I recently built a month-end close workflow in Awish for a client at a finance company. The problem was not creating the final report. The real bottleneck was collecting data from different systems, checking what was missing, reconciling totals, chasing exceptions, and getting the report to the right people. I built the entire process in Awish by describing what I wanted. Step-by-step: 1. I opened the Awish chat and wrote: “At every month-end close, collect journal entries, invoices, vendor bills, and financial records from NetSuite together with reporting workbooks from Excel and SharePoint. Check submission completeness, reconcile totals across sources, identify missing data or unusual variances, prepare the management-reporting workbook, send unresolved exceptions to Finance in Microsoft Teams for approval, and once approved export the final report to PDF, store it in SharePoint, and distribute it through Outlook.” 2. Awish understood the request, planned the workflow, and selected NetSuite, Excel, SharePoint, Microsoft Teams, and Outlook for the required steps. 3. I reviewed the plan, connected the client’s accounts, and approved the automation. 4. At month-end, Awish pulls the required financial data and reporting files, checks whether anything is missing, reconciles totals, and flags unusual variances. 5. It updates the management-reporting workbook and sends only the unresolved exceptions to the Finance team in Microsoft Teams. 6. Once Finance approves the exceptions, Awish finalizes the report, exports it to PDF, stores it in SharePoint, and sends it to the authorized recipients through Outlook. The useful part is that Finance no longer has to spend most of the close manually collecting and checking information before making a decision. The repetitive reconciliation work is handled automatically, while the team retains control over unexplained exceptions and the final report. Trigger → Analyze → Approval → Action Month-end close → Reconciliation \u0026 variance checks → Finance approval → Final report \u0026 distribution

I used to open calls by asking people to remind me where we left off. The notes existed, but they were scattered across transcripts that nobody reviewed. I built two scheduled tasks that work together: one files every meeting at the end of the day, and the other sends me a prep brief every morning. At the end of each day, the first task pulls the verbatim transcript of every meeting I had into a shared Notion database. I use the transcript rather than the AI summary because summaries may be useful that afternoon but are less useful three weeks later when I need the exact thing somebody said. Each meeting becomes a page with the date, client, and attendees stored as real relations rather than text, so everything is filterable later. My business partner has access to the same database, so neither of us has to recap our calls for the other. At 7 a.m., the second task reads my Outlook calendar, finds the email thread or threads tied to each meeting, reads the associated transcripts in Notion, and writes a short prep brief for each one: what we said last time, what I owe them, and what is still open. The part that took the most thought was deciding what should not be filed in the main database. Personal meetings are skipped by keyword. Small internal meetings are screened for topics such as pay, hiring, legal matters, or client-confidential material. Those meetings are routed to a separate database with different permissions. When a meeting is ambiguous, it defaults to the restricted database. Failing toward privacy is the right default when a robot is making the decision. Step-by-step: 1. I turned on Zoom AI Companion so every meeting produces a transcript, then connected Zoom, Notion, and Outlook. 2. I built a Notion database for meeting notes with Date, Client, and Attendees as relation properties connected to existing Clients and People databases. These relations make the notes findable later. 3. I wrote the end-of-day task to pull each transcript verbatim, create a page, match the client by keyword against my client list, and add attendees based on the transcript speakers. 4. I filtered the speaker list because notetaker bots appear as attendees. I removed Fireflies, Otter, Fathom, and the other notetaker bots, and automatically created a person page for anyone who was genuinely new. 5. I deduplicated meetings using the title and date. If a page already existed as a placeholder, I updated it in place instead of creating a second one. 6. I added a skip list for personal meetings and a confidentiality screen that routes sensitive internal meetings to a separate, permission-restricted database. When the classification is unclear, it defaults to restricted. 7. I wrote the morning task to read that day’s calendar, search email and transcripts for each attendee and company, and produce one short brief per meeting covering the last contact, open commitments, and what I owe them. 8. I scheduled both tasks: the filing task for the end of the day and the briefing task for early morning.