AI search for RAG

An AI search API for RAG should return retrieval-ready evidence.

RAG teams often use search to fill freshness gaps, but search output still needs to become structured, deduplicated, citable retrieval input.

Synorb gives RAG systems source-grounded Manifests from watched coverage so current context can enter the pipeline with provenance attached.

MCP · REST · Source URLs · Stable IDs · Manifests

What should the data layer include?

For search and feed input for retrieval-augmented generation systems, the useful unit is not a loose search result. It is an object the agent can retrieve, cite, filter, store, and audit.

Freshness

Updated source context

Use feeds when the agent needs current information beyond model training data and static documentation.

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.

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"]
}

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.

Short answers for AI builders.

What is an AI search API for RAG?

It is an API used to find current external context that can support retrieval-augmented answers.

What should RAG teams do after search?

They should preserve source URLs, dates, stable IDs, summaries, tags, and provenance before indexing or answering.

Where does Synorb fit?

Synorb supplies recurring source-grounded Manifests for known coverage areas, reducing repeated search and cleanup work.

Does Synorb replace search?

No. Search remains useful for open discovery. Synorb is strongest where the sources, topics, or sectors need ongoing monitoring.

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

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.