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Transformative Financial Observability, Reporting, and Audit

Patterns streamlines financial operations and reporting, enabling companies to become audit-ready up to 10x faster by automating manual tasks and consolidating fragmented data.

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Real-time Financial Observability

Unified Financial Intelligence

Connect all your data,

get a single source of truth

Connect your data, get it cleaned automatically

Patterns transforms all your data into a unified data model, automatically integrating, cleaning, and categorizing it.

ERP, AP, AR

ERP, AP, AR

ERP, AP, AR

Sage Intacct | Netsuite |Quickbooks | Microsoft Dynamics 365 | Bill.com

Bank accounts

Bank accounts

Bank accounts

Brex | JPMorgan | Mercury | Ramp

EDI

EDI

EDI

SPS Commerce, TrueCommerce

Order / Payment systems

Order / Payment systems

Order / Payment systems

Shopify | Square | Stripe

Inventory management

Inventory management

Inventory management

Fulfil | TradeGecko | Fishbowl

Automated reconciliation and close done just by asking

Patterns handles collecting data, matching it up, and making reports.

Name AI Bot

Connect a data source

Set up data

Connect a data source

Select a data source and connect it to analyse data

Postgres

MySQL

Snowflake

BigQuery

Name AI Bot

Connect a data source

Set up data

Connect a data source

Select a data source and connect it to analyse data

Postgres

MySQL

Snowflake

BigQuery

Customer

Please reconcile our cash accounts for July.

AI Analyst

I have completed the reconciliation of our cash accounts for the month of July 2024. Below are the SQL queries and the results for the reconciliation process.

Query

Data

Chart

order_month

2011-05-01 00:00:00

2011-06-01 00:00:00

2011-07-01 00:00:00

2011-08-01 00:00:00

2011-09-01 00:00:00

2011-10-01 00:00:00

2011-11-01 00:00:00

2011-12-01 00:00:00

2012-01-01 00:00:00

2012-02-01 00:00:00

2012-03-01 00:00:00

2012-04-01 00:00:00

2012-05-01 00:00:00

2012-06-01 00:00:00

2012-07-01 00:00:00

2012-08-01 00:00:00

2012-09-01 00:00:00

2012-10-01 00:00:00

2012-11-01 00:00:00

2012-12-01 00:00:00

2013-01-01 00:00:00

2013-02-01 00:00:00

2013-03-01 00:00:00

2013-04-01 00:00:00

2013-05-01 00:00:00

2013-06-01 00:00:00

2013-07-01 00:00:00

2013-08-01 00:00:00

2013-09-01 00:00:00

2013-10-01 00:00:00

2013-11-01 00:00:00

2013-12-01 00:00:00

2014-01-01 00:00:00

2014-02-01 00:00:00

2014-03-01 00:00:00

2014-04-01 00:00:00

2014-05-01 00:00:00

2014-06-01 00:00:00

total_sales

567020.9498

507096.4690

2292182.8828

2800576.1723

554791.6082

5156269.5291

815313.0152

1462448.8986

4458337.4444

1649051.9001

3336347.4716

1871923.5039

3452924.4537

4610647.2153

3840231.4590

2442451.1831

3881724.1860

2858060.1970

2097153.1292

3176848.1687

2340061.5521

2600218.8667

3831605.9389

2840711.1734

3658084.9461

5726265.2635

5521840.8445

3733973.0032

5083505.3374

5374375.9418

3694667.9998

4560577.0958

4798027.8709

1478213.2920

8097036.3137

1985886.1496

6006183.2110

54151.4785

SELECT
 DATE_TRUNC('month', orderdate) AS order_month,
 SUM(totaldue) AS total_sales
FROM
 sales.salesorderheader
GROUP BY
 order_month
ORDER BY
 order_month

Sales trend over time

Yearly

2011

2012

2013

2014

8M

6M

4M

2M

0

Query

Data

Chart

order_month

2011-05-01 00:00:00

2011-06-01 00:00:00

2011-07-01 00:00:00

2011-08-01 00:00:00

2011-09-01 00:00:00

2011-10-01 00:00:00

2011-11-01 00:00:00

2011-12-01 00:00:00

2012-01-01 00:00:00

2012-02-01 00:00:00

2012-03-01 00:00:00

2012-04-01 00:00:00

2012-05-01 00:00:00

2012-06-01 00:00:00

2012-07-01 00:00:00

2012-08-01 00:00:00

2012-09-01 00:00:00

2012-10-01 00:00:00

2012-11-01 00:00:00

2012-12-01 00:00:00

2013-01-01 00:00:00

2013-02-01 00:00:00

2013-03-01 00:00:00

2013-04-01 00:00:00

2013-05-01 00:00:00

2013-06-01 00:00:00

2013-07-01 00:00:00

2013-08-01 00:00:00

2013-09-01 00:00:00

2013-10-01 00:00:00

2013-11-01 00:00:00

2013-12-01 00:00:00

2014-01-01 00:00:00

2014-02-01 00:00:00

2014-03-01 00:00:00

2014-04-01 00:00:00

2014-05-01 00:00:00

2014-06-01 00:00:00

total_sales

567020.9498

507096.4690

2292182.8828

2800576.1723

554791.6082

5156269.5291

815313.0152

1462448.8986

4458337.4444

1649051.9001

3336347.4716

1871923.5039

3452924.4537

4610647.2153

3840231.4590

2442451.1831

3881724.1860

2858060.1970

2097153.1292

3176848.1687

2340061.5521

2600218.8667

3831605.9389

2840711.1734

3658084.9461

5726265.2635

5521840.8445

3733973.0032

5083505.3374

5374375.9418

3694667.9998

4560577.0958

4798027.8709

1478213.2920

8097036.3137

1985886.1496

6006183.2110

54151.4785

SELECT
 DATE_TRUNC('month', orderdate) AS order_month,
 SUM(totaldue) AS total_sales
FROM
 sales.salesorderheader
GROUP BY
 order_month
ORDER BY
 order_month

Sales trend over time

Yearly

2011

2012

2013

2014

8M

6M

4M

2M

0

Query

Data

Chart

order_month

2011-05-01 00:00:00

2011-06-01 00:00:00

2011-07-01 00:00:00

2011-08-01 00:00:00

2011-09-01 00:00:00

2011-10-01 00:00:00

2011-11-01 00:00:00

2011-12-01 00:00:00

2012-01-01 00:00:00

2012-02-01 00:00:00

2012-03-01 00:00:00

2012-04-01 00:00:00

2012-05-01 00:00:00

2012-06-01 00:00:00

2012-07-01 00:00:00

2012-08-01 00:00:00

2012-09-01 00:00:00

2012-10-01 00:00:00

2012-11-01 00:00:00

2012-12-01 00:00:00

2013-01-01 00:00:00

2013-02-01 00:00:00

2013-03-01 00:00:00

2013-04-01 00:00:00

2013-05-01 00:00:00

2013-06-01 00:00:00

2013-07-01 00:00:00

2013-08-01 00:00:00

2013-09-01 00:00:00

2013-10-01 00:00:00

2013-11-01 00:00:00

2013-12-01 00:00:00

2014-01-01 00:00:00

2014-02-01 00:00:00

2014-03-01 00:00:00

2014-04-01 00:00:00

2014-05-01 00:00:00

2014-06-01 00:00:00

total_sales

567020.9498

507096.4690

2292182.8828

2800576.1723

554791.6082

5156269.5291

815313.0152

1462448.8986

4458337.4444

1649051.9001

3336347.4716

1871923.5039

3452924.4537

4610647.2153

3840231.4590

2442451.1831

3881724.1860

2858060.1970

2097153.1292

3176848.1687

2340061.5521

2600218.8667

3831605.9389

2840711.1734

3658084.9461

5726265.2635

5521840.8445

3733973.0032

5083505.3374

5374375.9418

3694667.9998

4560577.0958

4798027.8709

1478213.2920

8097036.3137

1985886.1496

6006183.2110

54151.4785

SELECT
 DATE_TRUNC('month', orderdate) AS order_month,
 SUM(totaldue) AS total_sales
FROM
 sales.salesorderheader
GROUP BY
 order_month
ORDER BY
 order_month

Sales trend over time

Yearly

2011

2012

2013

2014

8M

6M

4M

2M

0

Automated financial reports & management discussion

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Financial Intelligence for
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Financial Intelligence for
Serious Business

Linkedin

Privacy Policy

Terms of Service

Financial Intelligence for
Serious Business

Linkedin

Privacy Policy

Terms of Service