Can we really get results in four to six weeks?

One week to connect your existing systems read-only, two to three weeks to learn the usual range and set the detection rules, and one to two weeks to write up the evidence. We run it at the scope of one plant and one product family.

Do we have to touch our existing ERP or MES?

No. DARVIS reads through read-only connectors and only collects data, so your systems are untouched. No modification or redeployment is required.

What do we get for what it costs?

You are told the moment cost or margin moves unlike it usually does, before the loss lands. Scope and terms are agreed per plant and product family.

How is data security handled?

TLS 1.3 in transit, AES-256 at rest, RBAC for access. We have passed a government security review and designed against ISO 27001 controls.

Do you support on-premise or private cloud?

On-premise installation, private cloud (AWS / Azure / GCP VPC) and hybrid are all supported. Your data is returned immediately when a contract ends.

Can it integrate with our SSO and RBAC?

Yes — standard SSO over SAML 2.0 and OIDC, with fine-grained access control by department and role.

What is the ROI based on?

On the cost anomalies caught that were previously missed, and the money that can be saved by acting before the loss lands. It differs by plant and product family, so we size it together inside the agreed scope.

How is this different from BI or a systems integrator?

BI shows you metrics; an SI improves the systems you already run. DARVIS is built to catch manufacturing losses before they happen. It flags the moment something starts leaking, so people can check and act before the loss lands. DARVIS-P covers cost and margin, DARVIS-I inventory and DARVIS-Q quality.

How long does approval take?

Review periods differ by programme. We prepare the application and handle the review process with you, so it is less work for your team.

Cut rollout costs with public AI funding · we prepare the application with you, free

Check funding

DFINITE

Try the demo

DARVIS company & product DARVIO consulting

KO 한국어 EN English

Ontology-based AX

From ERP data reconciliationto tracing the causein one workflow

Q: Which government programmes can we use?

Korean programmes such as Manufacturing AI Innovation, Manufacturing Smart Service, the AI Voucher, the S Voucher and the Data Voucher. Award size and selection follow each programme’s public notice and review.

A food-manufacturing PoC validated the flow from tracing profit changes to compiling the report

See what it costs

DARVIS-P · exampleMaterial price is 12.5% above the quote basis

Where is the difference?

Price: Material cost in the sales quote uses KRW 2,400/kg; the recent purchase price is KRW 2,700/kg
Impact: KRW 2.4M more material cost at the same quantityPrice gap KRW 300/kgPlanned quantity 8,000kgKRW 300/kg × 8,000kg
Evidence: Example quote EX-Q1-QExample voucher EX-Q1-P
Proposed action: Request a price check from purchasing. Sales decides separately whether to change the quoteA person approves

Fictional example EX-Q1, not customer data. Additional material cost at the same planned quantity, not savings.

118 analyses closed
5 patents
Exhibiting at CES 2026

118 = analyses run since 2022 (161 in total) that were closed as completed

See AX cases in manufacturing, logistics and the public sector

Plant managerFlagged the moment cost moves unlike usual, with cause and evidence

Why the response is slow when data is everywhere

01Every system names the same item differently
02Reconciling them takes people’s hours
03Why it drifted lives only in one person’s head

The ontology reconciles the names and the AI finds and flags the cause, so people spend their time only on the call.

This is how far the AI goes

Connects ERP, MES and spreadsheets read-only and joins them into one map
Reconciles items that each system spells differently, automatically
Ask about cost, inventory or quality in plain language; answers come as tables and charts
Flags anomalies against company criteria, with candidate causes, amounts and evidence
Drafts the action and sends it to Slack or email. Approve or hold stays with people

See what the AI does, hands-on

Read only
Existing systems
- ERP
- MES
- Spreadsheets
- Documents

Ontology
The company's own standard
- Field names
- Reference values
- Relationships

AI
Anomaly, cause, amount, evidence
- Deviation detection
- Candidate causes
- Amount by cause
- Evidence line

Person
Proposal and approval
- Proposal drafted
- Approved
- Held

Evidence ledger
Decision and outcome, kept
- Evidence
- Approval history
- Outcome

Nothing is written back. People make the call, and every decision is kept with its evidence.

Where are you coming from

By industry

What we work on, industry by industry

The more layers of systems a company has, the better this fits — not because the data is missing, but because joining it is still left to people.

Cost, equipment and documents each sit in a different system. Joining them into a reason is still left to people.

Flags cost moving unlike usual and lets you act without waiting for the close
Slices equipment signals by work order and joins them to quality records
Finds documents scattered across systems with a single question

Published cases

Where did the change from the usual range begin? Three anomalies this morning

DARVIS-PFictional Manufacturer · Plant 1

As of 2026-06-13 07:00 · ERP · MES read-onlyRead-onlyFictional data

Anomalies

Month to date · 1 – 13 Jun

Only signals outside the usual range come up. Other items can be reviewed when needed.

CompanyFictional ManufacturerPlantPlant 1Period2026-06 to dateBasisUsual range · last 12 weeks

Where the anomaly appears

Cost+4.3%pFrozen-food cost ratio

MarginUsual range

Inventory19.5 daysCentre B · SKU-118

YieldUsual range

DefectsUsual range

DeliveryUsual range

3 anomalies · click a row to go down to the cause
Detected	Signal	Where	Usual → now	Next lock-in	Status
13 Jun	Cost ratio +4.3%p	Frozen food · material S-042	64.1%68.4%	Quote 25 Jun D-12	Check cause ›
12 Jun	Inventory 19.5 days	Centre B · SKU-118	4–7 days19.5 days	Disposal 20 Jun D-7	Review transfer ›
10 Jun	Purchase price +15.2%	S-042 · supplier K	Last-month avg+15.2%	Order 18 Jun D-5	Alternative quote ›

Cost, inventory and quality: DARVIS-P, I and Q Try the demo · share link Cut rollout cost with public funding

All seven industries in one table. Select a row to switch the tab above.

Data in	Basis for joining	Output	Role (before → after)
Cost, equipment, documents (ERP, MES)	Work order	Cost-anomaly alerts, equipment signals joined to quality records	Cost owner: month-end spreadsheet matching → confirming causes, approving actions
Regulations and guidelines	Clause	Answers with their supporting clause	Officer: answering the same enquiry again → checking the clause, ruling on exceptions
Closed-network internal material	Clearance level	Answers traceable to their source	Maintainer: searching manuals → deciding within clearance, recording it
Drawings and documents	Project and drawing	Safety and quality standards with their basis	Design and site lead: comparing drawings and specs → reviewing the gap, approving
Instrument data	History of standard changes	Audit evidence traced to the original record	QA: collecting records → ruling on deviations, answering audits
Internal databases	Ledger items and calculation steps	Read-only query results	Planning: writing data requests → verifying results, deciding
Voyage, inventory and settlement data	Decision criteria and change history	A record of when and why a call changed	Operations: matching settlements and stock by hand → approving exceptions, recording

Products

Three DARVIS products, and a DARVIO diagnosis to start

DARVIS-P handles cost and margin, DARVIS-I handles inventory, and DARVIS-Q handles quality. All three share DARVIS DB and Docs as a common engine, so the decision rules accumulate company-wide. DARVIO is the 1–3 week diagnosis before a rollout.

Not sure which one fits? Start from the cases by industry below. See all cases

What to compare it with

How it differs from Excel, BI and rule-based alerts

This lays out how far each of the four approaches goes for the same anomaly. Either way, the judgment is made by a person.

A table comparing manual Excel, BI dashboards, rule-based alerts and DFINITE from anomaly detection through action plans
Item	Manual Excel	BI dashboard	Rule-based alert	DFINITE
When the anomaly is known	After the month closes, once someone checks it by hand	When someone opens the dashboard and notices	When a preset condition is triggered	Items outside the company standard are picked out and flagged
Matching item names (across ERP, MES, spreadsheets)	By hand, every month	Once by hand at model design, redone when it changes	By hand, for every condition	The ontology matches them
Finding the cause	By hand	A decomposition tool exists, interpretation is by hand	None	AI proposes cause candidates, a person confirms
Whether an amount is attached	Calculated by hand	Shown as a chart, amount per cause is by hand	None	An amount for every cause
Evidence	Inside the file	Drill-down	The condition expression	An evidence line for every answer · decisions kept in the evidence ledger
Action plan and approval	Organized by hand	Needs a separate automation tool connected	Alert only	Proposes an action; approval or hold stays with a person
BI also has cause-exploration and automation-linking features. The difference is the effort a person spends designing and maintaining them, and who is left redoing the match when an item changes.

Customers we work with

From manufacturing to public sector, finance and defense — industries of every kind work with DFINITE

The record so far

The companies we’ve worked with, and what we sell today

Analyses closed

Field data from manufacturing, finance and the public sector

118analyses

See all cases

First rollout

One plant · one product family

4~6weeks

Start now and the first alert lands at the first month-end after 4 to 6 weeks.

Cost root-cause analysis

Ask each teamTrace in one flow

Before · manual

Check monthly report
Request data from each team
Gather and reconcile
Guess the cause
Report

After · linked

Detect the shift
Auto-decompose the cause
Ask follow-up in plain language
Report compiled automatically

Food-manufacturing PoC workflow comparison, not measured time savings.

Cases

Analyses we actually ran on the factory floor

Commercial printing

Measured across every order, on direct cost alone. The data already existed; it just arrived after the decision was made

Electronic components30–50 employees

Analysis axis: Unit cost by item
Output: Candidate causes with evidence

Automotive parts50–100 employees

Analysis axis: Input cost by process
Output: Variance broken down by contribution

Food100–200 employees

Analysis axis: Margin by product family
Output: Loss-making segments identified

Cases describe the analysis scope and workflow, without unpublished customer metrics. Published case studies may identify the customer.

See cases in your industry

How the platform is built

Meet DARVIS

Who does what

Who keys in the data? — Nobody

What the customer prepares

One read-only connection to ERP, MES and cost sheets
2–3 interviews — the words and rules the company runs on
The last 3–6 months of close data

Nothing new to key in. We read the systems you already have.

What DFINITE does automatically

Joins scattered tables and documents into one data map
Learns the usual range and catches the moment it drifts
Packages cause, amount and evidence into an alert

What people check

Whether the flagged item is real
Which action to take — quote, purchase, reallocate
Recording the decision and its outcome

People execute and approve. The system proposes and records.

Public funding

How public funding can cut what a rollout costs you

Based on Korean smart-factory funding programmes

The chart shows the structure only. The funded share and whether you are selected depend on each programme’s notice and review.

Which programmes apply

Manufacturing AI Innovation, Manufacturing Smart Service, the AI Voucher, the S Voucher and the Data Voucher.

We help, at no charge

From writing the application to handling the review, we prepare it with you.

How the review runs

Review periods differ by programme notice. You can start it in parallel with the product kickoff.

First rollout scopeOne plant · one product family

Time to kickoff4–6 weeks

Find the programme that fits your company

Guardrails

AI drafts, a person approves, and the decision is kept in the ledger

Four layers. Each answers one of the questions IT asks first: how it attaches, whether data leaves, who executes, what is kept. Same order as the ontology path shown in the demo.

01Connect
Attached read-only
Existing systems untouched
- ERP, MES, WMS and documents unchanged
- Nothing written back
- On-premise or air-gapped install
- TLS 1.3 in transit
02Standard
The company sets the standard
Ontology and access scope
- Field names, reference values and relations set by the company
- Access scoped by role (RBAC)
- Company SSO (SAML 2.0, OIDC)
- Identifiers masked, aggregates only
03Decide
AI drafts the action; a person approves
Nothing runs before approval
- AI: anomaly, cause, amount, evidence, proposed action
- Person: approve or hold
- Not executed until pressed
- No auto-execution, no prediction claims
04Record
Kept in the evidence ledger
Traceable in an audit
- Evidence, approvals, holds and outcomes in one ledger
- Who decided what, when, on which evidence
- AES-256 at rest
- Data returned immediately at contract end

We have passed a government security review and design against ISO 27001 controls. The formal names and scope of regulations and certifications are confirmed in the technical review material.

What each industry checks first

Industry	First question	Guardrail that answers it
Public sector · defense	Does anything leave the network; can visibility be split by clearance	Air-gapped on-premise install, clearance-scoped visibility, a passed government security review
Finance	Are personal identifiers exposed to the AI	Identifiers masked, aggregates only, read-only, role-scoped access, generated SQL visible
Pharma · construction	Can an audit trace back to the original record	Change history, trace to the original record, clause and page citations, evidence ledger
Manufacturing · logistics	Does it touch ERP or MES; who executes	Read-only connection, nothing written back, execution only after approval, SSO

We will write up the integration structure, the data flow and the security controls and send it over, and set up a review meeting with an engineer if you want one.

Next step

Your data is already enough. Now bring decisions forward and cut the loss

If another month went by without knowing where profit leaked, next month you can hear first. It starts with one early signal, and decision criteria accumulate with use. Start on the path that matches your role.

Plant manager · cost owner✓ Read-only connection · starts in 4–6 weeks

IT lead✓ No changes · on-premise · ISO 27001 controls

Public-sector lead✓ Programme guidance · we apply with you

FAQ

Frequently asked questions

Plant

C-level

Public sector