Give it the deepest, most trusted fundamentals out there — so it reasons like a real analyst, not a chatbot guessing at numbers.
The fundamentals desks pay six figures for — at a price a solo builder can run.
Structured so your agent actually understands it — not raw filings or flat JSON it has to guess at.
A direct source link on each data point, so every call your agent makes can be audited.
Full statements plus the footnote detail others drop — the numbers the way the company actually reported them.
We crunch the ratios and show every formula — verify it, or swap in your own.
International regulator-source filings·XBRL-native·MCP-ready·Provenance on every fact
User Interface
Left is a flat one-year summary. Right is AkkruData's interactive filing view with source-linked facts.
| 9/30/2025 | |
|---|---|
| Total Revenue | 416,161,000 |
| Cost of Revenue | 220,960,000 |
| Gross Profit | 195,201,000 |
| Operating Expense | 62,151,000 |
| Operating Income | 133,050,000 |
| Other Income Expense | -321,000 |
| Pretax Income | 132,729,000 |
| Tax Provision | 20,719,000 |
| Net Income | 112,010,000 |
| Basic EPS | 7.49 |
| Diluted EPS | 7.46 |
For AI Agent
Typical APIs return flattened totals. AkkruData returns filing-structured data with dimensional context.
{"ticker": "AAPL","fiscal_year": 2025,"period": "annual","currency": "USD","revenue": 416161000000,"cost_of_goods_sold": 220960000000,"gross_profit": 195201000000,"research_and_development": 34550000000,"selling_general_admin": 27601000000,"operating_expenses": 62151000000,"operating_income": 133050000000,"other_income_expense": -321000000,"income_tax": 20719000000,"net_income": 112010000000,"eps_basic": 7.49,"eps_diluted": 7.46}
That's everything. No product breakdown. No geographic segments. No cost-of-sales by region. No link to source. AkkruData gives you complete, auditable, source-linked data—every company in its own filing structure, yet cross-comparable across any ticker. Every fact carries rich metadata—concept definitions, dimensional context, period, table placement—so AI agents actually understand what each number means, not just what it equals.
{"ticker": "AAPL","fiscal_year": 2025,"period": "annual","currency": "USD",// ── Revenue ──"aapl:ProductMember": 307003000000,"aapl:IPhoneMember": {"label": "iPhone","calculated_value": 209586000000,"raw_value": "209,586","concept": "us-gaap:RevenueFromContract...ExcludingAssessedTax","unit": "USD", "decimals": -6, "item_type": "monetaryItemType","period": "2024-09-29 / 2025-09-27","dimensions": { "srt:ProductOrServiceAxis": "aapl:IPhoneMember" },"table": "CONSOLIDATED STATEMENTS OF OPERATIONS","line_item": "Products","fact_id": "f-377","sec_url": "https://www.sec.gov/ixviewer-plus/ix.xhtml?doc=/Archives/edgar/data/0000320193/000032019325000079/aapl-20250927.htm#fact-identifier-98"},// ↑ every fact carries the same depth of metadata"aapl:MacMember": 33708000000,"aapl:IPadMember": 28023000000,"aapl:WearablesHomeAndAccessoriesMember": 35686000000,"aapl:ServiceMember": 109158000000,"revenue": 416161000000,// srt:StatementGeographicalAxis"aapl:AmericasSegmentMember": 178353000000,"aapl:EuropeSegmentMember": 111032000000,"aapl:GreaterChinaSegmentMember": 64377000000,"aapl:JapanSegmentMember": 28703000000,"aapl:RestOfAsiaPacificSegmentMember": 33696000000,"aapl:CorporateMember": null,// ── Cost of Sales ──"cost_of_goods_sold": 220960000000,// srt:StatementGeographicalAxis"aapl:AmericasSegmentMember": 95699000000,"aapl:EuropeSegmentMember": 58617000000,"aapl:GreaterChinaSegmentMember": 35141000000,"aapl:JapanSegmentMember": 13779000000,"aapl:RestOfAsiaPacificSegmentMember": 17724000000,"aapl:CorporateMember": null,// ── Gross Profit ──"gross_profit": 195201000000,// ── Operating Expenses ──"research_and_development": 34550000000,"aapl:CorporateMember": 34550000000,"selling_and_marketing": 19524000000,// srt:StatementGeographicalAxis"aapl:AmericasSegmentMember": 10174000000,"aapl:EuropeSegmentMember": 4676000000,"aapl:GreaterChinaSegmentMember": 2319000000,"aapl:JapanSegmentMember": 969000000,"aapl:RestOfAsiaPacificSegmentMember": 1386000000,"aapl:CorporateMember": null,"general_and_administrative": 8077000000,"aapl:CorporateMember": 8077000000,// ── Operating Income ──"operating_income": 133050000000,// srt:StatementGeographicalAxis"aapl:AmericasSegmentMember": 72480000000,"aapl:EuropeSegmentMember": 47739000000,"aapl:GreaterChinaSegmentMember": 26917000000,"aapl:JapanSegmentMember": 13955000000,"aapl:RestOfAsiaPacificSegmentMember": 14586000000,"aapl:CorporateMember": -42627000000,"other_income_expense": -321000000,"income_tax": 20719000000,"net_income": 112010000000,"eps_basic": 7.49,"eps_diluted": 7.46}
Every value traces back to its exact location in the SEC filing.
Explore
What every financial data API should give your AI agent — but only Akkru does. Every number and every sentence traces back to the exact line in the original SEC filing.
US-listed universe · Russell 3000+ · 5-year history
"Find all US-listed companies where ROE stayed above 20% AND Debt-to-Equity stayed below 1.0 — for each of the last 5 fiscal years. Rank by latest ROE."
Try it yourself — paste into your AI agent and compare its answer to Akkru's below.
Gemini 3.1 Pro · extended thinking
"Scanning the entirety of the 4,000+ US-listed equities historically requires a dedicated quantitative terminal. Here are the premier large-cap companies that pass:"
| Ticker | ROE | D/E |
|---|---|---|
| V | 53.9% | 0.66 |
| GOOGL | 38.9% | 0.16 |
| MSFT | 36.3% | 0.30 |
Akkru · POST /screener · 1 API call
Matches · ranked by 5-year ROE
20+
Who We Serve
Whether you're shipping AI agents, building the next fintech platform, or doing the careful research that demands real audit trails — AkkruData gives you the same foundation: audit-ready · source-linked · AI-native · global-by-design.
Get an API key, pull real filing-backed facts, and ship agents that cite sources—not guesses.
