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01 GOAL
Which decision should improve
Define the time saved, risk reduced, or conversion lifted before choosing tools.
Turn data and AIinto a decision system that runs.
From data foundations and AI workflows to operating routines, we compose scattered tools into a system teams can use, validate, and improve every day.
MENTARIX DECISION CORESYSTEM ONLINE
INPUT / 01
›Which decision should improve first?
Composing an executable route
- 01Goal
Lock the decision and acceptance criteria
- 02Data
Map signals, gaps, and reliability
- 03Flow
Embed the system in daily work
- 04Proof
Ship against real usage and outcomes
A phase-one system, designed to ship.READY_
CHECKLIST
What we check in the first discussion
The first discussion covers five items: goal, data sources, users, workflow placement, and acceptance criteria. After that, we recommend whether to start with dashboards, pipelines, RAG, or automation.
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02 DATA
Where the data lives
Map systems, spreadsheets, reports, and documents so gaps are visible.
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03 USER
Who will use it daily
Separate what executives, operators, support, and product teams each need to do.
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04 FLOW
Where it enters the workflow
Confirm inputs, review steps, notifications, records, and exception handling.
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05 CHECK
How success is measured
Set acceptance criteria, ownership, and iteration rules before development.
reviewed in the first discussion
Which decision should improveGoal + metricWhere data is scattered todaySource mapWhich reports are still manualWorkload costWho uses it every dayRoles + scenesWhere review happensWorkflow fitWho should not see whatAccess rulesHow fresh data must beRefresh cadenceHow phase one is acceptedSuccess criteriaWhich decision should improveGoal + metricWhere data is scattered todaySource mapWhich reports are still manualWorkload costWho uses it every dayRoles + scenesWhere review happensWorkflow fitWho should not see whatAccess rulesHow fresh data must beRefresh cadenceHow phase one is acceptedSuccess criteriaWhich decision should improveGoal + metricWhere data is scattered todaySource mapWhich reports are still manualWorkload costWho uses it every dayRoles + scenesWhere review happensWorkflow fitWho should not see whatAccess rulesHow fresh data must beRefresh cadenceHow phase one is acceptedSuccess criteria
SERVICES
Common starting points
Use common situations to choose the first move, then turn the need into a phase-one route that can be scoped, validated, and handed over.
PACKAGES
First-phase packages you can start with
If you are not sure which service to buy, use the situation, output, and preparation notes to choose the first move.
012 weeksData Audit SprintMap sources, fields, access rules, and phase-one acceptance criteria.View full package023–4 weeksDashboard MVPTurn operations, finance, or marketing KPIs into a usable decision view.View full package033–5 weeksRAG Knowledge BaseHelp teams find approved answers across documents, support records, or internal knowledge.View full package044–6 weeksAI Workflow PilotPut AI-assisted judgement into a workflow while keeping human review and logs.View full package
BUILD ITEMS
Where should you start?
common build items
Ops / finance KPI dashboardsDecision viewData cleanup and field definitionsMaintainable baseAutomated data pipelinesLess manual workDocument knowledge base + RAGFind approved answersInternal AI assistantWorkflow entryForecasting and risk groupingEarlier signalsAccess logs and monitoring rulesAuditable handoffRunbooks and team trainingOwned by teamOps / finance KPI dashboardsDecision viewData cleanup and field definitionsMaintainable baseAutomated data pipelinesLess manual workDocument knowledge base + RAGFind approved answersInternal AI assistantWorkflow entryForecasting and risk groupingEarlier signalsAccess logs and monitoring rulesAuditable handoffRunbooks and team trainingOwned by teamOps / finance KPI dashboardsDecision viewData cleanup and field definitionsMaintainable baseAutomated data pipelinesLess manual workDocument knowledge base + RAGFind approved answersInternal AI assistantWorkflow entryForecasting and risk groupingEarlier signalsAccess logs and monitoring rulesAuditable handoffRunbooks and team trainingOwned by team
PROCESS
How We Work
Confirm the problem and data first, then design the first workflow. Each stage leaves a reviewable document, prototype, or system version.
STAGE 1
Strategy
Understand the business goals, data state, team constraints, and AI priorities.
ACCEPTANCE
The team can explain why it matters, what starts first, and how success will be judged.
01 INPUT
Stakeholder interviews, sample data, decision pain
02 ACTION
Clarify the real problem and rank AI/data investment order
03 OUTPUT
Launch roadmap
DELIVERABLES
01
Decision problem
02
Data maturity audit
03
First testable use case
START / 01