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PlateerAI Labs

Applied AI

PoC Projects

Enterprise AI proof-of-concept projects that start from a pain point on the customer's floor and are validated with XGEN

Plateer AI Labs digs into the problem alongside the customer and turns it into a verified result

Enterprise AI does not end at the demo. Plateer AI Labs starts from a real problem on the customer's floor and builds a solution that can be verified, using technology researched together

Kept apartRelationsTraversing meaning and relations
Knowledge reasoning·Ontology

Answers that thinned out as documents piled up, reconnected by relationship

One customer held survey, performance, evaluation, and audience-response data — and still felt that important information was missing from the AI's answers. The data sat in separate places, so cause and relationship could not be explained. XGEN's ontology goes past similarity-based top-K lookup to traverse meaning and relationships, answering not just what exists but why it is so and what it connects to.

“The answer isn't wrong, but it feels like something important is missing”
— Company M
Relationship-based fact searchCause and context reasoning
ExternalInternalMCP AppBuild once, reuse as is
Agent portability·MCP App

Build the agent once, run it inside the internal network

Reusing an agent built on the external network with n8n, Dify, XGEN, or Claude meant rebuilding it for a different platform inside — operating experience was lost and cost was paid twice. MCP App wraps an agent as a standard package that ports between environments, so support, review, and reporting agents run inside the internal network as they are.

“We want to reuse the agents we built outside on our internal network”
— Company J
Build once, run anywhereAgent reuse across environments
1Sign in2Connect API3Register tool4TestNo Code
No-code integration·PathFinder

From login to tool registration, without a developer

API documentation alone made integrations hard to implement, and the screens were not free to arrange, so even a small idea kept needing development capacity. Ideas stayed in the planner's head. PathFinder connects login, API wiring, tool registration, and testing without a line of code, so the business side turns its own ideas into working tools.

“In the end we couldn't move forward without a developer”
— Company I
No-code integrationBusiness-led tool building
?The question becomes the screen
Question-to-UI·FloUI

Ask the question, and the analysis screen appears

Looking at order, return, and purchasing data from several angles meant requesting a report or commissioning a separate analysis every time — and in a meeting or a planning session there was no way to test a hypothesis on the spot. FloUI turns the question itself into the analysis screen (question-to-UI), so several angles can be explored and checked immediately.

“Every time, we have to request a report or commission a separate analysis”
— Company L
Free analysis from several anglesHypotheses tested on the spot
Submitted docsQA cross-checkMCP ToolCleared!Needs fixNot for air
Review-workflow automation·MCP Tool

Product review that cross-checks many documents, handled by one MCP Tool

Putting a fashion product on air and on sale meant a person manually cross-checking the product specification, test report, care label, import declaration, OEM contract, and more to judge whether it complied. We turned that review workflow into an MCP Tool: extracting functional claims, checking them against the test report, cross-verifying care label and country of origin, and issuing a combined QA verdict — cleared for sale, needs correction, or not broadcastable — wired straight back into the system.

“Before a product goes on air, someone has to check by hand that the documents agree with each other”
— Company L
Automated document cross-verificationAutomatic sale-compliance verdict

Industries and workloads we have validated

Plateer AI Labs has applied XGEN to real work across finance, commerce, IT, manufacturing, the public sector, and healthcare. Below are the workloads validated in each industry

Finance

  • Internal generative-AI platform
  • Document and contract review
  • Search modernization (RAG)
  • Credit and underwriting support

Commerce & Retail

  • Product search and recommendation
  • Automated customer support
  • Product review and QA automation
  • Customer win-back

IT & Manufacturing

  • AI Code Assistant
  • In-house code recommendation (RAG)
  • Technical document search
  • Air-gapped on-premise deployment

Public Sector

  • Procurement search modernization
  • Citizen-inquiry support
  • Policy and regulation search
  • Workplace productivity

Health & Pharma

  • Medical and claims document recognition
  • New-product development decision support
  • Regulation and literature search

From discovery to operations, a way of working that has been proven

  1. 01

    Discover

    Define the pain point in the customer's work

  2. 02

    PoC

    Validate in as little as a day (1-Day PoC)

  3. 03

    Build

    Deploy, including on-premise and air-gapped

  4. 04

    Operate

    Settle in with on-site or remote operations

A model-agnostic architecture lets you choose the LLM you want, and deployment works on cloud as well as on-premise and network-separated environments

Beyond validation, see the customers where this is delivered and running today

View customer cases