AP/AR Intelligence for Serious Business
Patterns finds and captures the value hiding in your finances by integrating and reconciling data across all of your finance tools to provide you with real-time insights and automate manual work.
Real-time Financial Observability
Unified Financial Intelligence
Patterns transforms all your data into a unified data model, automatically integrating, cleaning, and categorizing it.
Sage Intacct | Netsuite |Quickbooks | Microsoft Dynamics 365 | Bill.com
Brex | JPMorgan | Mercury | Ramp
SPS Commerce, TrueCommerce
Shopify | Square | Stripe
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
Automated financial reports & management discussion
Let the AI do the busy work. Oversee their analyses, provide feedback, and get revised work back in seconds.
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