Knowledge Studio - AI Knowledge Management | Leena AI

RAG is only useful when the data isn't a mess.

The grounding layer for Leena AI Colleagues. Connects your sources, surfaces contradictions, tests answers, monitors health - so the AI on top is accurate, not just running.

Why it matters

The problem isn't doing RAG. It's the mess underneath.

RAG doesn't crash on dirty data. It quietly lies. We made that a technology problem instead of a year-long consulting engagement.

Before

The 14-month consulting engagement

Garbage in, garbage out — at scale

Now

A platform that runs itself

How it works

Six things, in order. Indexing is step three.

Most ‘knowledge layers’ stop at indexing. That's the engine, not the system. Here's the full flow that takes raw enterprise content and makes it safe behind an AI Colleague.

Connect to every source

SharePoint, ServiceNow, Drive, Snowflake, Databricks, S3, the open web. Synced centrally.

Parse what others drop

Images, tables, code, PDF structure. Most retrieval systems drop these. We don't.

Index with permissions

Chunked, embedded, indexed. Source permissions inherited. No parallel ACL.

Conflict analysis

Flags contradictions across documents before users hit them. Up to 1,000 articles per scan.

Smart testing

Generates questions, asks your AI Colleague, grades the answers. Continuously.

Health Dashboard

Stale, expiring, low-confidence, failed — all live, in one place.

What's different

Six things wikis, vector DBs, and bolt-on RAG kits don't do.

01 / Knowledge Health Dashboard

A quarterly audit, running continuously.

Stale, expiring, conflicting, low-confidence, parse-failed, sync-failed - all in one place, live. The audit never stops.

02 / Conflict Analysis

Find contradictions before users do.

Reads across documents, flags conflicts, shows the reasoning side by side. Up to 1,000 articles per scan.

03 / Exact-Source Citation

The passage. Not the document.

Others surface three relevant links. We point to the paragraph the answer came from. Verification in seconds, not minutes.

04 / Path-Based Access Control

Permissions inherited, not reimplemented.

Reads your folder structure and applies rules at retrieval. Stamp documents with attributes — country, role, function. No parallel ACL.

05 / Smart Testing

UAT as a dashboard, not a fire drill.

An LLM generates questions, asks your bot, grades the answers on accuracy, completeness, relevance. Continuously. Before users see anything.

06 / Multi-Format Parsing

Reads what others silently drop.

Images. Tables. Code. PDF structure. Most systems miss it. We don't - so the model sees what your authors actually wrote.

Connects to everywhere your knowledge already lives

Breakdown

Inside the Agentic AI architecture

Touchpoints

Your people work in Teams, Slack, email, voice, browsers, portals. AI Colleagues show up there. Not somewhere else.

Orchestrator

The brain. Reads the request, builds the plan, calls the right AI Colleague, routes between models on the fly. Model-agnostic by design - runs on Claude Opus 4.8, WorkLM™️, GPT 5.5, Llama 4, or Gemini 3.5.

AI Colleagues

Level 3 digital workers, each grounded by Agent Operating Protocols (AOPs), equipped with Tools to act in enterprise apps, powered by Context Graph and Memory, and managed via a Workbench.

Studios

Three no-code studios that let business users design, assemble, and ground AI Colleagues in plain English. AOP Studio writes the process. Workflow Studio wires in the tools. Knowledge Studio connects the truth.

Permissions and Access Controls

AI Colleagues only see and do what their role allows - across every system you connect. Permissions stay tied to your existing tools.

Integrations

200+ pre-built enterprise connectors across ServiceNow, Workday, SAP, Oracle, Salesforce, UKG, SharePoint, Snowflake, and more — via APIs, MCP, A2A, and browser/RPA.

Observability and Governance

Responsible AI layer, built into every AI Colleague. See every step, govern every action. Dashboards for execs, ops, and risk.

Trust and Security

Agentic AI security built into the architecture, not bolted on. SOC 2, ISO 27001, HIPAA, AES-256-GCM.

Frequently asked questions

What is Leena AI Knowledge Studio?

The grounding layer for Leena AI Colleagues. It connects to every system that holds your real knowledge, cleans and indexes the content, runs conflict analysis and smart testing, and continuously monitors knowledge health — so the AI on top is grounded in accurate information, not guessing. It's not a wiki. It's not another CMS. It's the connective tissue between your enterprise content and the AI working on top of it.

How is this different from a vector database or a standard RAG stack?

A vector database is the engine. Knowledge Studio is the car. Most vendors sell you the middle — chunking, embedding, retrieval. We add the parts that actually decide whether AI works in production: conflict detection, automated testing, knowledge health monitoring, and path-based access control. The mess of enterprise data is the real problem. We solve it.

Which enterprise systems does Knowledge Studio connect to?

SharePoint, ServiceNow KM, Confluence, Box, Google Drive, Dropbox — plus data lakes like Snowflake, Databricks, Azure, S3, and the open web via scraping. Content syncs and indexes centrally. Retrieval happens at inference time, not against your source systems, so you don't pay a latency tax.

How does Knowledge Studio handle permissions?

Source permissions are inherited automatically. Your security groups and path structures are respected, not re-implemented. Path-Based Access Control reads your folder structure (e.g., /Finance/Payroll/India_Managers) and applies the right rules at query time. No parallel ACL system to maintain. No re-permissioning project.

How fast can we deploy?

Days, not quarters. Every layer of Knowledge Studio is built to compress the path from raw enterprise content to grounded AI in production. Zero engineering tickets to get live. No 12-month consulting engagement. The first business value shows up in the first week.

What does Conflict Analysis actually do?

It reads across your documents and surfaces contradictions before your users see them. If one policy says the loan cap is $5,000 and another says $10,000, you see both, side by side, with the reasoning. Up to 1,000 articles per scan. The first run usually finds dozens of conflicts no one knew existed.

How does Smart Testing work?

The system generates questions from your knowledge base, sends them to your AI Colleague, and grades the answers on accuracy, completeness, and relevance — automatically and continuously. UAT becomes a live dashboard. Regressions are caught before users hit them, not after.

Does it extract content from images and tables inside PDFs?

Yes. Embedded images, code blocks, tables, and structural metadata inside PDFs — Knowledge Studio parses all of it. Most retrieval systems silently drop this content, which is one of the biggest reasons enterprise RAG quietly underperforms. We don't drop any of it.

What does the Knowledge Health Dashboard show?

Stale articles, expiring policies, low-confidence answers, parsing failures, sync failures, mis-permissioned content, and contradictions — all in one place, in real time. What used to be a quarterly audit runs continuously. Knowledge owners fix problems before they ever reach a user.

Do we need to clean our data first?

No. That's the whole point. We clean, parse, and pre-process everything we ingest. Conflict Analysis finds the contradictions. Smart Testing catches the broken answers. The Health Dashboard surfaces stale and parsing-failed content. We take the garbage out for you, instead of asking you to do it before you start.