The carrier intelligence layer for AI agents
AlphaLoops profiles every motor carrier in America (2.7 million of them) and exposes the entire dataset through an MCP server, REST API, SDKs, and CLI. Plug Claude, ChatGPT, Cursor, or your own agent into AlphaLoops and answer carrier questions in seconds.
Carrier intelligence is the bottleneck for AI in trucking
AI agents are good at reasoning and bad at facts. They cannot reliably answer "which Texas reefer carriers are running Samsara and growing 20% YoY" by reading the web. The data is scattered across FMCSA filings, social media, broker forums, and proprietary technology adoption databases. Without a structured source, the agent guesses.
AlphaLoops is that source. The MCP server gives any AI agent direct, structured access to 2.7 million carriers across 200+ fields, with sub-100ms response times. Carrier intelligence becomes a tool call instead of a research project.
30+ tools, every facet of carrier intelligence
The full carrier dataset exposed as discrete tools an LLM can call. Six categories cover every workflow.
Resolve any DOT or MC. Authority history, BMC-91 filings, officer roster, address graph.
Filter the carrier graph by fleet size, geography, equipment, technology, growth, authority age.
Rank by similarity to a reference set or natural-language criteria. ICP fit, risk, displacement value.
CSA BASICs, inspection history, crash records, OOS rates, VIN-level equipment, full tech stack.
Chameleon detection, MC sale watchlist, equipment sale signals, federal litigation, AI-flagged signals.
Verified emails and phones for owners, ops VPs, IT directors, CFOs across 2.7M carriers.
Build with AlphaLoops in any environment
Same data, four ways in. Pick the surface that matches your workflow.
from alphaloops import Client client = Client(api_key="sk_...") carriers = client.carriers.search( state="TX", equipment="reefer", telematics="samsara", growth_yoy=0.20 ) # 23 carriers · 142ms
Frequently asked questions
What is the AlphaLoops MCP server?+−
A hosted Model Context Protocol server that exposes the full AlphaLoops carrier dataset (2.7M carriers, 200+ fields each) as 30+ tools an LLM can call. Connect Claude, ChatGPT, Cursor, or any MCP-compatible agent and query carrier data in natural language. Endpoint: mcp-freight.runalphaloops.com.
How do I connect Claude Desktop to AlphaLoops?+−
Add this to your Claude Desktop MCP config:
{
"mcpServers": {
"alphaloops": {
"url": "https://mcp-freight.runalphaloops.com/mcp",
"auth": { "apiKey": "sk_..." }
}
}
}
Restart Claude Desktop. The 30+ AlphaLoops tools become available in your conversation.
How does AlphaLoops compare to FMCSA's public API?+−
FMCSA exposes raw safety data via SAFER and rate-limited APIs. AlphaLoops covers the full FMCSA dataset plus proprietary layers FMCSA doesn't have: technology adoption (303 platforms), AI-flagged fraud signals, MC sale watchlist, decision-maker contacts, and the entity graph across the full carrier registry. The MCP exposes all of it.
Can I use AlphaLoops with ChatGPT, Cursor, or my own agent?+−
Yes. Any LLM client supporting MCP works out of the box. For environments without MCP, use the REST API directly via the Python SDK, TypeScript SDK, or any HTTP client.