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The NorthAxium Data lobbying API exposes the full LobbyCan registry — registrations (who is registered to lobby and on what subjects) and communications (actual meeting records between lobbyists and federal officials). This guide covers the key query patterns for investigative and business intelligence use cases.

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Prerequisites

  • A NorthAxium Data API key (get one here or use the free demo key)
  • Python 3.8+ with the requests library installed (pip install requests)

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Find who is lobbying on a topic

Search communications by subject matter to find all organizations actively lobbying on a specific issue. 
import requests

API_KEY = "your_api_key_here"
BASE_URL = "https://api.northaxiumdata.ca/api/v1"
headers = {"X-API-Key": API_KEY}

# find lobbying communications on mining and natural resources
params = {
    "subject": "Mining",    # partial match on subject label
    "date_from": "2023-01-01",
    "limit": 20,
}

response = requests.get(
    f"{BASE_URL}/lobbying/communications",
    headers=headers,
    params=params
)
data = response.json()

print(f"Total communications on this subject: {data['meta']['total']}")
print()

for comm in data["data"]:
    print(f"Client:      {comm['client_org']}")
    print(f"DPOH:        {comm['dpoh_name']} ({comm['institution']})")
    print(f"Date:        {comm['communication_date']}")
    print(f"Subjects:    {', '.join(comm.get('subject_labels', []))}")
    print("---")

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Get a full client lobbying profile

Pull an aggregated view of a client’s lobbying activity — total communications, top institutions, and top subjects. 
# get the lobbying profile for a client organization
client_name = "Rio Tinto"

response = requests.get(
    f"{BASE_URL}/clients/{client_name}",
    headers=headers
)
profile = response.json()["data"]

print(f"Client: {profile['client_org']}")
print(f"Total communications: {profile['total_communications']}")
print(f"Active registrations: {profile['active_registrations']}")

print("\nTop institutions lobbied:")
for inst in profile["top_institutions"][:5]:
    print(f"  {inst['institution']}: {inst['communication_count']} communications")

print("\nTop subjects:")
for subj in profile["top_subjects"][:5]:
    print(f"  {subj['subject']}: {subj['count']}")

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Track the lobby → contract sequence

One of the most powerful patterns: check whether an organization lobbied a department before winning a contract from it. 
import requests
from datetime import datetime

API_KEY = "your_api_key_here"
BASE_URL = "https://api.northaxiumdata.ca/api/v1"
headers = {"X-API-Key": API_KEY}

company = "Palantir"

# Step 1: get lobbying communications with dates
lobby_response = requests.get(
    f"{BASE_URL}/lobbying/communications",
    headers=headers,
    params={"client": company, "limit": 50}
)
comms = lobby_response.json()["data"]

# Step 2: get contract wins
contract_response = requests.get(
    f"{BASE_URL}/contracts",
    headers=headers,
    params={"vendor": company, "limit": 50}
)
contracts = contract_response.json()["data"]

print(f"Lobbying communications: {len(comms)}")
print(f"Contract records found:  {len(contracts)}")

# Step 3: look for lobby-before-contract sequences by department
lobbied_depts = set()
for comm in comms:
    if comm.get("institution"):
        lobbied_depts.add(comm["institution"].lower())

print("\nContracts from departments that were previously lobbied:")
for contract in contracts:
    dept = (contract.get("department") or "").lower()
    # check for partial overlap between contract department and lobbied institution
    for lobbied in lobbied_depts:
        if any(word in lobbied for word in dept.split("|")[0].strip().split()):
            print(f"  {contract['vendor']} — ${contract['value']:,.0f} from {contract['department']} on {contract['event_date']}")
            break

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Search active registrations by institution

Find all organizations currently registered to lobby a specific federal institution. 
params = {
    "institution": "Department of Finance",
    "active": "true",
    "limit": 20,
}

response = requests.get(
    f"{BASE_URL}/lobbying/registrations",
    headers=headers,
    params=params
)
data = response.json()

print(f"Active registrations targeting this institution: {data['meta']['total']}")
for reg in data["data"]:
    print(f"  {reg['client_org_name']} — {reg['subject_labels']} (since {reg['effective_date']})")

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What’s happening here

subject parameter on communications — partial match on LobbyCan subject matter labels such as Mining, Environment, International Trade, Taxation, Health. Check the LobbyCan registry for the full label taxonomy. client parameter — partial match on client organization name. "Rio Tinto" matches Rio Tinto plc, Rio Tinto Canada Inc, and similar variants. active parameter on registrations — true returns only registrations with a current end_date; false returns only expired ones. Omit to get both. Lobby → contract pattern — LobbyCan communication dates and CanadaBuys contract dates are both ISO 8601. Sorting by date and comparing department names surfaces potential lobby-to-contract sequences. For the most accurate matching, use the department profile endpoint to align institution names with department names precisely.

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Next steps

Lobbying Communications Reference

Full parameter reference for the communications endpoint

Client & Lobbyist Profiles

Aggregated lobbying profiles for clients and individuals

Search Federal Contracts

Cross-reference lobbying with contract wins

MCP Server

Ask Claude to do this analysis in natural language