[ Deployment planning · browser local ]

Choose an AI boundary you can explain and test.

The AI Boundary Planner turns a stated workload into a transparent local, cloud, or hybrid planning recommendation. It shows the inputs that shaped the result and exports a reviewable decision record. Your entries stay in this browser.

[ What it does ]

Makes data movement, capability needs, operational constraints, and testing gaps visible in one decision brief.

[ What it exports ]

A machine-readable planning record with recommendation logic, open questions, and a review date.

[ What it does not do ]

It does not rank vendors, certify privacy or security, measure your system, or make an operational decision.

[ 01 · describe the workload ]

Decision or pilot nameTask, user, and human approval pointAccountable owner

[ 02 · state the conditions ]Input sensitivityHow material would an unnecessary transfer or exposure of the input be?Offline continuityHow much must the task remain useful through poor connectivity?Response-time needHow much does the experience depend on short, predictable response time?Model capabilityHow much does the task require broad reasoning, retrieval, scale, or multimodal work?Shared context & controlHow much does the task require centralized data, updates, or organizational administration?Target device readinessHow ready is the least capable supported device for local execution?Budget predictabilityHow important is a predictable operating cost?

Assumptions, exclusions, or data limits

[ 03 · planning result ]

On-device first

This is a planning hypothesis produced from the conditions you selected. It is not a product, vendor, security, or performance recommendation.

Local signals

Cloud signals

Why this boundary

· The workload has personal, proprietary, or otherwise sensitive inputs; map and minimize any remote transfer.
· The workflow needs some continuity through weak or unavailable connectivity.
· The task needs non-trivial model capability, context, retrieval, or multimodal work.

Review before implementation

· Name the decision or pilot before treating this as a complete record.
· Define the task, user, quality threshold, and human approval point.
· Assign an accountable owner who can accept output, review failures, and stop the pilot.
· Test the least capable supported device for storage, memory, heat, battery, accessibility, and expected response time.
· Document collection, retention, telemetry, sync, access, and any authorized external processor—not just model location.
· Test quality on representative cases and record correction, escalation, and abstention behavior.
· Specify failure and fallback behavior for a device, network, runtime, or provider outage.

{
  "schemaVersion": "maha-ai-boundary-record/0.1",
  "id": "288577b0-3117-4b06-8c2c-6087f7a8ddb1",
  "generatedAt": "2026-07-29T15:45:36.090Z",
  "status": "needs_review",
  "decision": {
    "title": "",
    "useCase": "",
    "accountableOwner": ""
  },
  "inputs": {
    "dataSensitivity": "medium",
    "offlineNeed": "medium",
    "latencyNeed": "medium",
    "capabilityNeed": "medium",
    "sharedContext": "low",
    "deviceReadiness": "medium",
    "budgetPredictability": "medium"
  },
  "recommendation": {
    "architecture": "On-device first",
    "localSignalScore": 4,
    "cloudSignalScore": 1,
    "drivers": [
      "The workload has personal, proprietary, or otherwise sensitive inputs; map and minimize any remote transfer.",
      "The workflow needs some continuity through weak or unavailable connectivity.",
      "The task needs non-trivial model capability, context, retrieval, or multimodal work."
    ],
    "reviewQuestions": [
      "Name the decision or pilot before treating this as a complete record.",
      "Define the task, user, quality threshold, and human approval point.",
      "Assign an accountable owner who can accept output, review failures, and stop the pilot.",
      "Test the least capable supported device for storage, memory, heat, battery, accessibility, and expected response time.",
      "Document collection, retention, telemetry, sync, access, and any authorized external processor—not just model location.",
      "Test quality on representative cases and record correction, escalation, and abstention behavior.",
      "Specify failure and fallback behavior for a device, network, runtime, or provider outage."
    ],
    "pilotTestPlan": [
      "Set a baseline: run at least 10 representative cases through the current workflow and record quality, turnaround time, correction effort, and exception rate.",
      "Run the proposed boundary on the same representative cases. Keep human approval in the stated control point and record failures or escalations.",
      "Test the least capable supported device under expected storage, memory, battery, heat, accessibility, and connectivity conditions; document unsupported cases.",
      "Review the end-to-end data map: capture, local storage, telemetry, sync, logs, remote escalation, retention, access, and deletion behavior.",
      "Review the results with the accountable owner and choose one outcome: continue, revise the boundary, or stop the pilot."
    ]
  },
  "limits": [
    "This is a browser-local planning aid; it does not contact vendors, devices, or external systems.",
    "The recommendation is not a privacy, security, cost, performance, legal, procurement, or sovereignty certification.",
    "Validate representative workloads, real device and network conditions, data flows, and operational controls before implementation."
  ],
  "references": [
    "https://www.mahastrategies.com/mps/learn/implementation",
    "https://www.mahastrategies.com/on-device-ai-vs-cloud"
  ]
}

Maha AI Boundary Planner · reviewable decision brief

Untitled AI boundary decision

Generated locally · 7/29/2026

Recommended planning boundary

On-device first

This is a planning hypothesis based only on the stated conditions below. It is not a vendor selection, privacy or security certification, performance benchmark, or implementation approval.

Workload & accountability

Use case: Not yet specified.
Accountable owner: Not yet assigned.

Selected conditions

Input sensitivity: Moderate
Offline continuity: Moderate
Response-time need: Moderate
Model capability: Moderate
Shared context & control: Low / limited
Device readiness: Moderate
Budget predictability: Moderate

Decision drivers

• The workload has personal, proprietary, or otherwise sensitive inputs; map and minimize any remote transfer.
• The workflow needs some continuity through weak or unavailable connectivity.
• The task needs non-trivial model capability, context, retrieval, or multimodal work.

Bounded pilot test plan

Set a baseline: run at least 10 representative cases through the current workflow and record quality, turnaround time, correction effort, and exception rate.
Run the proposed boundary on the same representative cases. Keep human approval in the stated control point and record failures or escalations.
Test the least capable supported device under expected storage, memory, battery, heat, accessibility, and connectivity conditions; document unsupported cases.
Review the end-to-end data map: capture, local storage, telemetry, sync, logs, remote escalation, retention, access, and deletion behavior.
Review the results with the accountable owner and choose one outcome: continue, revise the boundary, or stop the pilot.

Questions to close before expansion

• Name the decision or pilot before treating this as a complete record.
• Define the task, user, quality threshold, and human approval point.
• Assign an accountable owner who can accept output, review failures, and stop the pilot.
• Test the least capable supported device for storage, memory, heat, battery, accessibility, and expected response time.
• Document collection, retention, telemetry, sync, access, and any authorized external processor—not just model location.
• Test quality on representative cases and record correction, escalation, and abstention behavior.
• Specify failure and fallback behavior for a device, network, runtime, or provider outage.

Method: https://www.mahastrategies.com/mps/learn/implementation · This brief is generated in the browser and is not retained by Maha Strategies.

[ Optional human review ]

Attach this decision record to an inquiry—only if you choose.

The planner does not retain your entries. Opening this form still sends nothing. If you check the consent box and submit, the current JSON record and your inquiry details are transmitted to Maha Strategies' inquiry ledger for human scope review.

Use the result well

A planner result is a starting hypothesis. Validate it with representative inputs, real target devices and networks, data-flow review, and an accountable owner before expanding. Read the AI implementation framework for the underlying method.