New statements in as little as a few hours after filing

Financial statements of Polish companies, ready for analysis

Over 3 million full statements from the Polish company register (KRS), mapped onto one schema: 94 line items from the balance sheet, income statement and cash flow. Plug them into Claude, your BI stack or your own code. One flat monthly fee, unlimited queries.

Book a 30-minute demo Download CSV sample

Available via MCP SQL REST API file export

AI agent kwerenda MCP connected

Find metal component manufacturers (NACE 25.62) with revenue of PLN 20–100M whose EBIT margin has grown three years in a row. No liens on assets.

3 kwerenda calls · 1.4 s

kwerenda.screen 312 companies

NACE 25.62, revenue PLN 20–100M 0.3 s
kwerenda.financials 19 companies

EBIT margin 2022–2024, rising trend 0.8 s
kwerenda.registry 11 companies

registered pledges, insolvencies 0.3 s

11 companies meet every condition. The three with the highest margin:

Company	Revenue	EBIT margin	3 years
Avio Precision Sp. z o.o.	32.8M	41.6%	+17.6 pp
Domgos 2 Sp. z o.o.	62.3M	27.6%	+24.2 pp
Major Sp. z o.o.	69.2M	19.1%	+11.4 pp

The figures come from statements filed with the register for 2024. Want me to check the ownership structure of these three?

3.1M

financial statements

94

financial line items

since 2018

of history

daily

updates

Why kwerenda

Financial data is sold today as if analysis were a cost, not the work itself

Three differences that decide how much you actually get analysed.

Typical data vendor

kwerenda

Billing model

Typical data vendor

You pay per query

A credit model means you calculate whether you can afford an analysis before you run it. Screening 500 companies becomes a budget decision, not an analytical one.

credits per query

kwerenda

You pay for access

A flat monthly fee. Screening five hundred companies costs the same as screening one. What is worth checking is the analyst's call, not the budget's.

flat fee

Data depth

Typical data vendor

You get the top of the balance sheet

Revenue, profit, total assets. Everything below that (provisions, liabilities split by maturity, costs by nature, cash flows) you read by hand out of a PDF.

~15 line items

kwerenda

You get the whole statement

We parse full documents, not selected fields: from the cash flow statement to the notes. Every line item ready to query, with no PDF reading.

94 line items

Integration

Typical data vendor

You cannot plug it into anything

A search box in a browser and an Excel export. Your AI assistant has no access to the data, so you end up copy-pasting anyway.

Excel export

kwerenda

You connect in five minutes

MCP for Claude and ChatGPT, SQL for your BI, REST API for your own code. One config entry and your agent queries the data itself.

MCP · SQL · API

Access

One database, any form of access

MCP for AI assistants, SQL for BI and notebooks, REST API for your own code. The same data set; switch whenever you like.

claude_desktop_config.json

{
  "mcpServers": {
    "kwerenda": {
      "command": "npx",
      "args": ["-y", "@kwerenda/mcp-server"],
      "env": {
        "KWERENDA_API_KEY": "pk_live_••••••••"
      }
    }
  }
}

AI agent kwerenda MCP connected

Find the bottom percentile of companies by pension reserves, among those with a reserve above zero, headcount above 4, and revenue above one million.

1 kwerenda call · 1.4 s

kwerenda.screen 14,198 companies

metric="pension_reserve", percentile="P10", filters… 1.4 s

The database matching these conditions holds 14,198 companies. The bottom-percentile threshold (P10) is PLN 7,502.55 in reserves. About 142 companies fall below it.

Company	KRS	Pension reserve	Headcount	Revenue
Bratex sp. z o.o.	0000112837	15.00	9.7	5,512,579
Kormatex sp. z o.o.	0000221934	31.00	20.0	2,756,497
Ligmet sp. z o.o.	0000347612	157.63	16.0	7,208,497
Norvex sp. z o.o.	0000398201	252.46	11.4	2,145,689
Polstal sp.k.	0000450128	439.70	128.0	117,271,739

screening.sql

SELECT
  c.name,
  c.pkd,
  f.pnl_revenue_net_sales,
  f.pnl_comp_amortization,
  ROUND(f.pnl_result_operating::numeric / NULLIF(f.pnl_revenue_net_sales,0)*100, 1) AS ebit_margin,
  f.liabilities_short_term_trade
FROM companies c
JOIN financials f ON c.krs = f.krs
WHERE c.pkd LIKE '62.01%'
  AND f.year = 2024
  AND f.pnl_revenue_net_sales BETWEEN 20000000 AND 100000000
ORDER BY ebit_margin DESC
LIMIT 20;

output

20 rows · 0.34s · read-only replica

name	pnl_revenue_net_sales	ebit_margin	liabilities_short_term_trade
Fintech Studio Sp. z o.o.	43,000,000	31.2%	8,100,000
CodeHouse S.A.	78,000,000	26.8%	15,210,000
DataLab Sp. z o.o.	31,000,000	22.4%	6,340,000

Full result set in your BI tool or notebook. Connect with any standard PostgreSQL driver.

terminal

curl https://api.kwerenda.ai/v1/companies \
  -H "Authorization: Bearer pk_live_••••••••" \
  -G --data-urlencode 'pkd=62.01' \
  --data-urlencode 'min_revenue=20000000' \
  --data-urlencode 'sort=ebit_margin:desc' \
  --data-urlencode 'limit=5'

output

{
  "total": 142,
  "results": [
    {
      "name": "Fintech Studio Sp. z o.o.",
      "krs": "0000514222",
      "period_end": "2024-12-31",
      "pnl_revenue_net_sales": "43000000.00",
      "ebit_margin": 31.2
    },
    {
      "name": "CodeHouse S.A.",
      "krs": "0000388111",
      "period_end": "2024-12-31",
      "pnl_revenue_net_sales": "78000000.00",
      "ebit_margin": 26.8
    },
    {
      "name": "DataLab Sp. z o.o.",
      "krs": "0000623001",
      "period_end": "2024-12-31",
      "pnl_revenue_net_sales": "31000000.00",
      "ebit_margin": 22.4
    }
  ]
}

Prefer a file or your own instance? We also deliver full database dumps as files and dedicated instances on your infrastructure.

Tell us what you work in

Data

94 line items from every statement, not fifteen

Competitors give you the top of the balance sheet. We parse the full documents and reduce them to one canonical model: the balance sheet, both variants of the income statement, the cash flow statement, bank and fund fields, and the narrative from the notes.

	Typical vendor	kwerenda
Financial line items	~15	94
Cash flow statement	rarely	13 fields
Liabilities by maturity	no	yes
Costs by nature	no	yes
Notes to the statements	no	parsed
Ownership structure	yes	yes
Connection graph	yes	yes
Access over MCP	no	yes

One schema, whatever the company happened to file

Statements reach the KRS registry in several dozen XML variants, in two income statement layouts, under separate schemas for banks and funds. What comes out on our side is a single record, identical for every company and year.

Year-over-year comparison: Every record carries the current and the previous year in an identical structure. You can compute trends up front from a single document, without stitching filings together.
Both income statement variants: We support both the comparative and the calculatory layout. Our records carry both the shared line items and the ones specific to each variant.
Banks, funds, consolidations: Bank, investment fund and consolidated statements get extra fields in the same record, instead of a separate schema and a separate code path on your side.
Not only numbers: Valuation methods, how the result was determined, the going-concern assumption and the merger accounting stay with the record.
Source documents: Every record links back to its specific source document, available through the API and the MCP server too. You can pull the source XML in a few seconds.

financial_report.json

{
  "report_type": "JednostkaInnaWZlotych",
  "schema_version": "1.2",
  "is_consolidated": false,
  "period_start": "2024-01-01",
  "period_end": "2024-12-31",
  "currency": "PLN",
  "pnl_variant": "POROWNAWCZY",
  "is_going_concern": true,
  "average_annual_employment": "143.50",

  "current_year": {
    "assets":                      "48231000.00",
    "assets_current_inventory":     "6120000.00",
    "equity":                      "21044000.00",
    "liabilities_short_term_trade": "8317000.00",
    "pnl_revenue_net_sales":       "87412000.00",
    "pnl_comp_amortization":        "3180000.00",
    "cash_flow_operating_net":      "9903000.00"
  },
  "previous_year": { "…": "same structure" }
}

Balance sheet: assets 24

Total assets

Non-current assets

Intangible assets

Goodwill

Property, plant and equipment

Land

Buildings and premises

Long-term receivables

Long-term investments

Investment property

Long-term financial assets

Long-term prepayments and accrued income

Current assets

Inventory

Materials

Semi-finished goods and work in progress

Finished goods

Merchandise

Short-term receivables

Short-term investments

Cash and cash equivalents

Short-term prepayments and accrued income

Called-up share capital not paid

Own shares

Balance sheet: equity and liabilities 21

Total equity and liabilities

Equity

Share capital

Supplementary capital

Revaluation reserve

Other reserve capital

Retained earnings from prior years

Net profit (loss)

Liabilities and provisions

Provisions for liabilities

Provision for pensions and similar benefits

Long-term liabilities

Long-term liabilities to related parties

Short-term liabilities

Short-term liabilities to related parties

Short-term loans and borrowings

Trade payables

Taxes, customs duties and social security

Payroll liabilities

Special funds

Accruals and deferred income

Income statement 9

Net revenue from sales

Gross profit on sales

Profit (loss) on sales

Profit (loss) on operating activities

Financial income

Financial costs

Gross profit (loss)

Income tax

Net profit (loss)

Income statement: comparative variant 7

Net revenue from sales and equivalent

Operating costs

Depreciation and amortisation

Materials and energy consumed

External services

Wages and salaries

Social security and other benefits

Income statement: calculatory variant 4

Revenue from products, goods and materials sold

Cost of products, goods and materials sold

Selling costs

General and administrative expenses

Cash flow statement 13

Operating inflows

Operating outflows

Net cash flow from operating activities

Investing inflows

Investing outflows

Purchase of intangible and tangible fixed assets

Net cash flow from investing activities

Financing inflows

Financing outflows

Net cash flow from financing activities

Total net cash flow

Balance sheet change in cash

Closing cash balance

Banks 7

Amounts due from the financial sector

Amounts due from the non-financial sector

Amounts due to the financial sector

Amounts due to the non-financial sector

Net interest result

Net commission result

Result on banking activity

Investment funds 4

Net assets

Income from investments

Realised and unrealised gain (loss)

Result on operations

Consolidated statements 4

Minority interest

Minority profit (loss)

Goodwill on consolidation

Negative goodwill on consolidation

Changes in equity 1

Opening equity balance

Plus metadata on every report 14

Report type (source schema)

XML schema version

Whether the report is consolidated

Period start and end

Currency

Income statement variant

Going-concern assumption

Description of threats to going concern

Whether the report follows a merger

Merger accounting method

Average annual employment in FTE

Discussion of valuation methods

How the financial result was determined

How the statement was prepared

Missing a field you need?

We parse full documents, not a selection of line items. If something is in the statement but not in the model, we add it. Usually within days.

Tell us what is missing

Coverage

Not only financial statements

A statement tells you what a company earned. The registers tell you who stands behind it. We take both from the same public sources and join them on the KRS number, so you query financials and owners together instead of stitching databases on company names.

Ownership structure

Shareholders and members recorded in the KRS, with the number and value of their shares. Where the holder is another company, we link it by KRS number to its own record, so an ownership chain can be walked upwards in a query rather than by hand.

Representatives

Management board, commercial proxies and supervisory board: who represents the company, in what role, and under what rules of representation. The same people can be found across every company they sit on.

Beneficial owners (UBO)

Filings from the Central Register of Beneficial Owners: the natural persons who actually control the company - including when they do so through a chain of other entities.

Same channels as the statements: MCP, SQL, REST API and file export.

Methodology

Where the data comes from and why you can trust it

Four steps from a document in the register to a record in your query.

Source

Financial statements from public sources: the Financial Document Repository (RDF), the KRS registry, CRBR.

Parsing

Statements reach the register as XML in dozens of schema variants: standard, small and micro entities, banks, funds, consolidated statements, each in several versions. Every variant has its own parser. If a document carries a field the model lacks, we add it.

Normalisation

Different statement layouts (comparative, simplified, full) are mapped onto one common schema. Line items from different years and different reporting formats are comparable, with no manual mapping.

Coverage and updates

The database covers hundreds of thousands of Polish companies and millions of statements. New statements usually reach the database within a few hours of publication; on a standard contract it can occasionally take longer than a day. For clients who need speed we bring that down to an hour. We check for updates on monitored companies every day.

Sample

Download a data sample

Download the CSV and see what our model looks like before you connect full access.

kwerenda_probka_1000_sprawozdan.csv

1,000 reports, 1.1 MB

Download the CSV sample

Before you open the file

Amounts in PLN; "in thousands" reports converted to PLN
Empty cell: the field is not in that report type's schema. 0.00 is a reported zero
Average fill rate 41%, depending on the reporting obligation
No contact data or accounting-policy descriptions (both in the full database)
One, most recent report per company; full history since 2018 in the database

kwerenda_probka_1000_sprawozdan.csv preview: 5 of 1,000 rows

name	main_pkd_code	period_end	pnl_revenue_net_sales_cy	equity_cy
TERG SPÓŁKA AKCYJNA	4754Z	2025-03-31	20281953846.00	2026869599.28
ARCELORMITTAL POLAND S.A.	2410Z	2025-12-31	19117435000	4739461000
W.EG Polska Sp. zo.o.	4690Z	2025-12-31	1229551798.23	8682522.49
Gdańskie Wodociągi Sp. z o.o.	3600Z	2025-12-31	391307663.11	38369278.13
CELMA INDUKTA SPÓŁKA AKCYJNA	2711Z	2025-12-31	293574000	140274000

215 columns: registry and address data, statement metadata, and financial line items for the current (_cy) and previous (_py) year.

Use cases

What our clients use it for

Investment screening

Filter thousands of companies on any combination of financial metrics and trends, not on the three fields you happen to have.

Due diligence

A target's full financial history, ownership structure and personal connections in a single query.

Sector analysis and benchmarking

Compare a target against the full population of companies in a NACE code, not against the sample you can afford.

Portfolio monitoring

Automatic detection of new statements and changes at the companies you track.

“It works, it is current, and we do not have to count queries. That is all we need.”

Pionier Broker Sp. z o.o.

Pricing

You pay for access, not per query

A flat monthly fee, agreed individually. No credits, no counting, no overage charges.

Monthly subscription

Custom quote

We set the fee after the demo, once we know which data you need and in what form.

Book a demo Ask for a quote

Included in every subscription

MCP, SQL and REST API
unlimited queries
94 financial line items per statement
history back to 2018
new fields on request
source data (XML) for every record

On request

dedicated instance
SLA and updates within the hour
onboarding support
integration with your infrastructure
full database dumps as files

Unlimited queries under fair use.

FAQ

Frequently asked questions

The questions we hear most often before a contract is signed.

Ask your own question

Where does the data come from and may I use it commercially?

The data comes from public sources: the Financial Document Repository, the KRS registry, CRBR. A commercial licence is part of the engagement: you may use the data in client reports and in your own products.

How quickly does a statement reach the database after publication?

Usually within a few hours of publication; on a standard contract it can occasionally take longer than a day. If speed matters to you, the contract can bring that down to an hour. We scan the sources daily.

What if a company has not filed a statement?

Every statement carries its filing date and reporting-period dates, so a missing year is visible at once. We never interpolate or synthesise data: you see exactly what the company filed, and what it did not.

How is GDPR handled for board member data?

Data on people holding positions in company bodies comes from the public KRS registry. Processing it for statistical purposes is fully permitted. Sensitive data such as PESEL numbers is cryptographically anonymised.

May I resell the data inside my own product?

We can tailor the contract to each client. Get in touch and we'll work through the details of the arrangement together.

What if I need a field you do not have?

We parse full documents, not a selection of line items. If a field exists in the statement but not in the database, we add it, usually within days.

What does MCP setup involve and how long does it take?

Setting up MCP means entering the right details into the configuration of Claude, ChatGPT, or another AI tool. From receiving your access credentials to your first query takes about 5 minutes. No code, no infrastructure, no ETL.