Your agent shouldn't wait for the web to load
Search APIs crawl after your agent asks, seconds of latency, unranked results, and a parsing step you have to write. Incord ingested and embedded the world already. One /v1/search call returns ranked, validated facts in under 50ms.
GET YOUR API KEYWhat you get from the layer
Latency your users don't notice
A crawl-on-demand search API adds seconds to every grounded response. Pre-ingested retrieval returns in tens of milliseconds, which is the difference between an assistant that feels instant and one users abandon mid-answer.
No pipeline to build or maintain
No scraper, no chunker, no embedding job, no vector database to operate and pay for. The retrieval layer arrives as an endpoint.
Answers you can defend
Facts are validated by quorum before they're served and returned with citations, so your agent isn't confidently repeating a single unverified source.
Where teams put it to work
Customer-facing assistants
Ground product, policy and market answers in current fact instead of training data that ended months ago.
Research and briefing agents
Query global news and events semantically, ranked by freshness and relevance, in a single call rather than orchestrating ten searches.
Multi-agent systems
Every agent in the swarm reads the same knowledge layer and writes to the same memory graph, so they don't each rediscover the same facts.
Personal assistants with continuity
Global Memory means the agent remembers the user across sessions, and across agents, without a separate vector store.
Drop-in replacement
MCP, OpenAI and Tavily-compatible interfaces mean swapping your existing retrieval layer is a config change, not a rewrite.
What the endpoint gives you
- /v1/search
One endpoint, ranked and reranked results
- Freshness control
Request live, recent or historical context explicitly
- Six-dimension filtering
Narrow by source, time, domain, entity and more
- Global Memory Graph
Persistent cross-agent user context, no external vector DB
- Pre-embedded index
Retrieval, not crawling
- Quorum validation
BFT-validated facts with citations
- Self-hostable
Run privately where the deployment requires it
Questions teams ask first
- How is this different from a search API like Tavily or Exa?
- They crawl when you ask. Incord ingests continuously on a ten-minute cycle, embeds and validates ahead of time, then serves from that index. You're reading a prepared index rather than triggering a fetch, which is why latency is an order of magnitude lower.
- Do we still need a vector database?
- No. Embeddings live with the knowledge layer, and Global Memory handles persistent user context, the two things most teams stand up a separate vector store for.
- Can we use our own models?
- Yes. Incord is model-agnostic; the Unified LLM API routes to whichever model you choose, or you bring your own.
- How do we migrate from our current retrieval setup?
- Drop-in compatible interfaces for MCP, OpenAI and Tavily formats mean most teams swap the endpoint and keep their existing call structure.
- What happens when we ask about something not yet indexed?
- Watchdog detects the gap and auto-spawns an ingestion pipeline for it, so the second request for that topic is served from the index.
- Is there a free tier?
- Yes, start free and move to usage-based pricing.