{
"signal_id": "1777527738714483700",
"manifest_id": "1777525429698648000",
"record_id": "1777525429649909800",
"stream_id": "17723038993580068",
"claim": "AI adoption is blocked by how companies are built, not by model capability.",
"claim_type": "analysis",
"evidence": "paraphrase",
"confidence": "stated",
"sentiment": "neutral",
"significance": "high",
"source": {
"name": "Mayfield Blog",
"source_type": "organization",
"media_format": "text",
"published_date": "2026-04-30"
},
"tags": {
"organizations": ["Mayfield"],
"people": ["CIOs", "AI founders"],
"topics": ["AI adoption", "workflow design", "enterprise AI"]
},
"domain_classification": {
"home_domain": "engineering-technology",
"cross_domains": ["economics-business-work", "society-law-government", "people-biography-history"]
},
"evidence_ref": {
"source_url": "https://mayfield.com/...",
"quote_mode": "paraphrase",
"source_span": "AI's primary barrier to adoption is not technological models..."
}
}
The Manifest is the product.
Synorb turns source events into machine-readable intelligence objects. Every Manifest keeps the source, the structured record, the brief, and the extracted signals together with stable IDs and provenance.
One source event. Three machine-native views.
A Manifest is the complete unit Synorb writes. Agents can inspect the canonical JSON, read a concise Brief, or act on individual Signals without losing lineage back to the original source.
AI's primary barrier to adoption is not technological models but how companies are structured and operate. Founders must focus on rebuilding workflows for AI, not just optimizing existing systems.
{
"manifest_id": "1777525429698648000",
"record_id": "1777525429649909800",
"stream_ids": ["17723038993580068"],
"stream_names": ["mayfield"],
"home_domain": "engineering-technology",
"source": {
"name": "Mayfield Blog",
"media_format": "text",
"claim_type": "publication",
"published_date": "2026-04-30"
},
"entities": {
"organizations": ["Mayfield", "OpenAI", "Microsoft"],
"people": ["CIOs", "AI founders"],
"topics": ["AI adoption", "workflow design", "enterprise AI"]
},
"outputs": {
"signals_count": 76,
"brief_id": "1777527738510498300",
"record_version": 1
},
"domain_classification": {
"home_domain": "engineering-technology",
"cross_domains": [
"economics-business-work",
"society-law-government",
"people-biography-history"
]
},
"provenance": {
"source_url": "https://mayfield.com/...",
"captured_at": "2026-04-30T05:42:48.748631+00:00",
"matched_at": "2026-04-30T05:42:49.006112+00:00",
"pipeline_version": "synorb-unified-story-v3",
"schema_version": "manifest/1"
},
"lineage": {
"source_channel_id": "mayfield-blog",
"stream_id": "17723038993580068",
"record_id": "1777525429649909800",
"brief_id": "1777527738510498300",
"signal_ids": [
"1777527738714483700",
"1777527738801269800",
"1777527738893154300"
]
},
"delivery": {
"formats": ["signal", "brief", "record"],
"interfaces": ["REST", "MCP", "webhook", "S3"],
"refresh_cadence": "continuous"
}
}
{
"brief_id": "1777527738510498300",
"manifest_id": "1777525429698648000",
"stream_name": "mayfield",
"headline": "CIOs frame AI adoption as workflow reconstruction",
"summary": "The article argues that enterprise AI value depends on rebuilding decision paths, operating models, and human workflows around machine capability.",
"key_points": [
"Enterprise AI adoption is framed as an organizational design problem rather than a model quality problem.",
"Founders should build around workflow reconstruction, decision latency, and operating model change.",
"The CIO perspective shifts the sales conversation from model performance to implementation path."
],
"notable_quotes": [],
"actionable_takeaways": [
"Map the workflow before selling the AI layer.",
"Show how the customer organization changes once the system is adopted.",
"Treat change management as product surface area."
],
"source_urls": ["https://mayfield.com/..."],
"published_date": "2026-04-30",
"home_domain": "engineering-technology"
}
{
"record_id": "1777525429649909800",
"manifest_id": "1777525429698648000",
"title": "3 Things CIOs Know That Every AI Founder Should Understand - Issue #24",
"url": "https://mayfield.com/...",
"source_name": "Mayfield Blog",
"source_channel": "mayfield-blog",
"source_type": "organization",
"media_format": "text",
"claim_type": "publication",
"content": "AI's primary barrier to adoption is not technological models but how companies are structured and operate...",
"published_date": "2026-04-30",
"captured_at": "2026-04-30T05:42:48.748631+00:00",
"entities": {
"organizations": ["Mayfield", "OpenAI", "Microsoft"],
"people": ["CIOs", "AI founders"],
"places": [],
"topics": ["AI adoption", "workflow design", "enterprise AI"]
},
"routing": {
"stream_names": ["mayfield"],
"home_domain": "engineering-technology",
"cross_domains": ["economics-business-work", "society-law-government", "people-biography-history"]
},
"version": 1
}
Same event. Different jobs.
The format choice is not cosmetic. Signals are for reasoning and alerts. Briefs are for dashboards and RAG context. Records are for joins, lineage, replay, and warehouse-grade use.
Atomic assertions
Claims with evidence, confidence, sentiment, source, tags, and date. Built so agents can reason over the smallest useful unit.
Compact narrative
A source-aware summary that keeps the important claims together. Built for dashboards, digests, and retrieval-augmented systems.
Canonical JSON
The structured content object with stable identifiers, provenance, versioning, source metadata, and machine-joinable fields.
What produces a Manifest?
Source Channels are the exact surfaces Synorb watches. Streams are the canonical rollups agents subscribe to. The Manifest is what gets written when those watched surfaces publish.
The publishing surface
Blogs, filings, podcasts, feeds, reports, social posts, data releases, research pages, and other surfaces.
The canonical subscription
A saved query or entity rollup that bundles related channels under a durable object agents can follow.
The machine object
Signal, Brief, and Record views with stable IDs, provenance, lineage, domain classification, and typed tags.
Streams start from three primitive shapes.
Synorb does not treat every source as a loose feed. Streams resolve to primitives that agents can reason about: organizations, people, and datasets.
Organizations
Companies, labs, banks, governments, publishers, funds, universities, agencies, and public institutions.
People
Founders, operators, researchers, investors, executives, policymakers, creators, and domain specialists.
Datasets
Company filings, research reports, economic indicators, podcasts, corporate blogs, statistical releases, and more.
A slice of the primitive inventory.
The matrix is intentionally broad: organizations, people, and datasets live together because agents need to join them together. A person can cite a company; a company can publish a data point; a dataset can move a market narrative.
A Stream bundles every channel an entity publishes.
Alphabet is one Stream. It includes developer, research, cloud, AI, product, and corporate channels. An agent subscribes to the Stream and receives Manifests from every linked Source Channel.
The top-level ontology for every Manifest.
Every Manifest has one home_domain: the primary place an agent should file it. Cross-domains preserve the secondary context, but the home domain keeps the graph stable.
Markets, management, financial institutions, employment, company updates, allocation, and business operations.
Engineering work, platforms, developer tools, AI research, cloud, semiconductors, cybersecurity, and technical strategy.
Public institutions, legislation, regulation, elections, courts, agencies, civic infrastructure, and government action.
Healthcare delivery, biotech, clinical research, public health, pharmaceuticals, medical devices, and life sciences.
Environment, agriculture, energy transition, weather impacts, climate systems, conservation, and resource use.
Physics, chemistry, mathematics, materials, measurement, scientific discovery, and quantitative research.
Film, television, music, games, sports, cultural industries, media production, and entertainment markets.
Careers, biographies, leadership changes, historical narratives, institutional memory, and individual influence.
Books, essays, rhetoric, translation, language systems, literary work, and communication as a domain.
Consumer behavior, work practices, education, how-to knowledge, local decisions, and usable everyday context.
Geographic context, regional economies, urban systems, geopolitics, demographics, and physical place.
Space, astronomy, planetary science, earth systems, geophysics, and phenomena beyond local human systems.
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