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Data & AI Platform Blueprint Slide Template

Data and AI platform architecture: ingestion through storage, processing, and hero intelligence services up to consumption. Downward tonal ramp shows platform flow. Data sources on the left, consumers on the right or design-principles panel. Warm accent reserved for governance/model-risk cross-cutting. Best for lakehouse blueprints, AI operating model slides, analytics platform overviews, semantic-layer architecture, and AI-factory diagrams.

Try in Playground
Data & AI Platform Blueprint slide template preview — rendered with SlideForge
Cost
$0.03/slide
Speed
<1 second
Items
37
Output
.pptx + PDF + PNG

Parameters

Parameter
Description
title
{"type":"str","required":true}
layers
Platform layers top→bottom; consumption usually at top, ingestion at bottom
external_left
{"type":"object","required":false}
external_right
{"type":"object","required":false}
cross_cutting
{"type":"list","required":false}
commentary
{"type":"object","required":false}
takeaway
{"type":"str","required":false}

Example brief

Target data & AI platform: ingestion (batch, CDC, streaming, API), storage (lakehouse, operational store, metadata), processing (ETL, orchestration, quality), Data & AI Services (semantic layer, feature store, ML, vector search), consumption (dashboards, apps, reporting, AI copilots). Sources on the left, governance/observability/model risk cross-cutting, 3 design principles on the right.

API example

With a brief (async, $0.05)

curl -X POST https://api.slideforge.dev/v1/render \
  -H "Authorization: Bearer sf_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "template": "a8578b52-49c0-5848-a04e-4ee19ef83815",
    "brief": "Target data & AI platform: ingestion (batch, CDC, streaming, API), storage (lake..."
  }'

With structured params (sync, $0.03)

curl -X POST https://api.slideforge.dev/v1/render \
  -H "Authorization: Bearer sf_live_..." \
  -H "Content-Type: application/json" \
  -d '{
  "template": "a8578b52-49c0-5848-a04e-4ee19ef83815",
  "params": {
    "title": "Target data & AI platform — ingestion to intelligence",
    "layers": [
      {
        "id": "consumption",
        "label": "Consumption",
        "description": "Business products + interfaces",
        "items": [
          {
            "id": "dashboards",
            "label": "BI Dashboards"
          },
          {
            "id": "operational_apps",
            "label": "Operational Apps"
          },
          {
            "id": "executive_reports",
            "label": "Executive Reporting"
          },
          {
            "id": "copilots",
            "label": "AI Copilots",
            "emphasis": "high"
          }
        ]
      },
      {
        "id": "ai_services",
        "label": "Data & AI",
        "description": "Reusable intelligence services",
        "items": [
          {
            "id": "semantic_layer",
            "label": "Semantic Layer",
            "emphasis": "high"
          },
          {
            "id": "feature_store",
            "label": "Feature Store"
          },
          {
            "id": "ml_models",
            "label": "ML Models"
          },
          {
            "id": "vector_search",
            "label": "Vector Search"
          }
        ]
      },
      {
        "id": "processing",
        "label": "Processing",
        "description": "Transform + orchestration",
        "items": [
          {
            "id": "etl_elt",
            "label": "ETL / ELT Pipelines"
          },
          {
            "id": "orchestration",
            "label": "Workflow Orchestration"
          },
          {
            "id": "data_quality",
            "label": "Data Quality"
          },
          {
            "id": "business_rules",
            "label": "Business Rules"
          }
        ]
      },
      {
        "id": "storage",
        "label": "Storage",
        "description": "Structured + unstructured data",
        "items": [
          {
            "id": "lakehouse",
            "label": "Lakehouse"
          },
          {
            "id": "operational_store",
            "label": "Operational Store"
          },
          {
            "id": "metadata",
            "label": "Metadata Catalog"
          },
          {
            "id": "document_store",
            "label": "Document Store"
          }
        ]
      },
      {
        "id": "ingestion",
        "label": "Ingestion",
        "description": "Batch + streaming ingestion",
        "items": [
          {
            "id": "batch_ingest",
            "label": "Batch Ingestion"
          },
          {
            "id": "cdc",
            "label": "CDC"
          },
          {
            "id": "streaming",
            "label": "Streaming Events"
          },
          {
            "id": "api_ingestion",
            "label": "API Ingestion"
          }
        ]
      }
    ],
    "external_left": {
      "id": "data_sources",
      "label": "Data Sources",
      "items": [
        {
          "id": "erp",
          "label": "ERP"
        },
        {
          "id": "crm",
          "label": "CRM"
        },
        {
          "id": "iot",
          "label": "IoT"
        },
        {
          "id": "files",
          "label": "Files"
        },
        {
          "id": "third_party",
          "label": "3rd-Party"
        }
      ]
    },
    "cross_cutting": [
      {
        "id": "security",
        "label": "Security & Access"
      },
      {
        "id": "governance",
        "label": "Governance"
      },
      {
        "id": "observability",
        "label": "Observability"
      },
      {
        "id": "model_risk",
        "label": "Model Risk Mgmt"
      }
    ],
    "commentary": {
      "title": "Design principles",
      "items": [
        {
          "id": "p1",
          "label": "Separate persistent storage from transformation so data assets remain reusable"
        },
        {
          "id": "p2",
          "label": "Expose reusable AI services through a governed semantic layer, not point pipelines"
        },
        {
          "id": "p3",
          "label": "Treat governance, observability, and model risk as platform controls across the stack"
        },
        {
          "id": "p4",
          "label": "Decouple ingestion from consumption so they evolve at different rates"
        }
      ]
    },
    "takeaway": "Centralising storage + intelligence while decoupling ingestion and consumption scales platform reuse."
  }
}'

Tags

architecturedata platformai platformlakehousesemantic layerfeature storemlanalyticsoperating modelgovernancemodel riskdata mesh

Related templates

Frequently asked questions

How do I render a Data & AI Platform Blueprint slide?

Send a POST request to /v1/render with template "a8578b52-49c0-5848-a04e-4ee19ef83815" and your data in the params field. The response includes a .pptx download URL and PNG preview. Template renders cost $0.03 and complete in under 1 second.

What data does the Data & AI Platform Blueprint template accept?

The Data & AI Platform Blueprint template accepts: title, layers, external_left, external_right, cross_cutting, commentary, takeaway. Supports 3-7 items. See the parameter reference below for details.

Can I customize the colors and fonts of this template?

Yes — pass a theme_id parameter to apply your custom brand colors, fonts, and logo. SlideForge has 8 built-in themes, or you can create custom themes via the Themes API.

Try this template

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