Changelog

The problem

Funding is one of the clearest buying signals, but it usually reaches reps late, and keyword-based feeds often attach it to the wrong company.

How it works

1. 1GraphIQ.ai watches filings and news for Series A–D rounds.
2. 2Each round resolves to one canonical entity, so the signal lands on the right company and its corporate family.
3. 3The round appears in the entity’s Recent funding module and in the Signals feed within 24 hours of filing.
4. 4Signals also reach you by email digest, webhook, API pull or the MCP server.

Example

A Series B filing for a company in your ICP shows up on its entity page the next morning, with its parent company and any other active signals alongside it.

Try it

POST https://api.graphiq.ai/api/v2/news/search
X-API-Key: YOUR_WORKSPACE_KEY

# filter by organization ID, keyword, date or sentiment

Read the thinking

The problem

Traditional market search depends on SIC codes and boolean strings. Those describe how a company was classified years ago, not what it does today.

How it works

1. 1State your intent in plain English.
2. 2The graph parses it into a fingerprint across capability, scale, geography, hierarchy and signal dimensions.
3. 3Vector search and graph traversal run together across the graph.
4. 4Results come back ranked, with entity confidence scores, hierarchy context and signal overlays.

Matches are deliberately capped at a couple of thousand highly resolved entities, because accuracy drops sharply past that point.

Example query

Tier-2 precision machining suppliers to automotive OEMs in the Great Lakes region with ISO 9001 certification and 100–800 employees.

Read the thinking

The problem

Most agents can read business data but can’t record what they learn, so their findings stay in a chat transcript instead of the system of record.

How it works

1. 1Connect to the MCP server over streamable HTTP. Tools are discovered on the first handshake.
2. 2Authenticate with an X-API-Key for backend and CLI use, or OAuth 2.0 for interactive web apps.
3. 3Set read/write governance for the workspace.
4. 4Agents flag entities, update watch lists and route signals as ordinary tool calls.

Every session is isolated to its own tenant.

Try it

https://app.graphiq.ai/mcp

# works with Claude, OpenAI Agents SDK, LangChain, LangGraph or custom Python

Read the thinking

The problem

About 30% of a B2B contact list goes out of date each year. With quarterly batch refreshes, titles and numbers are already wrong on the day you export them.

How it works

1. 1Employee records re-resolve as new information arrives, instead of waiting for a quarterly batch.
2. 2Each direct dial carries a freshness score and a last-verified timestamp, so you can see how recently it was confirmed.

Tip

Sort call lists by freshness score so reps start with the numbers most likely to connect.

Read the thinking

The problem

Filter-based lookalikes match on industry code, headcount band and geography. That says little about how a company operates today.

How it works

1. 1Pick 3–5 anchor accounts, such as recent closed-won customers.
2. 2The graph analyzes their footprint across capabilities, signals and structural position.
3. 3Network-topology matching runs across the full graph.
4. 4Ranked results push to your CRM, Clay or sequencing tool over MCP or REST.

Try it

search_organizations
  similar = True
  anchors = 3–5 entity IDs from your best accounts

# full schema in Docs

Read the thinking

The problem

A deal with a subsidiary is often a way into its parent and sister companies, but flat account records hide that family.

How it works

1. 1Hierarchies resolve from the global ultimate parent down to local subsidiaries and acquired brands.
2. 2Acquisitions update the graph, and renamed subsidiaries keep their history.
3. 3Account pages now show dormant and acquired entities as part of the family.

Example

Search for Lucasfilm and you get the full 153-entity Disney corporate family, not an isolated row.

Read the thinking