Firefly Neuroscience, Inc.: cost of revenue

At a glance

Firefly Neuroscience reported cost of revenue of $11.4 million for the year ending 2021-12-31, down 2.4% from $11.7 million a year earlier. From 2010 to 2021 it grew at a compound 8.9% a year, from $4.47 million to $11.4 million. Across the last five reported years it ranged from $6.79 million (2018-12-31) to $11.7 million (2020-12-31). That was 75.9% of revenue ($15.0 million) for the same year.

Computed from the values in the table below, in USD; each links to the filing it came from.

What this measure means

Costs attributed to goods produced and sold and services provided during the period. This is not the sum of every operating expense.

Exact concept: us-gaap:CostOfRevenue. Each value covers an annual-duration reporting interval, shown with both start and end dates. Different units remain separate; no currency conversion or interpolation is applied.

Coverage of this history

Selected reporting periods run from 2010-01-01 to 2021-12-31. The SEC response was captured on 2026-09-19.

This selected history ends more than two years before capture. Do not treat its final value as a current balance or current annual result. More recent filings may use another accounting tag; inspect the filings before drawing conclusions about the company.

Selected filing history

Related FIREFLY NEUROSCIENCE, INC. histories

• Revenue
• Gross profit
• Operating expenses
• Selling, general and administrative expense
• General and administrative expense
• Research and development expense
• Total assets
• Total liabilities

All 59 Firefly Neuroscience histories

Inspect the source

Entity

FIREFLY NEUROSCIENCE, INC. / CIK 0000803578

Captured

2026-09-19 11:14 UTC

SEC response SHA-256

386f614f287e8c5b3e2613bdf47fdb87a9d81ae013faa05a6408c67881847335

Current SEC company facts · Download the original response snapshot (gzip) · Download the selected JSON

Latest-filed annual-report facts per unit and reporting period at capture time. Duration facts cover 300 to 400 days. This selection can include restatements and is not a point-in-time backtest dataset. Missing concepts are omitted, never zero-filled. Values retain original units and are not currency converted. Extended concepts require compatible unit shapes and at least three reporting ends with changing values within one unit. Constant or incompatible added histories are omitted.

Public company accounting reference, not market prices, returns, an investment recommendation, or ALPHAC performance. Validate a separately constructed return series with the validation API; accounting values are not returns.

Use this in research

A financial period ends before its results become public. Use the filing date as a minimum availability boundary, inspect amendments, and retain the original filing vintage when testing historical signals. This latest-filed selection can contain information unavailable at the time.

These pages do not supply prices, total-return histories, corporate-action adjustments or a tradable universe. Build those inputs separately before evaluating a strategy. A profitable backtest can still reflect selection bias or costs that were left out.

Research methodology · Execution and cost assumptions · Check backtest overfitting

Build with the open-source tools

Use these accounting records as inspectable inputs. When you have constructed a return series, the validation tools can help test its statistical evidence and preserve the result with its limitations.

• Get an API key and run your first validation
• Connect the MCP server to your coding assistant
• Inspect the ALPHAC engine on GitHub
• Read the MCP server source and integration examples

The MCP server is free and MIT-licensed: npx -y canli-mcp. If these records help your research, a star on GitHub helps other researchers find them.

import json
from urllib.request import urlopen

with urlopen("https://canlicapital.com/company-data/0000803578.json") as response:
    record = json.load(response)
print(record["fetched_at"])
print(record["policy"])
for concept in record["concepts"]:
    print(concept["tag"], next(iter(concept["observations"])))