Freshness
Updated source context
Use feeds when the agent needs current information beyond model training data and static documentation.
AI app data layer
An AI app data layer should separate fresh context from model logic.
AI applications need prompts, models, retrieval, and tools. They also need a data layer that keeps changing external context current and source-backed.
Synorb provides that feed-first data layer: watched sources become Streams of Manifests that applications can retrieve, store, cite, and render.
MCP · REST · Source URLs · Stable IDs · Manifests
01 / Criteria
What should the data layer include?
For the fresh source-grounded data layer behind AI applications, the useful unit is not a loose search result. It is an object the agent can retrieve, cite, filter, store, and audit.
Grounding
Evidence stays attached
Source URLs, dates, and stable IDs help the application cite, inspect, and audit what the model used.
Delivery
MCP, REST, webhooks, and archives
Agents can explore through Core MCP. Production systems use REST and webhooks for current delivery. The live window covers the current calendar month plus the previous three full months; S3 archive exports support historical backfills and replay for older months.
02 / Shape
A Manifest is the object the agent can use.
This JSON manifest is the source-grounded object delivered through MCP or REST. It is compact enough for an agent workflow and explicit enough for an application to store, cite, and audit.
Manifest excerptJSON
{
"manifest_id": "1777525429698648000",
"headline": "Source-grounded update for an AI workflow",
"summary": "What changed, why it matters, and what source supports it.",
"source": {
"name": "Watched source",
"url": "https://source.example/update",
"published_date": "2026-06-21"
},
"delivery": {
"mcp": "https://mcp.synorb.com/mcp",
"rest": "https://api.synorb.com"
},
"tags": ["company", "topic", "source-backed"]
}
03 / Fit
Where Synorb fits in the workflow.
Use Synorb when your team already knows the sources or topics it needs to monitor, and the workflow needs current context again and again. Use search or crawling for open-ended discovery.
Agents
Pull live context
Use Synorb MCP to discover Streams, inspect details, and retrieve Manifests inside an agent workflow.
RAG
Load before prompts
Push source-grounded Manifests into retrieval stores before users ask for current answers.
Apps
Render with citations
Build dashboards, feeds, monitors, and briefings with source URLs available at display time.
FAQ
Short answers for AI builders.
What is an AI app data layer?
It is the infrastructure that supplies an AI application with the current data, metadata, and source evidence it needs.
Where does Synorb fit?
Synorb sits before the model and retrieval layer, delivering source-grounded Manifests from watched source coverage.
What delivery options work for AI apps?
Use MCP for agent-assisted workflows, REST for backend calls, webhooks for push updates, and S3 archive exports for backfills and replay.
Why keep the data layer separate?
Separating context from prompts makes the application easier to refresh, audit, route, and change over time.
Test
Test Synorb feeds for free.
Want to connect to Synorb's graph to test source-grounded feeds for free? Start with free test credentials, then connect through Core MCP or REST.
Free test credentialscurl
curl -s https://synorb.com/connect
Start
Give your agent fresh source-backed context.
Start with keys, then connect through Core MCP while building or REST when your application owns the workflow.