Trust & security posture

  • Zero Cloud Egress
  • Ingestion PII Redaction
  • Runs on Local Infrastructure
  • DPDP Act Compliant

Every connector points at a local engine. Nothing leaves the building.

100% ON-PREMISE · AIR-GAPPED · MODEL CONTEXT PROTOCOL (MCP)

The Air-Gapped Context Engine for Enterprise AI Agents and Teams.

KORE connects directly to your communication channels, issue trackers, code repositories, databases, and unstructured files, extracting scattered operational knowledge into verified business rules, an auditable knowledge graph, and deterministic memory for AI agents and employees.

Schedule a 30-Day Pilot Deployment Explore Architecture & MCP Spec

Accepting Pilot Partners for the 30-day bounded pilot.

Ingestion layer

Connect Your Entire Stack.

Ingest dark context, engineering decisions, and operational rules directly from your day-to-day tools with zero third-party data leakage.

Slack

Local Export + Live API

GitHub

Live Repository API

Notion

Workspace Live API

Jira

Tickets & Project Boards

Linear

Issues & Cycles API

Microsoft Teams

Team Channels & Chats

Confluence

Knowledge Base Live API

Zendesk

Support Tickets & Logs

Google Drive

Drive Folders & Docs

SQL Databases

Relational DB Ingestion

Local Files

PDF, XLSX, DOCX, CSV, MD

Ingestion PII Filter

PAN, Aadhaar, Phone, Email

The context gap

Why Standard AI & Naive RAG Fail in the Enterprise

Throwing raw documents into generic cloud vector databases creates three critical roadblocks.

01

Hallucinations & Outdated Logic

Standard search retrieves unstructured text snippets without understanding which policies are active versus superseded. AI agents end up executing outdated rules with high confidence.

02

The CISO & Privacy Block

Enterprises cannot pipe proprietary CAD files, customer support tickets, financial sheets, and source code into multi-tenant public AI platforms.

03

Institutional Knowledge is Unwritten

Over 70% of vital troubleshooting procedures and pricing exceptions exist in Slack threads, PR comments, and personal spreadsheets, never in clean documentation manuals.

Product tour

See How KORE Structures Institutional Memory

Watch scattered internal documents turn into grounded business rules and an interactive knowledge graph in real time.

KORE Local Engine (Running on Localhost:8080)

Core architecture

Deterministic Grounding. From Ingestion to Serving.

Verified context, not ranked text. KORE grounds every answer before it is served to an agent or an employee.

1 INGEST

  • Content-addressed chunking: tiktoken 450 token cap.
  • Automated PII redaction on ingestion.
  • Soft supersession model for updated revisions.

2 GROUND & STRUCTURE

  • Multi-tier Grounding Gate: Layer B Cosine floor + Layer C Entailment judge.
  • Blended Entity Resolution: maps entities, relations, and domain constraints.

3 SERVE

  • MCP to AI agents: exposes verified context via the Model Context Protocol.
  • Sub-50ms conversational search: dashboard for human employees with citations.

Interfaces

Built for Autonomous Agents. Designed for Human Teams.

One grounded knowledge layer, two serving surfaces: standard MCP endpoints for agents, a local dashboard for your people.

Interface A · For AI Agents

MCP

  • Deterministic Tool Context Injects verified operational rules directly into agent workflows (Cursor, Claude Desktop, LangGraph, custom agents) via standard MCP endpoints.
  • Budget-Aware Retrieval Dynamically caps retrieved chunks against your active LLM context window without KV-cache overflow.
  • Zero Cloud Lock-in Compatible with local open-weights engines (LM Studio, llama.cpp, Ollama) or private enterprise endpoints.

Interface B · For Human Teams

Local Web Dashboard

  • Ask KORE Plain-language operational search with grounded answer cards and clickable source citations.
  • Rules Explorer Searchable enterprise rulebook with confidence scores, category filters, and manual approval workflows.
  • Knowledge Graph Interactive Cytoscape.js force-directed map of company entities, processes, and dependencies.
  • Data Sources Hub Monitored local folder watchers and file inventory management.

Control surface

Designed for the questions you cannot send away.

Knowledge is only useful when the people responsible for it can trust where it lives, how it was formed, and who can query it.

Deployment
Air-gapped & on-premise100% on your local workstations or private VPC, with zero cloud egress.
Traceability
Source-attributedEvery verified rule keeps the evidence that shaped it.
Compliance
DPDP Act Compliant by DesignBuilt for DPDP Act data residency requirements.
Models
Bring your ownOllama, LM-Studio, OpenAI, Anthropic, Gemini, or a private enterprise endpoint.

Enterprise pilot

Prove Measurable Accuracy in 30 Days.

Every Pilot Partner deployment begins with a Day-1 baseline and concludes with an empirical accuracy readout across your target operational workflows.

Day-1 baseline

30days

from baseline to an empirical accuracy readout, not a vague AI success story.

All targets are benchmarked directly against your nominated internal workflows on Day 1.

Metric Measurement Focus Target Outcome
Answer Accuracy Grounded retrieval accuracy against verified company sources +30% Accuracy Lift vs. standard search
Verification Latency Time required for an employee or agent to verify source proof < 5 Seconds with direct source citations
Knowledge Retention Extraction and mapping of unwritten business procedures Full Knowledge Graph Baseline

Each metric is instrumented on Day 1, measured at Day 30, and read out against the workflows you nominate.

What We Deliver During the Pilot

  • Air-Gapped Deployment Installed 100% on-premise on your local workstations or private VPC.
  • Connector Integration Ingestion across your primary data channels (Slack, Jira, GitHub, Drive, Local Docs).
  • Dedicated MCP Pipeline Custom context tools configured for your internal developer or operational agents.