Introducing Patterns
Your AI-powered financial analyst, harnessing the power of advanced language models.
Your AI-powered financial analyst, harnessing the power of advanced language models.
Monthly revenue
1
10
11
12
2
3
4
5
6
7
8
9
Month
0
2,000,000
4,000,000
6,000,000
8,000,000
10,000,000
12,000,000
14,000,000
16,000,000
Revenue
2011
2012
2013
2014
Year
SELECT
EXTRACT(YEAR FROM orderdate) AS year,
EXTRACT(MONTH FROM orderdate) AS month,
SUM(totaldue) AS revenue
FROM
sales.salesorderheader
GROUP BY
year,
month
ORDER BY
year,
month
year
month
revenue
2011
5
567020.9498
2011
6
507096.4690
2011
7
2292182.8828
2011
8
2800576.1723
2011
9
554791.6082
2011
10
5156269.5291
2011
11
815313.0152
2011
12
1462448.8986
2012
1
4458337.4444
2012
2
1649051.9001
2012
3
3336347.4716
2012
4
1871923.5039
2012
5
3452924.4537
2012
6
4610647.2153
2012
7
3840231.4590
2012
8
2442451.1831
2012
9
3881724.1860
2012
10
2858060.1970
2012
11
2097153.1292
2012
12
3176848.1687
2013
1
2340061.5521
2013
2
2600218.8667
2013
3
3831605.9389
2013
4
2840711.1734
2013
5
3658084.9461
2013
6
5726265.2635
2013
7
5521840.8445
2013
8
3733973.0032
2013
9
5083505.3374
2013
10
5374375.9418
2013
11
3694667.9998
2013
12
4560577.0958
2014
1
4798027.8709
2014
2
1478213.2920
2014
3
8097036.3137
2014
4
1985886.1496
2014
5
6006183.2110
2014
6
54151.4785
Monthly revenue
1
10
11
12
2
3
4
5
6
7
8
9
Month
0
2,000,000
4,000,000
6,000,000
8,000,000
10,000,000
12,000,000
14,000,000
16,000,000
Revenue
2011
2012
2013
2014
Year
SELECT
EXTRACT(YEAR FROM orderdate) AS year,
EXTRACT(MONTH FROM orderdate) AS month,
SUM(totaldue) AS revenue
FROM
sales.salesorderheader
GROUP BY
year,
month
ORDER BY
year,
month
year
month
revenue
2011
5
567020.9498
2011
6
507096.4690
2011
7
2292182.8828
2011
8
2800576.1723
2011
9
554791.6082
2011
10
5156269.5291
2011
11
815313.0152
2011
12
1462448.8986
2012
1
4458337.4444
2012
2
1649051.9001
2012
3
3336347.4716
2012
4
1871923.5039
2012
5
3452924.4537
2012
6
4610647.2153
2012
7
3840231.4590
2012
8
2442451.1831
2012
9
3881724.1860
2012
10
2858060.1970
2012
11
2097153.1292
2012
12
3176848.1687
2013
1
2340061.5521
2013
2
2600218.8667
2013
3
3831605.9389
2013
4
2840711.1734
2013
5
3658084.9461
2013
6
5726265.2635
2013
7
5521840.8445
2013
8
3733973.0032
2013
9
5083505.3374
2013
10
5374375.9418
2013
11
3694667.9998
2013
12
4560577.0958
2014
1
4798027.8709
2014
2
1478213.2920
2014
3
8097036.3137
2014
4
1985886.1496
2014
5
6006183.2110
2014
6
54151.4785
Monthly revenue
1
10
11
12
2
3
4
5
6
7
8
9
Month
0
2,000,000
4,000,000
6,000,000
8,000,000
10,000,000
12,000,000
14,000,000
16,000,000
Revenue
2011
2012
2013
2014
Year
SELECT
EXTRACT(YEAR FROM orderdate) AS year,
EXTRACT(MONTH FROM orderdate) AS month,
SUM(totaldue) AS revenue
FROM
sales.salesorderheader
GROUP BY
year,
month
ORDER BY
year,
month
year
month
revenue
2011
5
567020.9498
2011
6
507096.4690
2011
7
2292182.8828
2011
8
2800576.1723
2011
9
554791.6082
2011
10
5156269.5291
2011
11
815313.0152
2011
12
1462448.8986
2012
1
4458337.4444
2012
2
1649051.9001
2012
3
3336347.4716
2012
4
1871923.5039
2012
5
3452924.4537
2012
6
4610647.2153
2012
7
3840231.4590
2012
8
2442451.1831
2012
9
3881724.1860
2012
10
2858060.1970
2012
11
2097153.1292
2012
12
3176848.1687
2013
1
2340061.5521
2013
2
2600218.8667
2013
3
3831605.9389
2013
4
2840711.1734
2013
5
3658084.9461
2013
6
5726265.2635
2013
7
5521840.8445
2013
8
3733973.0032
2013
9
5083505.3374
2013
10
5374375.9418
2013
11
3694667.9998
2013
12
4560577.0958
2014
1
4798027.8709
2014
2
1478213.2920
2014
3
8097036.3137
2014
4
1985886.1496
2014
5
6006183.2110
2014
6
54151.4785
What is Patterns?
Your AI financial analyst,
With Patterns as your AI-powered financial analyst, there's no need to navigate complicated tools. Add Patterns to email, copy emailing, or assign a task in Jira and get instant access to the financial insights you need. No more report hunting, just effortless financial data access tailored to you.
With Patterns as your AI-powered financial analyst, there's no need to navigate complicated tools. Add Patterns to email, copy emailing, or assign a task in Jira and get instant access to the financial insights you need. No more report hunting, just effortless financial data access tailored to you.
Key feautures
POWERED BY YOUR FINANCIAL DATA
Patterns is trained using verified queries and citations from real-world financial scenarios.
POWERED BY YOUR FINANCIAL DATA
Patterns is trained using verified queries and citations from real-world financial scenarios.
POWERED BY YOUR FINANCIAL DATA
Patterns is trained using verified queries and citations from real-world financial scenarios.
EXPERT SQL KNOWLEDGE
Patterns can accurately retrieve and manipulate data from financial databases, saving time and effort for users who might not be proficient in SQL themselves.
EXPERT SQL KNOWLEDGE
Patterns can accurately retrieve and manipulate data from financial databases, saving time and effort for users who might not be proficient in SQL themselves.
EXPERT SQL KNOWLEDGE
Patterns can accurately retrieve and manipulate data from financial databases, saving time and effort for users who might not be proficient in SQL themselves.
AUTOMATED FP&A TASKS
Patterns can automate repetitive FP&A tasks, such as data collection, reconciliation, and report generation, freeing up your team to focus on strategic initiatives.
AUTOMATED FP&A TASKS
Patterns can automate repetitive FP&A tasks, such as data collection, reconciliation, and report generation, freeing up your team to focus on strategic initiatives.
AUTOMATED FP&A TASKS
Patterns can automate repetitive FP&A tasks, such as data collection, reconciliation, and report generation, freeing up your team to focus on strategic initiatives.
financial reports and metrics
Capable of producing detailed reports, interpreting data trends, and exploring critical business questions to support strategic decision-making processes.
financial reports and metrics
Capable of producing detailed reports, interpreting data trends, and exploring critical business questions to support strategic decision-making processes.
financial reports and metrics
Capable of producing detailed reports, interpreting data trends, and exploring critical business questions to support strategic decision-making processes.
auditable analyses
Patterns ensures transparency and accountability by providing a clear audit trail for all financial analyses. With every query, visualization, and insight fully documented, Patterns simplifies compliance, regulatory reporting, and investor relations.
auditable analyses
Patterns ensures transparency and accountability by providing a clear audit trail for all financial analyses. With every query, visualization, and insight fully documented, Patterns simplifies compliance, regulatory reporting, and investor relations.
auditable analyses
Patterns ensures transparency and accountability by providing a clear audit trail for all financial analyses. With every query, visualization, and insight fully documented, Patterns simplifies compliance, regulatory reporting, and investor relations.
ADVANCED FINANCIAL ANALYTICS
Leverage Patterns for advanced analytics, such as demand forecasting, churn reduction, and financial modeling, empowering your finance team with powerful technical tools.
ADVANCED FINANCIAL ANALYTICS
Leverage Patterns for advanced analytics, such as demand forecasting, churn reduction, and financial modeling, empowering your finance team with powerful technical tools.
ADVANCED FINANCIAL ANALYTICS
Leverage Patterns for advanced analytics, such as demand forecasting, churn reduction, and financial modeling, empowering your finance team with powerful technical tools.
How it works?
How it works?
How it works?
01
01
Connect your database to integrate Patterns with your financial data stack, including systems like NetSuite, Salesforce, Stripe, and Brex.
Connect your database to integrate Patterns with your financial data stack, including systems like NetSuite, Salesforce, Stripe, and Brex.
02
02
Let Patterns automate tedious financial analytics tasks, such as data collection, reconciliation, and report generation.
Let Patterns automate tedious financial analytics tasks, such as data collection, reconciliation, and report generation.
Query
Data
Chart
#
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14
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38
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
Query
Data
Chart
#
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22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
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
03
03
Gain instant insights and recommendations for optimizing financial performance, complete with contextual summaries and actionable next steps.
Gain instant insights and recommendations for optimizing financial performance, complete with contextual summaries and actionable next steps.
04
04
Stay ahead with future developments, including advanced financial modeling, risk assessment, and proactive portfolio monitoring.
Stay ahead with future developments, including advanced financial modeling, risk assessment, and proactive portfolio monitoring.
Customizing Patterns
Customizing Patterns for Your Finance Team's Needs
When you link your database and choose data sources, Patterns learns about your business by automatically analyzing your data and query history. You can further tailor its performance by adding your own context, for example, to create specialized reports for specific teams within large enterprises, such as FP&A, accounting, or treasury.
When you link your database and choose data sources, Patterns learns about your business by automatically analyzing your data and query history. You can further tailor its performance by adding your own context, for example, to create specialized reports for specific teams within large enterprises, such as FP&A, accounting, or treasury.