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Wonder AtlasThe enterprise AI platform built on an ontology

Bring your entire enterprise into the digital world.

Every machine, every order, every customer and every process has a live digital twin in Wonder Atlas. See your operations as they are right now, and rehearse the future before you act.

Book a consultationSee how it works

Turn scattered data into a real-time picture of your operations.

When a machine fails, an order slips or inventory falls out of balance, you no longer wait for reports or chase people for numbers. You know right away what happened, how far it reaches and who should handle it.

WonderTech builds Wonder Atlas around an ontology, connecting the data in your ERP, MES, IoT systems and spreadsheets into a digital twin of your enterprise, so people and AI make decisions in the same business language.

  1. Data layerERP, MES, IoT, CRM, databases and spreadsheets
  2. Ontology layerObjects, properties, relationships and executable actions, with built-in permissions and audit
  3. Application layerAI agents, simulation, dashboards and workflows
When a machine raises an alert, follow its relationships to the affected work orders, orders and customers, and see who should handle it.

See the present, rehearse the future

With an ontology, data is no longer just tables. It becomes an operating picture you can query, reason over and act on. These are the six changes your organization will feel.

  • One business language across the company

    An "order" means the same thing in every system and every department. No more meetings just to reconcile numbers.

  • One alert, every impact in view

    Follow the relationships between objects to instantly trace the affected work orders, orders, customers and owners.

  • Simulate first, then decide

    Test options on the digital twin, from changing orders to reallocating stock or rescheduling. See the results first, then pick the best one.

  • Every AI answer has a source

    AI agents query and reason over the ontology, and every result can be traced back to its data source.

  • Decisions become actions

    Once you confirm, assignments, notifications and write-backs to existing systems happen in one step, within your existing approval flow.

  • Permissions and audit built in

    Roles determine what each person can see and do, and every action leaves an audit trail.

Wonder Atlas solutions by industry

Every industry has its own objects and relationships. Once they are modeled in the ontology, AI can reason across systems, make recommendations and act once you confirm. Point solutions cannot do this.

Aerial view of a container port and warehouse yard

Logistics & supply chain

Supply chain control center

Suppliers, plants, warehouses and carriers each run their own systems, so shortages and delays are often discovered only after they happen.

Objects in the ontology

  • Supplier
  • Material
  • Purchase order
  • Inventory
  • Shipment

What Wonder Atlas does

  • A live view of inventory and in-transit stock across suppliers, plants and warehouses
  • Early warnings of shortages and delays, with simulations of changing, reassigning or transferring orders
  • Recommendations for stock transfers and replenishment, written back to ERP once you confirm

What you getSee risks and decide how to respond before your customers feel them.

Precision machining equipment and an inspection station in a factory

Manufacturing & semiconductors

Smart manufacturing & quality traceability

Tracing a quality issue to the right lot, machine or supplier often means digging through several systems.

Objects in the ontology

  • Machine
  • Work order
  • Lot
  • BOM
  • Complaint

What Wonder Atlas does

  • Material readiness: which work orders can start now, and which materials are still missing
  • Trace from complaints, warranty and inspection data back to lots, machines and suppliers
  • A digital twin of the plant: simulate how rush orders or machine downtime affect delivery dates

What you getProblems surface earlier, and finding the cause no longer means searching every system.

Contract documents and material samples on a desk

Procurement

Smart procurement & contracts

Supplier risks, tariff changes and contract terms are buried in emails and documents, making timely purchasing decisions hard.

Objects in the ontology

  • Supplier
  • Contract terms
  • Part
  • Price
  • Tariff

What Wonder Atlas does

  • Scan suppliers for financial, geopolitical and delivery risks, and flag them early
  • AI reviews contract terms and flags expiry dates, minimum order quantities and rebate conditions
  • AI agents draft routine RFQs and supplier emails; a person reviews and sends them

What you getBuyers spend their time on negotiation and strategy, not chasing paperwork.

Hospital corridor and nurse station

Healthcare

Hospital operations

Beds, staffing and patient flow change by the hour, while claims and administrative work consume a great deal of staff time.

Objects in the ontology

  • Bed
  • Patient flow
  • Clinical staff
  • Shift
  • Claim

What Wonder Atlas does

  • A live view of beds and capacity, to plan transfers and discharges ahead of time
  • Staff scheduling recommendations based on patient volume and regulations
  • Data preparation and document drafting for National Health Insurance claims and appeals

What you getClinicians spend less time on paperwork and more time with patients.

Steel-frame construction site and crane

Construction

Construction projects

Drawings, specifications, schedules and subcontractor information are scattered, so delays often surface only on site.

Objects in the ontology

  • Project
  • Work item
  • Equipment
  • Subcontractor
  • Drawing

What Wonder Atlas does

  • Extract quantities and key clauses from drawings and specifications to speed up estimating
  • Coordinate labor, equipment and materials across sites
  • Predict delay risks from progress, weather and supply data

What you getAdjust resources before delays happen, and protect schedule and budget.

Documents and a screen on a financial institution desk

Finance & insurance

Banking & insurance

Customer data is spread across business systems, and reviews, underwriting and claims rely heavily on manual cross-checking.

Objects in the ontology

  • Customer
  • Account
  • Policy
  • Transaction
  • Claim

What Wonder Atlas does

  • A single view of each customer, across every product line
  • Support for KYC and anti-money-laundering reviews, flagging high-risk cases to handle first
  • Automated extraction and first-pass review of underwriting and claims documents, with an audit trail at every step

What you getFaster, more consistent reviews that stand up to audit.

Neatly stocked shelves in a retail store

Retail & consumer goods

Retail & consumer goods

Some stores run out of stock while others are overstocked, and the impact of pricing and promotions is hard to judge in advance.

Objects in the ontology

  • Store
  • Product
  • Inventory
  • Promotion
  • Supplier

What Wonder Atlas does

  • Warnings for stockouts and overstock, with suggested transfers between stores
  • Simulate how pricing and promotions affect sales and margin before launch
  • Replenishment recommendations that adjust automatically as sales and inventory change

What you getThe right products, in the right stores, at the right time.

Solar farm and wind turbines

Energy & utilities

Energy & facilities

Sites and equipment are spread out, and failures are often discovered only after downtime.

Objects in the ontology

  • Site
  • Equipment
  • Sensor
  • Maintenance order
  • Technician

What Wonder Atlas does

  • Combine sensor data to predict equipment failures and schedule repairs early
  • Instant downtime alerts, with work automatically dispatched to the nearest technician
  • Tracking and reporting of energy use and carbon emissions

What you getLess unplanned downtime, and maintenance that moves from reactive to proactive.

Large display wall in an operations center

Government & public services

Public sector readiness

Assets, personnel and budgets sit in separate systems, making it hard for leaders to see overall readiness.

Objects in the ontology

  • Asset
  • Personnel
  • Training
  • Budget
  • Contract

What Wonder Atlas does

  • A complete view of asset availability and maintenance status
  • Personnel skills, training and deployment at a glance
  • Budget execution and reallocation, without replacing entire systems

What you getWhen it matters, know exactly which resources you can draw on.

Drone and edge computing equipment in the field

Defense & public safety

Edge AI & multi-sensor coordination

Field environments are often disconnected or low-bandwidth, and large volumes of sensor data cannot reach the command center in time.

Objects in the ontology

  • Sensor
  • Drone
  • Camera
  • Area
  • Event

What Wonder Atlas does

  • Run inference directly on drones, cameras and other field devices, sending back only what matters
  • Fuse imagery, radar, audio, positioning and other sensor data
  • Field events flow back to the ontology in real time, so command and field teams share the same picture

What you getFaster judgment, even where communications are limited.

Why WonderTech

Most enterprises are not short of systems. What they lack is a way for those systems to speak the same language. We start from your operational problems and connect the systems you already have, instead of asking you to replace them.

  • Built around an ontology

    We are not adding yet another report or system. We build a shared business model for the enterprise, so every system, department and AI agent has the same understanding of a "machine", an "order" or a "customer".

  • Vendor-neutral, keep your existing systems

    ERPs such as SAP and Digiwin can be connected alongside in-house systems, MES, IoT and Excel. Keep your existing investments and grow new capabilities on top of them.

  • On-premises and private cloud first

    Critical data stays in your own environment, in a single-tenant deployment that meets the security requirements of government agencies and every industry.

  • Phased rollout, with results at every step

    We do not stop at slides and demos. Go live with the most valuable use case first, then expand to your full operations based on real-world results.

8 challenges in enterprise systems, and how we solve them

We have met every one of these problems. Each has a proven approach, and we deliver it right the first time.

  • Siloed systems, isolated data

    A unified object model built on an ontology maps data from every system to the same business language.

  • Replacing legacy systems risks migration errors

    Phased migration with old and new systems running and reconciled in parallel. We switch over only when everything matches, without interrupting operations.

  • Existing systems lack AI capabilities

    No need to replace them. We connect AI directly into your existing workflows.

  • AI answers cannot be traced, so no one trusts them

    Every result traces back to its data source, runs under role-based authorization and follows your existing approvals.

  • Custom development leaves security gaps

    DevSecOps and code security testing that meet the requirements of Taiwan's Cyber Security Management Act.

  • Accounts and permissions are scattered

    Single sign-on and role-based permissions: configure once, apply everywhere.

  • Performance falls short after go-live

    We find and tune bottlenecks layer by layer, from the database and queries to the architecture.

  • Critical data cannot go to the cloud

    On-premises or private-cloud single-tenant deployment keeps your data in your own environment.

Four strengths that put the platform to work

Even a great platform has to connect to your existing systems, run fast enough and pass security review. These are the fundamentals we have built up over many years.

  • Data integration & migration

    Deep experience integrating heterogeneous databases and systems. Legacy data is migrated in phases and reconciled in parallel, so operations continue through the switchover.

  • Performance tuning

    We find bottlenecks layer by layer, from the database and queries to the system architecture, and can optimize systems built in-house or by other vendors.

  • Code security testing

    Static and dynamic code analysis with industry-standard tools, combined with a DevSecOps process, meeting Taiwan's Cyber Security Management Act and government source-code review requirements.

  • 15+ years building large-scale systems

    We bring more than 15 years of hands-on experience across government information systems, defense-related technology projects and enterprise software, and we know the requirements, processes and acceptance practices of the public sector and large enterprises.

A 5-step rollout, from planning to ongoing support

One team takes responsibility from kickoff, modeling and development through go-live and ongoing support, as the ontology grows step by step inside your enterprise. Every step has clear deliverables, so you always know where the project stands.

  1. 01

    Project kickoff

    A kickoff meeting confirms goals, overall scope, points of contact and timeline, and plans the order of a phased rollout.

    DeliverableProject plan

  2. 02

    Current-state review & interviews

    We review existing systems, data tables and workflows, and interview frontline staff and managers to find where decisions get stuck.

    DeliverableCurrent-state analysis report

  3. 03

    Ontology modeling & system design

    We define objects, properties, relationships and executable actions, write the SRS and build interactive prototypes, confirming each item with business teams.

    DeliverableOntology model, specifications and prototypes

  4. 04

    Integration, development & go-live

    We connect data from existing systems, build AI agents and user interfaces, and go live in real environments in planned phases.

    DeliverableA system running in production

  5. 05

    Acceptance, training & ongoing support

    Phased acceptance and user training, followed by annual maintenance after go-live, with objects and use cases expanded based on user feedback.

    DeliverableAcceptance records, training and a maintenance plan

Our mission

AI should make people more capable, not replace them.

WonderTech builds an AI platform on an ontology, so everyone in an enterprise or agency can see the full picture at the right moment and make better decisions.

  • Bring data back to the language of the business

    Data is scattered because every system describes the world in its own way. We use an ontology to translate it back into the business language people use every day.

  • Make AI grounded and traceable

    AI recommendations must be able to show their basis. Only when every result traces back to its sources and rules can it enter critical processes.

  • Keep decisions in human hands

    AI gathers information, projects impacts and makes recommendations. People make the decisions and remain accountable.

  • Put great technology to work in Taiwan

    With an on-premises-first, vendor-neutral approach, we help manufacturing, logistics, public services and every industry put AI to work, with confidence.

Our team

To build and sustain an ontology platform for the long term, WonderTech organizes its team around six functions. They share one goal: build a great product and ecosystem, while safeguarding service quality and information security.

  • Ontology platform engineering

    Builds the core of the platform: the object model, data integration, data lineage and quality checks. The goal is a stable, extensible platform where data from any system joins the same business language and can carry our clients' operations for years.

  • AI agents & applications

    Enables AI to query and reason over the ontology, and to make recommendations and take actions within authorized limits. Also turns capabilities such as speech, vision and document automation into reusable modules that keep extending what the platform can do.

  • Forward deployed engineering

    Works on site with clients, connecting the platform to real systems and processes and guiding them all the way to go-live. Everything learned in each deployment goes back into the product, so the next client goes live faster and more smoothly.

  • Product & design

    Sets product direction and designs interfaces and workflows that frontline staff can genuinely use. We believe a good system is one people want to open every day.

  • Security, governance & quality

    Owns access control, audit trails, code security testing, regulatory compliance and service quality management, so clients can entrust us with critical data and processes.

  • Industry ecosystem & operations

    Develops solution templates for each industry, builds an ecosystem of technology and channel partners, and runs legal, finance and recruiting to support steady long-term growth.

What we value, and how we work

Every day, we make decisions that affect our clients and the people who use our systems. These principles shape what we do and do not do, and how we treat clients, users and each other.

  1. 01

    People first

    Technology should serve people. We build tools that make people more capable: AI organizes information and makes recommendations, while final judgment and responsibility stay with people.

  2. 02

    Act for the long-term good

    We choose what benefits clients, users and society over the long run, even when it is harder in the short term. We do not build systems designed to harm or deceive people.

  3. 03

    Take both opportunity and risk seriously

    AI can bring tremendous progress, and real risks too. Seeing both clearly is how we get things right.

  4. 04

    Be honest and transparent

    We are clear about what our systems can and cannot do, and every AI-generated result can be traced to its source.

  5. 05

    Care for every user

    Users are not only the clients who pay. They include the frontline staff who operate the system every day, and everyone affected by it.

  6. 06

    Do simple things that work

    If a problem can be solved simply, we do not reach for complexity. We value real-world validation over looking impressive.

  7. 07

    Own the outcome

    A system is done only when it is live and actually in use. We let results speak, and we take responsibility for them.

Let's bring AI to the front lines of real industry.

We connect ontology and AI agents to the daily operations of factories, hospitals, ports and public agencies. Every line of code here will actually be used by people on the ground. If that is the work you want to do, we would like to meet you.

  • Ontology platform engineering
  • AI agents & applications
  • Forward deployed engineering
  • Product & design
  • Security, governance & quality
Join us

Let's talk about your operational challenges

Tell us about your industry, the systems you use today and the problems you most want to solve. Together we will find the best place to start, and show how WonderTech can help.

Book a consultation