MEERKAT OS

The relationship graph that finds the warmest path to anyone.

Meerkat maps your CRM, inboxes, and team networks into one graph, then ranks the warmest path to any person. Four verified edge types, every one sourced.

FOUR VERIFIED EDGE TYPES

Four Verified Edge Types. Every One Sourced.

Most graphs guess at who knows whom.
Meerkat builds every edge from licensed data and resolves it into one trusted record per person.
Only relationships real enough to act on.

Career

From professional and education data

  • Who worked together, and for how long
  • Shared employers, teams, and tenure
  • Alumni and classmate overlaps

Board

From professional and education data

  • Shared board and advisory seats
  • Co-investors and committee ties
  • The high-trust relationships that open doors

Neighbor

From property data

  • Verified neighbors resolved from property records
  • Grounded in real, confirmed addresses
  • Local ties that cold data never sees

Household

From property data

  • The people who share a home
  • Resolved from property and contact data
  • Family and household relationships
A NEW CATEGORY

Contact Data Tells You Who. The Graph Tells You Who Can Reach Them.

Contact databases give you a record. Enrichment tools fill in a job title. Relationship tools map your own inbox alone. Meerkat unifies the relationships across your whole team into one graph, then ranks the warmest path to any person, so your AI agents and your team always know who can open the door.

An introduction lands differently when it comes through someone the other person already trusts. Meerkat finds that person, and the verified path to them, every time.
That's not contact data. That's the warmest path made visible.

HOW IT WORKS

Four Layers. One Relationship Graph.

Meerkat OS turns scattered relationships into warm paths through four layers that build on each other: licensed data, the relationships graph, ranking signals, and connectivity to every AI surface.

1

LAYER 1: DATA LAYER

Licensed Data

The foundation. Licensed professional, education, and property data, resolved against contact records into one trusted record per person.

ProfessionalEducationProperty
2

LAYER 2: RELATIONSHIP GRAPH

Who Knows Whom

The map. Four verified edge types connect every person: career, board, neighbor, and household. The graph of who actually knows whom.

CareerBoardNeighborHousehold
3

LAYER 3: SIGNALS

Ranking, Recency, Confidence

The instinct. Every path is scored by source, recency, confidence, and context, so the strongest route to a person surfaces first.

RankingRecencyConfidence
4

LAYER 4: CONNECTIVITY LAYER

MCP and API Connectivity

The connection. Call the graph from every AI surface through one MCP and API layer. Claude, ChatGPT, the CRM, or your team.

MCPRESTCRMAgents

And it compounds. Every intro and every reply teaches the graph which paths convert, so each answer is sharper than the last.

BUILD ON MEERKAT OS

Your App Queries the Graph.
The Warmest Path Comes Back.

Build in any stack and ask the graph one question: who is the warmest path to this person?

Simple API Integration

Clean REST and MCP endpoints. From zero to warm paths in minutes, not months.

Real-Time Paths

As relationships and signals change, the warmest path to any person updates with them.

Ranked by Confidence

Every path comes scored by source, recency, and confidence, so you act on the strongest route first.

MEERKAT OS FOR AI

Who can open the door, answered by Meerkat OS.

Give your AI the relationship context to act, not just answer. Ask for anyone, and Meerkat OS returns the reachable targets and the verified path to reach each one.

Claude
ChatGPT
Gemini
Perplexity
Your CRM
Custom Agents
Meerkat OS · Claude
Warmest paths to Northwind Health
Reachable targets
7
of 9 at Northwind
Verified paths
19
across your team
Strongest edge
Board
two shared seats
Avg confidence
89%
source and recency
Verified Paths by Edge Type
Career
8
Board
5
Household
3
Neighbor
3
Why These Paths Are Warm

Mary to Dana Reyes is your strongest path: two shared board seats over three years.

Lalith to Sam Okoye overlapped at the same employer for two years on the same team.

Bill to Priya Nair are verified neighbors, resolved from property records.

Reachable Targets: Warm Path and Verified Edge
CareerBoardNeighborHousehold
TargetWarm Path ViaEdgeConfidence
Dana ReyesChief Revenue OfficerMary SheaBoard
96%
Sam OkoyeVP EngineeringLalith DenduluriCareer
94%
Priya NairHead of ProcurementBill ConnollyNeighbor
91%
Tomás VidalChief Financial OfficerRalph SchonenbachCareer
90%
Grace LiuVP MarketingMary SheaBoard
88%
Owen BennettDirector of OperationsMark BradleyHousehold
84%
Hana SuzukiHead of PeopleBill ConnollyCareer
82%

Ready to unlock relationship intelligence?

Whether you want API access to the graph, a warm-path analysis for your team, or to build on the relationship layer, the conversation starts the same way.

Let's Connect

Tell us what you need: API access to the graph, a warm-path analysis, or to build on the relationship layer. We'll follow up.