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DARVIS · industrial AI data platform

DARVIS — the industrial AIplatform that turns floor data into decisions

Four stages — from the week-1 data map to week-5 operational improvement,
each with a deliverable of its own.
The weeks count the stages of installing this platform. The 4–6 weeks in month-end cost explanation is one cycle run on top of it — the two count different things.

  • Analyses closed118Cumulative since 2022 · 0 failed
  • Text2SQL3 secMeasured in the Company W PoC
  • Rollouts · PoCs4Electronics · automotive · food · semiconductors
  • Patents55 granted · 5 pending

Rollout timeline

A data flow designed the way DARVIS does it

Each stage is designed so that what it produces becomes the basis for the next.

01.DISCOVERYWeeks 1–2
02.AGENTICWeek 3
03.AUTOWeek 4
04.OPSWeek 5 onward
Knowledge Map01Weeks 1–2
Knowledge Map01Weeks 1–2
  • Automatic scan
  • Data map
  • Consistency report
Agentic Workflow02Week 3
Agentic Workflow02Week 3
  • Process manuals
  • AI assistant
  • Automatic citations
Automation03Week 4
Automation03Week 4
  • Automatic alerts
  • 5 rule types
  • Slack and Teams integration
Admin04Week 5 onward
Admin04Week 5 onward
  • Permission management
  • Usage log
  • Question-pattern dashboard

What the modules stand on

All four modules read one and the same structure

The modules do not each look at different data. They read one structure that fixes how the company decides, each from its own angle. Click a node to see which evidence and rules hang off it.

DARVIS Ontology ExplorerFictional food manufacturer · month-end cost · CLOSE-SYN-08
Read-OnlyExample — fictional data
100%
ASSESSMENT COREEVIDENCE · PLAN · REVIEWCLASSIFICATION · SUPPORTrelatedToevidencesgeneratestriggerstestedByproposesquantifiestracesscoresPGProductGroupProduct family and SKU schemeCECostElementTypeMaterial, labour, overheadARAllocationRuleTypeAllocation rule typesACAccountTypeAccount schemeIKInformationKindObserved, inferred, AI, simulatedCLConfidenceLevelLOW · MED · HIGHSCSecurityLevelMarking and export controlPRPerformerRoleFDE and reviewersRCRiskCategoryWatch, potential, highCPMonthlyClosePlanAugust close · CLOSE-SYN-08CMCostModel24-step, 3-tier allocationAPAllocationPolicyFreight allocation policy v12AVApprovalRuleCorrection and price approvalMTMetricSpecOperating margin definitionGSGoldenSetGolden set · validation specAFamily A12 frozen-food SKUsERERP ledgerVouchers and accountsMEMES actualsProduction and input actualsCCCost centre CC-095Logistics and freightROReview bodyFDE and customer ownerCACarrier TSynthetic vendor07Analyst-07FDE reviewerLLLedger load2,344 rows · read-onlyALAllocation runRUN-084 · doneREReconciliationSG&A gap zeroANAnalysis runRUN-118 · runningRVExpert reviewFirst and second · pendingAECorrection approvalNot approvedMGMarginDropSignalFamily A −2.1%pLELossExposureExposure · being estimatedCVCounterEvidencePrior-year policy changesCSCauseCandidateFreight allocation 0→400FTCorrectionTargetCorrection target · unapprovedSMScopeMatchQuestion inside scopeRPRealizedProfitRealised profit · unmeasuredPVProvenanceRecordSource, transform, model lineageDTDecisionTraceFull decision traceSXSecurityMarkingSYNTHETIC · DEMOCFConfidenceAssessment0.87 · HIGHTITimeInterval08-01 ~ 08-21OSOrgScopeFood division · Seoul plant
41 nodes46 relations6 layersONTOLOGY 0.4ObservedDARVIS inferencePlan / ruleRisk / unapprovedSimulated

Click a node and only its direct relations stay lit — you can see how evidence, counter-evidence, rules and review hang off a single assertion.

Pinch to zoom, drag to move.

What an ontology is, and how it is verified →See real cases analysed this way →

How DARVIS solves a problem

This is how DARVIS learns.

  1. 1

    Structure

    Scattered data organised into one language. No database migration — one map laid over the systems you already run.

  2. 2

    Decision rule

    The veteran’s rule of thumb goes into the system — rules like “a temperature swing over 3 °C means defect risk”.

  3. 3

    Framing the problem

    Start where it hurts most. Interviews and data analysis put the biggest loss first.

  4. 4

    Execution

    The queries a root-cause analysis needs, handled in plain language — with which data and which formula were used shown alongside.

  5. 5

    Learning

    The rules get finer the more it is used. Patterns found in accumulated questions are proposed as the next rule candidates — whether to adopt them is your team’s call.

Cases

The four-stage flow, run on real sites

Company I case — Electronic components

Selected from a three-way competitive pitch

A three-way pitch. We had to prove technical depth and fit on the floor at the same time.

  1. 01Data map
  2. 02Work chatbot
  3. 03Schema-change automation
  4. 04Equipment-name validation
Key resultPoC completed on three AI agents
Read the full case

Choosing modules — FAQ

How the combination gets decided

Next step

Pick where to start

Month-end cost explained

  • Read-only connection to your systems
  • One plant · one product family · one close
  • Comparison · contribution · evidence
  • Starts in 4–6 weeks

AX kickoff guide

  • The three most common false starts
  • Four conditions for picking the first use case
  • A 90-day roadmap
  • A reporting frame for executives, plus a 10-point self-check

Technical review pack

  • Read-only integration and data flow
  • On-premise and air-gapped options
  • TLS 1.3 · AES-256 · SSO/RBAC
  • An engineer review call if you want one