Platform

Welcome to the FPT.AI Enterprise Data Platform (EDP) documentation. An agent only answers correctly when it reads the right documents. EDP is where a company’s documents - Processes, policies, legal texts, contracts, templates - Are brought into one place, turned into searchable knowledge, and served to every agent system through one common interface. It is not a document Q&A chatbot; it is the data layer that sits behind all of your agents.

Why a separate data layer

Most companies feed documents to AI like this: Each team builds its own retrieval for its own agent, documents are copied into many places, and nobody knows which version is current. When an agent answers wrongly, nobody knows where it went wrong. EDP changes that:

Companies today	Companies on EDP
Each agent has its own copy of the documents	One source, many knowledge bases, shared by every authorized agent
A wrong answer could be the document, the chunking or the model - Nobody can tell	See exactly which passages the agent received before it answered
Changing how documents are processed means fixing every agent	Change it once in the knowledge base; every connected agent benefits
No one knows which agent is using which knowledge base	Each KB has its own endpoint and keys; every call is recorded in the KB’s call log

How it differs from building RAG per agent

	RAG built per agent	Enterprise Data Platform
Documents	Uploaded separately into each agent	Ingested once into a Data source, folder tree preserved
Processing	Each team writes its own chunking and OCR	Ready-made pipelines per file type, tunable in the UI
Connection	Tied to one framework	MCP and REST API - Claude, Cursor, VS Code, Dify, LangGraph or custom agents
Quality	Checked by gut feeling	Retrieval test with diagnosis, golden question sets, live quality tracking
Control	Anyone with the code can read everything	Per-KB API keys or OAuth, revocable, with call log

The quickest way to tell them apart: Agents do the work. EDP is the company library - Where documents are organized, kept current, and lent to the right people.

Five building blocks

Data ingestion and Knowledge are the foundation - Without clean documents, every answer downstream is wrong. Quality, Connection and Administration are what bring that knowledge into real operations.

#	Block	Answers the question
1	Data ingestion	Where documents come from, and how they are organized and tagged with metadata
2	Knowledge	Where agents search, and what they are allowed to search
3	Quality	Does the agent get the right passages, and did the last change help or hurt
4	Connection	How agents connect
5	Administration	Who can do what, and what information is attached to documents

1. Data ingestion

Upload files or whole folders into a Data source - PDF, DOCX, XLSX, PPTX, TXT, MD, HTML. The folder tree is preserved and becomes filterable metadata. The pipeline picks a method per file type: DOCX split by headings, scanned PDFs get OCR automatically, spreadsheets split by rows with column headers kept.

2. Knowledge

A Knowledge base groups one or more sources into something agents can search. Each KB has its own embedding model, retrieval method (Vector, BM25 or Hybrid + rerank) and endpoints. For example: A “Legal documents” KB turns on only return documents in effect; an “Accounting procedures” KB turns on expand to parent chunk so the agent receives a whole Article instead of half a sentence.

3. Quality

Retrieval test shows exactly which passages an agent will receive - The first thing to check when an agent answers wrongly. Quality evaluation keeps a golden question set for checking a KB, and shows the questions agents ask that the KB cannot answer yet.

4. Connection

Every KB has its own MCP URL and REST URL. Agents connect with OAuth (the user signs in and the agent reads with that user’s own permissions) or with an API key issued for that KB.

5. Administration

A metadata schema shared across the workspace, member roles, and ownership transfer when someone leaves.

Key concepts

Concept	Meaning
Data source	Where documents enter the platform, by uploading files and folders
Knowledge base	What agents retrieve from, and the unit access is granted on. Always built from one or more sources
Pipeline	The steps that parse, chunk, enrich, embed and index documents
Chunk	A passage after splitting - What the agent actually receives
Metadata	Information attached to a file (confidentiality, effective date, department…) used to filter at retrieval time
Endpoint	The KB’s MCP / REST URL, used with an API key or OAuth sign-in

There are no document-level permissions yet. Every workspace member can read uploaded files, and anyone with a KB’s API key can query everything in it. Do not upload confidential documents at this stage.

Who EDP is for

Audience	Main value
Agent builders	No need to write a retrieval layer; connect a KB over MCP in minutes
Business document owners	Upload, organize folders, add metadata - No need to know about vectors or chunks
Data & AI teams	Tune retrieval methods, review chunks and track quality
IT & Security	Per-KB keys, OAuth, call log, centralized metadata schema

Where to start

Create a data source

Go to Data sources and upload a folder of business documents. Keep the folder tree so you can filter on it later.

Create a knowledge base

Select the new source, keep the Automatic per file pipeline, and write a description telling agents when to call this KB.

Test retrieval

Ask the questions real users will ask and check the returned passages before handing the KB to an agent.

Connect an agent

Copy the KB’s MCP URL into your agent, sign in with OAuth or issue an API key - The agent starts searching right away.

See each step in detail on the Quick start page, and a detailed description of each screen in the Screens & features group.

​Why a separate data layer

​How it differs from building RAG per agent

​Five building blocks

​1. Data ingestion

​2. Knowledge

​3. Quality

​4. Connection

​5. Administration

​Key concepts

​Who EDP is for

​Where to start

Why a separate data layer

How it differs from building RAG per agent

Five building blocks

1. Data ingestion

2. Knowledge

3. Quality

4. Connection

5. Administration

Key concepts

Who EDP is for

Where to start