Evidence-backed answers from your own documents

Direct answers grounded in policies, runbooks, and institutional knowledge. Stays current as your content changes.

Solution

Search that gives answers, not links

AI agents answer from your actual documents with citations.
The platform keeps answers current as content changes.
Deploy as an API endpoint or use the built-in console UI.

Answers grounded in your content

AI agents reason over your actual documents, not the internet.
Every answer includes passage-level citations tied to source passages.
Users verify evidence, not guess whether the AI made something up.

AI-generated answer with passage-level citations from source documents

Stays current automatically

The platform keeps content current as your knowledge sources change.
Agents serve answers from the latest version, not a stale snapshot from last quarter.
No manual reindexing or rebuild required when content changes.

Document list with current content and recent update timestamps

Deployed as a product, not a prototype

Each agent gets its own API endpoint you can embed in your internal tools.
Built-in UI for teams who want search without building anything.
Tenant-scoped access, audit logging, and role-based controls for enterprise use.

Agent configuration with API endpoint, corpus, and system prompt

The engineer gets a direct answer, not a list of documents to read.
The citation points to the exact policy section, so the answer is verifiable.
The content was updated last week, and the agent already has the latest version automatically.

Example user experience

A support engineer checks the current escalation policy

The agent returns a cited answer from the latest version of the policy.

Question

What is the escalation path for P1 incidents after hours?

Grounded answer

After-hours P1 incidents should be escalated to the on-call SRE via PagerDuty, with simultaneous notification to the engineering manager on rotation. If no acknowledgment within 15 minutes, escalate to the VP of Engineering.

Source: Incident Response Runbook v4.1
Section: §3.2 After-Hours Escalation
Last updated: 5 days ago

Implemented with the Knowledge² Python SDK

Keep the implementation surface small

Python SDK TypeScript SDK Docs Examples

Python SDK example

Python

from sdk import Knowledge2k2 = Knowledge2(api_key="k2_...")# Create a knowledge search agentagent = k2.create_agent(    name="internal_search",    corpus_id="corp_internal_docs",    system_prompt="Answer questions about internal policies and procedures. Always cite the source document and section.",)# Query the agentanswer = k2.chat(    agent_id=agent["agent_id"],    query="What is the escalation path for P1 incidents after hours?",)

Illustrative search response

JSON

{  "answer": "After-hours P1 incidents should be escalated to the on-call SRE via PagerDuty...",  "citations": [    {      "source": "Incident Response Runbook v4.1",      "section": "§3.2 After-Hours Escalation",      "text": "P1 incidents outside business hours: notify on-call SRE via PagerDuty..."    }  ],  "agent_id": "agent_internal_search",  "corpus_id": "corp_internal_docs"}

Cited evidence on every answer
Tenant-scoped access controls
Audit logging
VPC / on-prem deployment
SOC 2 readiness

“With Knowledge², our customers go from scoping to a working proof-of-concept in minutes, not weeks. Their technical and business teams move in lockstep instead of stalling on infrastructure. The ability to deploy within regions that have strict data residency requirements removes the last blocker to adoption.”

Bruno Machado, CEOElevata

Evidence-backed answers from your own documents

Search that gives answers, not links

What grounded internal search looks like

Answers grounded in your content

Stays current automatically

Deployed as a product, not a prototype

A support engineer asks a domain question and gets a cited answer

A support engineer checks the current escalation policy

Your first agent is twenty minutes away