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DARVIS · industrial AI data platform

DARVIS, the industrial AIplatform that turns floor data into decisions

Four stages — from Knowledge Map (stage 1) to Admin (the operating stage),
each with a deliverable of its own.
These stages are the process of installing this platform. The 4–6 weeks in the cost-anomaly detection-and-response cycle (Profit OS) is a separate flow that runs on top of it — the two count different things.

  • Analyses closed118118 of 161 runs since 2022 closed · 0 failed · 43 open excluded
  • 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 input for the next.

01.DISCOVERYStage 1 · about 1–2 weeks
02.AGENTICStage 2 · about 1 week
03.AUTOStage 3 · about 1 week
04.OPSOperating stage
Knowledge Map01Stage 1 · about 1–2 weeks
Knowledge Map01Stage 1 · about 1–2 weeks
  • Automatic scan
  • Data map
  • Consistency report
Agentic Workflow02Stage 2 · about 1 week
Agentic Workflow02Stage 2 · about 1 week
  • Process manuals
  • AI assistant
  • Automatic citations
Automation03Stage 3 · about 1 week
Automation03Stage 3 · about 1 week
  • Automatic alerts
  • 5 rule types
  • Slack and Teams integration
Admin04Operating stage
Admin04Operating stage
  • 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.

DARVIS is the data-and-judgement structure that Profit OS and the company’s other decision tools all stand on.

  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

Consultation

  • A 30-minute call to decide what to look at first
  • Read-only connection to your systems
  • A narrow first scope, live in 4–6 weeks
  • Early signal · cause · evidence

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