AI knowledge base for teams and agents

The AI Knowledge Base
That Actually Learns

Named AI agents join your team chat, read your documentation, and become your team's knowledge expert.

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DATA SOURCES C Confluence Wiki pages, spaces N Notion Databases, pages S SharePoint Documents, sites G GitHub Repos, wikis, issues U Any URL Web pages, APIs + more connectors LioraEngine AI Knowledge Base LLM Synthesis Knowledge Graph Agent Memory Contradictions Embeddings Versioning Crawl → Extract → Structure → Link → Validate PII redacted before LLM KNOWLEDGE CONSUMERS M MCP Agents Claude, GPT, custom A REST API 120+ endpoints T Team Chat Slack, Teams U Web UI Browser, dashboards E Event Streams WebSocket, Kafka + your agents

Your team's knowledge is scattered

Three problems every engineering team faces.

Docs go stale

Wiki pages written once, never updated. New hires read outdated runbooks. Nobody knows which version to trust.

Search doesn't understand

RAG retrieves text chunks. It doesn't know what your team does, which docs conflict, or what was validated last week.

Knowledge walks out the door

When senior engineers leave, their knowledge goes with them. There's no system that captures and preserves what the team knows.

Meet Harry, your team's AI knowledge agent

Harry is a named LioraAgent that lives in your team chat — Slack, Teams, or wherever your team communicates. He reports to a manager, reads your docs, and answers questions with structured, cited knowledge.

Slack
Mick Jagger
How do we deploy Smithsonian?
Harry (SRE Team)
Smithsonian Production Deployment Playbook runbook
Product: Smithsonian · Owner: Platform Engineering

Steps:
  1. Set kubectl context to smithsonian-prod-us-east-1
  2. Ensure JIRA ticket approved + CI green on main
  3. Review DB migrations, notify on-call SRE
  4. Deploy in sequence: auth-gateway → inventory-tracker → order-processor → notification-dispatcher

Key facts:
  • 5 microservices, deployment order strictly enforced
  • auth-gateway is the critical edge service
Source: Confluence SAAS space · 48% semantic match

Reads your docs

Point Harry at your wiki — Confluence, Notion, SharePoint, or a plain URL. He crawls it, extracts key steps, facts, and owners using LLM synthesis. Not raw text — structured knowledge.

Builds a Knowledge Graph

Entries are linked: references, depends_on, derived_from, contradicts. Browse connections in an interactive D3 graph.

Asks permission first

Harry only talks to people his manager approves. Unknown person? He creates a group DM with the manager to ask permission.

Spots contradictions

Two docs say different things? Harry detects it, notifies the manager, and asks "which one should I trust?"

Not just for humans. For AI agents too.

Other AI agents connect to Liora's knowledge via MCP Server or REST API. Your on-call agent, code review bot, or postmortem writer can all query and contribute knowledge.

MCP Server

9 tools: search, get, ingest, challenge, memory, graph, changelog. Any MCP-compatible agent connects natively — Claude, GPT, custom agents.

REST API

120+ endpoints. Register agents with scoped API keys (read, write, challenge). Rate-limited, tenant-isolated, audit-logged.

Event Streams

Subscribe to knowledge changes via WebSocket or Kafka. Get notified when docs update, contradictions are detected, or new knowledge is added.

What's inside

Everything you need to manage, explore, and govern your knowledge base.

Knowledge Browser

Wiki-style browsing with semantic + full-text search. Every entry shows product, owner, key steps, key facts, source URL, validator, and confidence score.

Knowledge Graph

Interactive D3 visualization. 6 relationship types. BFS traversal. Click through connections. Impact analysis. Reading order from depends_on chains.

Agent Memory

Durable facts Harry learns from documents and conversations. "We use ArgoCD for deployments." "auth-gateway is the critical edge service." Searchable, citable.

Cost Dashboard

Every LLM call logged: prompt, response, tokens, cost, latency. See exactly what goes to external AI providers. PII redacted before sending.

Contradiction Dashboard

Side-by-side comparison of conflicting entries. Resolve with explanation. Resolution history. LLM-confirmed severity (critical/high/medium/low).

Platform Connectors

Confluence, Slack, Teams, SharePoint, Google Docs, Notion, GitHub. Credentials encrypted in database. Test connectivity from the UI.

Connects to your stack

Knowledge sources, messaging platforms, AI providers, and databases.

Confluence
Slack
Teams
Notion
GitHub
PostgreSQL
Kafka

How it's different from RAG

Traditional RAG LioraEngine
Knowledge Retrieved at query time, forgotten after Accumulated over time, structured, versioned, linked
Understanding Text chunks with similarity scores Product, owner, key steps, key facts extracted by LLM
Contradictions Returns both conflicting docs without noticing Detects conflicts, notifies manager, tracks resolution
Sources No provenance Every fact traceable to source URL + validator + date
Memory Stateless — starts fresh every query Persistent agent memory across sessions
Access control Anyone can query Manager-approved contacts only. Unknown users gated.
Other agents Not designed for agent-to-agent MCP Server (9 tools) + REST API + event subscriptions

Ready to give your team a knowledge expert?

Deploy as a VM or AMI. Your data stays in your infrastructure. LLM calls are PII-redacted. Full cost visibility.

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