Case StorySituation-challenge-approach-result

Situation-challenge-approach-result

Case study on reducing transit delays through automated passenger flow management.

Try in Playground

Situation-challenge-approach-result slide template rendered with SlideForge

Cost

$0.05/slide

Engine

Deterministic

Pattern

Case Story

Output

.pptx + PDF + PNG

Example brief

A detailed case study for a regional transit authority showing how they addressed peak-hour dwell time delays. The slide outlines the situation of platform overcrowding, the challenge of a 15% ridership spike, the approach of using LiDAR and real-time data for load balancing, and the results showing a 35% reduction in delays. It includes a dedicated results panel with hero metrics and operational proof points.

Render it

Structured intent — create_slide payload

Copy-paste-ready. Renders deterministically (verbatim, no LLM). Add a theme_id for any of the built-in themes — the layout stays identical.

{
  "form": "case_story",
  "variant": "scar_result_panel",
  "headline": "Metrolink reduced dwell-time delays by 35% through automated platform load balancing",
  "context": "Real-time passenger flow optimization - municipal transit case study",
  "takeaway": "Efficiency gains were driven by better passenger distribution rather than increasing train speeds.",
  "source_note": "Internal operations audit; Metropolitan Transit Authority 2025 annual report",
  "data": {
    "story_steps": [
      {
        "label": "Situation",
        "detail": "Passenger surges at central hubs caused uneven boarding across train cars; station staff relied on visual cues and manual radio dispatch to manage crowds."
      },
      {
        "label": "Challenge",
        "detail": "Peak ridership increased 15% following line expansions, dwell times exceeded 90 seconds at hub stations, and cascading delays impacted 30% of morning services."
      },
      {
        "label": "Approach",
        "detail": "Over 6 months: deployed LiDAR-based platform sensors, integrated real-time car-occupancy data into station signage, and automated conductor dispatch alerts."
      },
      {
        "label": "Result",
        "detail": "Mean dwell time dropped to 58 seconds, passenger distribution across cars improved significantly, and system-wide punctuality reached a five-year high."
      }
    ],
    "results": [
      {
        "value": "-35%",
        "label": "Dwell-time delays",
        "sub": "90s -> 58s average",
        "tone": "good"
      },
      {
        "value": "+28%",
        "label": "On-time performance",
        "sub": "morning peak frequency",
        "tone": "good"
      }
    ],
    "proof_notes": [
      "45 stations - 220 rail cars - Q1-Q3 2025 rollout",
      "Safety incidents during boarding decreased by 12%"
    ]
  }
}

cURL (REST)

curl -X POST https://api.slideforge.dev/v1/render/intent \
  -H "Authorization: Bearer sf_live_..." \
  -H "Content-Type: application/json" \
  -d '{"form":"case_story","variant":"scar_result_panel","headline":"Metrolink reduced dwell-time delays by 35% through automated platform load balancing","context":"Real-time passenger flow optimization - municipal transit case study","takeaway":"Efficiency gains were driven by better passenger distribution rather than increasing train speeds.","source_note":"Internal operations audit; Metropolitan Transit Authority 2025 annual report","data":{"story_steps":[{"label":"Situation","detail":"Passenger surges at central hubs caused uneven boarding across train cars; station staff relied on visual cues and manual radio dispatch to manage crowds."},{"label":"Challenge","detail":"Peak ridership increased 15% following line expansions, dwell times exceeded 90 seconds at hub stations, and cascading delays impacted 30% of morning services."},{"label":"Approach","detail":"Over 6 months: deployed LiDAR-based platform sensors, integrated real-time car-occupancy data into station signage, and automated conductor dispatch alerts."},{"label":"Result","detail":"Mean dwell time dropped to 58 seconds, passenger distribution across cars improved significantly, and system-wide punctuality reached a five-year high."}],"results":[{"value":"-35%","label":"Dwell-time delays","sub":"90s -> 58s average","tone":"good"},{"value":"+28%","label":"On-time performance","sub":"morning peak frequency","tone":"good"}],"proof_notes":["45 stations - 220 rail cars - Q1-Q3 2025 rollout","Safety incidents during boarding decreased by 12%"]}}'

Frequently asked questions

How do I render the Situation-challenge-approach-result template?

Send a POST to /v1/render/auto with a brief, or call create_slide via MCP with the structured intent shown below. The response includes an editable .pptx, a PDF, and a PNG. $0.05 per slide.

Does an AI write the slide?

No. SlideForge renders deterministically from a structured intent — no LLM draws the slide, so text is never mangled or fabricated. The optional brief path uses a router to pick the pattern, then renders deterministically.

Can I customize the colors and fonts?

Yes. Pass a theme_id to apply a built-in or custom brand theme; the layout stays the same.

Try this template

Render Situation-challenge-approach-result deterministically — editable PowerPoint in under a second.

Try in Playground Sign Up Free