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PlateerAI Labs

Research

Research that makes
Enterprise AI real

In an enterprise, whether AI succeeds is decided by the operating structure, not the model. Plateer AI Labs researches Agentic AI, knowledge graphs, MCP, and AgenticOps — and carries them through to the product and the customer's floor.

/ Research Areas

Research Areas

Public agencies and large enterprises have to account for data sovereignty, security, audit trails, organizational governance, and operational stability — and AI has to move past experiment and into the actual work process

Plateer AI Labs researches Agentic AI technology and an operating model built for enterprise environments, to answer exactly those demands

Our aim goes past simply using an LLM: an Enterprise AI Runtime where knowledge, reasoning, execution, and operations connect as one system

Core problems we research

AI you can trust

Answers come from the knowledge and data the enterprise already holds, not from what the model invents.

Every response is grounded in documents, regulations, operating guidelines, and an ontology-based knowledge model. Where there is no basis for an answer, the system says plainly that it cannot judge.

That keeps hallucination to a minimum and makes the AI explainable.

Enterprise data sovereignty

AI has to run on top of the enterprise's own data.

Plateer AI Labs researches an on-premise-first architecture that runs AI inside the customer's own infrastructure, with no dependence on a particular cloud.

The same operating model applies in the network-separated environments found in finance, the public sector, and manufacturing.

AI that composes and extends

No two enterprises work the same way.

We research a composable AI architecture that modularizes agents, workflows, knowledge, and tools so they can be recombined for the task at hand.

The result is a flexible AI environment that is not locked to one vendor or one model.

AgentWorkflowKnowledgeTool

Core research areas

Execution

Agentic AI Runtime

A runtime where several AI agents collaborate to carry out complex work.

Planner-driven planningMulti-agent collaborationTool callingWorkflow orchestrationHuman-in-the-loop

The goal is doing the work, not just answering the question.

Retrieval

Knowledge & RAG

A knowledge engine that produces accurate answers from the enterprise's own documents and data.

Hybrid retrievalMultimodal document understandingStructured table understandingMetadata-based access controlGrounded generation

We are building next-generation RAG that reads documents, images, tables, and scans as one.

Core Focus

Ontology & Graph Intelligence

The core research area at Plateer AI Labs. Past plain search, we research ontology-based AI that understands the relationships and context between enterprise data.

Structuring organizational knowledgeRelationship-based traversalRoot-cause analysisImpact analysisMulti-hop reasoning

It underpins a Graph RAG environment that reasons over relationships and context.

Model-Agnostic

Enterprise Model Architecture

An enterprise should not be locked to one model. We research a model-agnostic structure that lets you choose among LLMs by purpose, cost, and accuracy.

ClaudeGPTGeminiPrivate LLM

Several models run together under a single operating scheme.

Integration

AI Connectivity & MCP

Technology that connects AI to the systems already running inside the enterprise.

ERPGroupwareDatabasesDocument systemsLine-of-business systems

Internal systems and AI are linked safely, so the work actually gets automated.

Security

AI Governance & Security

Security and control decide whether AI can be adopted at all. We research a governance framework that meets the requirements of financial institutions and public agencies.

Context IsolationPrompt FirewallPersonal-data protectionAudit trailPolicy-based agent controlRisk assessmentHuman approval

Research in production

Research results become product, inside the XGEN technology stack

Engines

The core XGEN AI engines that handle ingestion, retrieval, and reasoning

Frameworks

Application frameworks that compose agents, workflows, and knowledge

Runtime

The execution runtime that keeps AI running reliably in enterprise environments

Library Gallery

The open-source libraries behind XGEN — pip install, try it in the browser

Methodology for verifiable AI

Plateer AI Labs runs a reproducible validation regime, not a demo

Validation Framework

Accuracy BenchmarkGroundedness EvaluationHallucination AssessmentCoverage AnalysisResponse Performance Benchmark

Every research result is evaluated against quantitative measures

/ Publications

Publications

Publications by our members

Papers our members have presented at conferences and in journals. Research in natural-language processing, deep learning, graphs and recommendation, and distributed training — the areas that meet Enterprise AI — forms the technical base of the product

NLP, deep learning, graph, recommendation, and distributed-training research that meets Enterprise and Agentic AI