Welcome to the Calera Labs Enterprise Integration portal. The Volumetric Lattice Network (VLN) provides a mathematically rigorous, zero-hallucination query interface tailored for capital markets compliance, auditing, and SEC filing validation.
Unlike probabilistic Large Language Models (LLMs), the VLN executes query decomposition deterministically, referencing Hebbian reinforcement patterns to resolve queries directly against XBRL taxonomies. If a query cannot be verified to 100% confidence, the network returns an honest failure/silence rather than inventing or guessing an answer.
To maintain the highest level of security, all client interactions are authenticated using post-quantum cryptography (PQC) hybrid schemes.
All machine-to-machine integrations require plan activation and an Ed25519-signed license key.
Different plan tiers grant access to distinct endpoints and query limits. Enterprise and High-Volume features require a Startup, Starter, Professional, or Enterprise subscription.
Direct machine-to-machine integrations bypass browser sessions and authenticate using Ed25519-signed license keys.
calera_lk_).X_LICENSE_KEY).Allows executing a single natural language question against historical SEC filings. Response times typically range from 150ms to 400ms.
Authenticate your request by sending your credentials in the X-License-Key header.
X-License-Key: <YOUR_LICENSE_KEY> Content-Type: application/json
{
"query": "What is Apple's FY2023 revenue?"
}
{
"query_id": "q_9a87d6f5e4",
"status": "VERIFIED_SUCCESS",
"source": "Apple Inc. 10-K (FY2023)",
"answer": {
"value": 383285000000,
"unit": "USD",
"formatted": "$383.29B"
},
"confidence_score": 1.0,
"provenance": {
"entity_id": "CIK0000320193",
"source_filing": "0000320193-23-000106",
"company": "Apple Inc.",
"xbrl_concept": "us-gaap:RevenueFromContractWithCustomerExcludingAssessedTax",
"doc_period": "FY2023",
"audit_hash": "a8f7c9e54d3c2b1a..."
}
}
For high-volume clients, we support asynchronous queue-based batch execution. Clients submit a list of questions, receive a batch job ID, and poll the status endpoint until completion.
Submit up to 100 queries in a single request. The server returns a status code 202 (Accepted) and registers your job.
{
"queries": [
"What is Apple's FY2023 revenue?",
"What is Microsoft's FY2023 net income?"
]
}
{
"batch_id": "batch_abc123xyz789",
"status": "PENDING",
"total_queries": 2,
"processed_queries": 0,
"created_at": "2026-07-09T22:30:00Z"
}
Poll this endpoint to retrieve the current state and answers for a batch job.
Once completed, download the full array of verified results by appending /results to the path:
Enterprise subscribers can configure webhook endpoints to receive immediate event payload push notifications when a batch job finishes processing.
When a batch job moves to a terminal state (COMPLETED or FAILED), Calera server executes a POST request containing details to your registered endpoint:
{
"event": "batch.completed",
"batch_id": "batch_abc123xyz789",
"total_queries": 2,
"status": "COMPLETED",
"completed_at": "2026-07-09T22:32:15Z"
}
We provide multiple assistance channels depending on your active plan tier:
support@caleralabs.com.Below are official code snippets showing how to implement asynchronous batch submission and result polling in multiple languages.
import time
import requests
API_KEY = "your_license_key_here"
BASE_URL = "https://financesec.api.caleralabs.com"
headers = {
"X-License-Key": API_KEY,
"Content-Type": "application/json"
}
# Submit batch
payload = {
"queries": [
"What is Tesla's FY22 gross margin?",
"What is Amazon's FY23 operating margin?"
]
}
print("Submitting batch job...")
res = requests.post(f"{BASE_URL}/api/batch", json=payload, headers=headers)
res.raise_for_status()
job = res.json()
batch_id = job["batch_id"]
print(f"Job queued successfully. Batch ID: {batch_id}")
# Poll status
while True:
res = requests.get(f"{BASE_URL}/api/batch/{batch_id}", headers=headers)
res.raise_for_status()
job = res.json()
status = job["status"]
processed = job["processed_queries"]
total = job["total_queries"]
print(f"Status: {status} ({processed}/{total} processed)")
if status == "COMPLETED":
print("\n=== Results ===")
for result in job["results"]:
print(f"Q: {result['query_id']} | Answer: {result['answer']['formatted'] if 'answer' in result else 'N/A'}")
break
elif status == "FAILED":
print("Batch job failed.")
break
time.sleep(3)