Source identity
| Field | Recorded value |
|---|---|
| Source type | GitHub source |
| Original record | Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json |
| Date | Not specified in source |
| Repository | Janady13/AI_Brain |
| Revision | 11aa50ef5d7692e8abd1b8d74ae31c5c29201b14 |
| Branch | codex/build-online-github-ani-irqa7y |
| Path or archive member | daemon/datasets/aws_growth_playbooks.json |
| Original SHA-256 | 41c35bed29715b26859a18ac0a55262f74e8d95264cd00ecb6c8f822de364fd6 |
| Git blob SHA | f8ef5160f5f119cd2bb113a1e8dc03326cbd9de7 |
| Reading-copy SHA-256 | 41c35bed29715b26859a18ac0a55262f74e8d95264cd00ecb6c8f822de364fd6 |
Every recorded occurrence
These 23 records contain identical original source bytes. Their paths and revisions remain part of the archive.
| Original record | Branch or archive | Revision / identifier | Original location |
|---|---|---|---|
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | codex/build-online-github-ani-irqa7y | 11aa50ef5d7692e8abd1b8d74ae31c5c29201b14 | Open original |
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | codex/build-online-github-ani-hr4y0z | 366ab848896ae49159cfb3f6c201b8c5a731e9c8 | Open original |
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | codex/build-online-github-ani-i3cigl | 3a6e002311c48c27ffd41ef4a2a31c63947128cd | Open original |
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | codex/build-online-github-ani-hrtsoc | 50285aff347685f043e7a72e4d72891eebaa6cd6 | Open original |
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | codex/build-online-github-ani-7v4kug | 5cd0bbf744b0aee9445913337e852889d9a8098e | Open original |
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | copilot/convert-python-files-to-csharp | 6d0767aad08781eb5c4e1eb14a42ec92c437be78 | Open original |
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | codex/build-online-github-ani-nbgsix | 840945d70b88674d8f40d330001cc63d871dc8e2 | Open original |
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | copilot/vscode1760283895982 | 9e1c1a5b171addb5bb4a83b2ea7dede50c1cc81e | Open original |
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | codex/build-online-github-ani-jf5olo | c6653627c453ab428d665226167fa5f4aa05e9a5 | Open original |
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | feature/seeder-engine-tests | cdb497ece2d1a770ca8c83eb1de8923cfaa9eceb | Open original |
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | codex/build-online-github-ani-7nmb21 | d0f50071c85cddaaebbf9c4dd1313bd7676986da | Open original |
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | codex/build-online-github-ani-0etkns | e250b19d446bdffd542c9eea31cead12e30800d6 | Open original |
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | codex/build-online-github-ani-hr4y0z | 366ab848896ae49159cfb3f6c201b8c5a731e9c8 | Open original |
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | codex/build-online-github-ani-irqa7y | 11aa50ef5d7692e8abd1b8d74ae31c5c29201b14 | Open original |
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | codex/build-online-github-ani-i3cigl | 3a6e002311c48c27ffd41ef4a2a31c63947128cd | Open original |
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | copilot/convert-python-files-to-csharp | 6d0767aad08781eb5c4e1eb14a42ec92c437be78 | Open original |
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | codex/build-online-github-ani-7v4kug | 5cd0bbf744b0aee9445913337e852889d9a8098e | Open original |
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | codex/build-online-github-ani-nbgsix | 840945d70b88674d8f40d330001cc63d871dc8e2 | Open original |
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | copilot/vscode1760283895982 | 9e1c1a5b171addb5bb4a83b2ea7dede50c1cc81e | Open original |
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | codex/build-online-github-ani-7nmb21 | d0f50071c85cddaaebbf9c4dd1313bd7676986da | Open original |
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | feature/seeder-engine-tests | cdb497ece2d1a770ca8c83eb1de8923cfaa9eceb | Open original |
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | codex/build-online-github-ani-jf5olo | c6653627c453ab428d665226167fa5f4aa05e9a5 | Open original |
| Janady13/AI_Brain / daemon/datasets/aws_growth_playbooks.json | codex/build-online-github-ani-hrtsoc | 50285aff347685f043e7a72e4d72891eebaa6cd6 | Open original |
Recovered source transcript
Download complete source text ↓58 lines · 3.2 KB
[
{
"program_name": "AWS Pinpoint Conversion Lab",
"focus": "conversion",
"description": "Build omnichannel engagement journeys with Amazon Pinpoint and Personalize to recover abandoned funnels.",
"revenue_motion": "Re-activate high intent prospects via predictive messaging and targeted incentives.",
"primary_service": "Amazon Pinpoint",
"secondary_services": ["Amazon Personalize", "AWS Lambda", "Amazon S3"],
"automation_play": "Trigger journey orchestration and dynamic incentives using Lambda functions backed by Personalize segments.",
"base_roi": 3.4,
"focus_keywords": ["conversion", "pinpoint", "journey", "email", "sms", "re-engage"]
},
{
"program_name": "AWS Marketplace SaaS Launchpad",
"focus": "acquisition",
"description": "List curated SaaS offers on AWS Marketplace and automate co-sell motions.",
"revenue_motion": "Extend reach to AWS buyers and close Marketplace private offers faster.",
"primary_service": "AWS Marketplace",
"secondary_services": ["AWS IQ", "AWS Partner Central"],
"automation_play": "Sync Marketplace leads into CRM and orchestrate guided trials with EventBridge.",
"base_roi": 2.8,
"focus_keywords": ["marketplace", "acquisition", "listing", "aws", "co-sell"]
},
{
"program_name": "Serverless Attribution Fabric",
"focus": "analytics",
"description": "Instrument marketing attribution with AWS Glue, Redshift, and QuickSight dashboards.",
"revenue_motion": "Expose profitable channels and automatically rebalance spend into high-ROI cohorts.",
"primary_service": "Amazon Redshift",
"secondary_services": ["AWS Glue", "Amazon QuickSight", "AWS Step Functions"],
"automation_play": "Build an attribution lakehouse with Glue jobs and Step Functions orchestrations feeding QuickSight.",
"base_roi": 3.1,
"focus_keywords": ["analytics", "attribution", "data", "warehouse", "reporting"]
},
{
"program_name": "Personalization Revenue Pods",
"focus": "retention",
"description": "Deploy recommendation pods using Amazon Personalize and AppFlow connectors for lifecycle marketing.",
"revenue_motion": "Increase expansion revenue with product-led upsell moments and predictive offers.",
"primary_service": "Amazon Personalize",
"secondary_services": ["Amazon AppFlow", "Amazon EventBridge"],
"automation_play": "Pipe product usage into Personalize and trigger lifecycle campaigns via EventBridge rules.",
"base_roi": 3.7,
"focus_keywords": ["retention", "personalize", "upsell", "lifecycle", "expansion"]
},
{
"program_name": "AI Content Generation Forge",
"focus": "awareness",
"description": "Use Amazon Bedrock with SageMaker workflows to automate campaign assets at scale.",
"revenue_motion": "Produce high-performing creative variants while controlling compliance and brand voice.",
"primary_service": "Amazon Bedrock",
"secondary_services": ["Amazon SageMaker", "AWS Step Functions"],
"automation_play": "Train guardrails in SageMaker, orchestrate Bedrock prompts, and feed results to CMS pipelines.",
"base_roi": 2.9,
"focus_keywords": ["awareness", "creative", "content", "bedrock", "sagemaker"]
}
]