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A chocolate factory client had a simple but expensive sales problem: good conversations with buyers sometimes went quiet because nobody followed up at the right time. Some opportunities were waiting on sample feedback, pricing, private-label details, minimum order quantities (MOQs), packaging, or distributor discussions. The opportunities were still active, but they were easy to lose track of. I built a follow-up workflow for the client in Awish. Step-by-step: 1. I opened the Awish chat and wrote: “Track our open sales opportunities from Microsoft Teams and Google Sheets. Every business day, review the latest customer conversation, sales stage, planned follow-up date, customer interest, and how long we’ve been out of touch. Find opportunities that need follow-up around samples, quotations, private label, MOQ, packaging, or distribution. Explain why each customer should be contacted, recommend the next action, and prepare a personalized Teams follow-up message based on the real conversation history. Never send anything without the salesperson’s approval. When the customer replies, close the old follow-up action and update the Sales Master record.” 2. Awish understood the request, planned the workflow, and selected Microsoft Teams and Google Sheets for the process. 3. I connected the client’s accounts, reviewed the plan, and approved the automation. 4. When a new customer conversation takes place in Teams, Awish updates the related sales record. 5. Every business day, it reviews open opportunities and finds deals that are overdue, at risk, or ready for the next follow-up. 6. For each opportunity, it explains why follow-up is needed and what the salesperson should do next. 7. Awish prepares a personalized Teams message using the actual conversation history, but waits for the salesperson’s approval before sending anything. 8. When the customer responds, the previous follow-up is closed and the opportunity status in Google Sheets is updated automatically. Now the sales team gets a prioritized daily list of who needs attention, why they need attention, and what the next message should be—without manually reviewing every old conversation. I want to keep building more workflows like this for real businesses. If you have a complex, repetitive process in your company that you think should be automated but you’re not sure how to build it, send it to me. I’d be happy to see if I can turn it into a working automation with Awish.

Yesterday, I built a social media workflow in Awish for a business that wanted to post more consistently without managing three platforms manually. The idea was simple: let Awish understand the business first, then use that context to plan different content for Instagram, LinkedIn, and X instead of reposting the same thing everywhere. I built the entire workflow by describing what I wanted in the Awish chat. Step-by-step: 1. I opened Awish and wrote: “Analyze this business from its website. Based on what the company does, its audience, products, and positioning, create separate content plans for Instagram, LinkedIn, and X. Generate the posts, publish them automatically at the times I choose, analyze how each post performs, and send me the important insights on WhatsApp. I also want to be able to manage the workflow from WhatsApp.” 2. Awish analyzed the website, understood the business, and planned the complete content workflow. 3. I connected the company’s Instagram, LinkedIn, X, and WhatsApp accounts and approved the plan. 4. Awish created a different content strategy for each platform instead of copying the same post everywhere: more visual content for Instagram, business-focused posts for LinkedIn, and shorter, conversation-driven posts for X. 5. I chose the publishing schedule, and Awish started creating and publishing the posts automatically at those times. 6. After publishing, Awish tracked performance and analyzed which topics, formats, and messages were getting the strongest response on each platform. 7. Awish sent me short WhatsApp updates with the important insights, such as which post performed best, what topic was gaining traction, and what it recommended changing next. 8. I could also manage the automation directly from WhatsApp by asking what was scheduled, changing a publishing time, requesting another post, pausing publishing, or asking for the latest performance summary without opening Awish. The useful part for me is that this is not just automatic posting. The workflow starts by understanding the business, treats each social platform differently, learns from what gets published, and keeps the whole process manageable from WhatsApp.

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

Today, I wanted to see how far an AI agent could go if I gave it a real business instead of a predefined automation. I entered binayah.com into Awish.ai. Binayah is a real estate company, and instead of telling Awish.ai exactly what workflow to build, I asked it to analyze the business first and find an automation opportunity. It suggested a customer acquisition funnel, which I reviewed and approved. Around 10 minutes later, the automation was ready. Step-by-step: 1. I entered binayah.com into Awish.ai. 2. Awish.ai agents analyzed the website and how the business operates. 3. Awish.ai identified customer acquisition as an area that could be automated. 4. It suggested a funnel designed to capture and qualify potential leads. 5. I reviewed the suggestion and approved it. 6. Awish.ai created the workflow and connected the required steps. 7. The funnel was ready to use in around 10 minutes. What I find most interesting is that I didn’t start by designing a workflow. The system first understood the business, found an opportunity, suggested what should be automated, and built it only after I approved. That feels much closer to having an automation consultant inside the product than using a traditional workflow builder.

I’ve been using the Awish app I built less as an automation builder and more as a way to solve specific business problems people bring me. One hotel client, Aurelia Bay Hotel, had an interesting marketing problem. They could see travel trends across search, Instagram, YouTube, website traffic, and ads, but connecting those signals early enough to spot a real campaign or guest-experience opportunity was difficult. I built an automation for them with Awish and gave it to them to test. Step-by-step: 1. I opened the Awish chat and wrote: “For Aurelia Bay Hotel, monitor Google Trends, YouTube, Instagram, Google Analytics, and Google Ads every day. Find rising interests around Dubai travel, staycations, family trips, wellness, dining, events, and hotel experiences. Cross-check those trends with social engagement, website behavior, and campaign performance. Rank the strongest opportunities, send them to the marketing team in Microsoft Teams for approval, and create a campaign or guest-experience opportunity brief in SharePoint for anything they approve.” 2. Awish understood what I wanted, planned the complete workflow, and selected the apps required for each step. 3. I reviewed the plan, connected the client’s accounts, and approved it. 4. Awish added Microsoft Teams as the human approval step and SharePoint for the final opportunity briefs. 5. Once everything was connected, Awish built the workflow and started running it daily. 6. It now cross-checks trend growth, social engagement, website behavior, and ad performance. When it finds a strong opportunity, the marketing team gets the evidence in Teams. If they approve it, Awish creates the full campaign or guest-experience brief in SharePoint. The useful part is that a trend isn’t treated as an opportunity just because it is growing on one platform. Awish checks whether the same signal is appearing across the business before recommending action. Do you have any time-consuming, repetitive tasks or problematic aspects in your job? I would be happy to develop an automation solution for you as well.

I built an Awish workflow that handles a support ticket before anyone on the team opens it. I realized support tickets were not just taking time to answer. Someone still had to understand the problem, decide how urgent it was, search the documentation, route it to the right person, and prepare a response. I wanted that first layer of support work to happen automatically. I opened Awish and wrote: “Whenever a new support ticket comes in, understand the issue, determine its urgency, check our documentation, prepare a response, create a Jira issue if it looks like a product bug, and escalate anything important to the team before taking customer-facing action.” Awish understood the request, planned the workflow, and showed me which applications it needed. Step-by-step: 1. I described the complete support process I wanted in the Awish chat. 2. Awish planned the workflow and selected Zendesk, Notion, Jira, and Slack for the required steps. 3. I connected my accounts and approved the automation plan. 4. When a new Zendesk ticket arrives, the workflow identifies the issue type, urgency, and customer intent. 5. It checks the relevant Notion documentation and prepares a response based on the available information. 6. If the issue looks like a product bug, it creates a Jira ticket with the customer context already attached. 7. Urgent or sensitive cases are escalated to the team instead of being handled automatically. 8. I connected WhatsApp from the Awish chat. I now receive workflow updates there and can manage the automation without opening Awish. Customer-facing actions still stay under my control when approval is needed.

I conducted a workflow experiment with Awish, the product I built, and the results surprised me. I think this approach could be useful for anyone who wants to automate more of their business but is not sure where to start. I gave Awish my website, and it identified the automations my business was missing. The difficult part was not always building a workflow; it was figuring out which tasks were worth automating first. I opened Awish and asked: “Analyze my business from awish.ai, suggest automations that could improve how I work, and only build them after I approve.” Awish’s agents analyzed the website to understand what the business does, how customers interact with it, and where repetitive work could be automated. Instead of asking me to design a workflow from scratch, Awish suggested several automations based on the business. One suggestion was a lead workflow that finds relevant prospects and automatically prepares and sends personalized outreach emails. Another was a website inquiry workflow that analyzes new customer requests, identifies what they need, and sends me the important details as a WhatsApp notification. It also suggested a content workflow that creates and publishes posts for Instagram, X, and LinkedIn based on the business and its content strategy. I chose the lead generation and automated email workflow. Step-by-step: 1. I opened the Awish chat and asked it to analyze my business from awish.ai, suggest automations that could improve it, and only build something after I approved it. 2. Awish’s agents analyzed the website and identified repetitive sales, customer communication, and content tasks that could be automated. 3. Awish suggested a lead generation and email outreach workflow, a website inquiry analysis and WhatsApp notification workflow, and a social content workflow for Instagram, X, and LinkedIn. 4. I selected the lead generation and automated email workflow. 5. Awish’s agents understood the request and created the workflow plan, including how leads should be found, what information should be collected, and how personalized outreach should be prepared. 6. Awish selected the applications needed for the workflow and showed me the connections before anything was built. 7. I approved the plan and connected my personal accounts to the required applications through the sign-in flow instead of manually setting up each integration. 8. Awish built the automation within minutes, so new leads could be identified, researched, and contacted through the workflow without me having to design each step manually. What I like about this approach is that I do not need to know the exact automation I want before I start. I can begin with the business itself, let the agents identify useful opportunities, choose the one that makes sense to me, and then approve the build.

I built a predictive maintenance workflow in Awish for one of my manufacturing clients. The client had machine telemetry, production data, and maintenance history spread across different systems. The problem wasn’t collecting the data—it was spotting failure risk early enough to act. I built a custom Awish workflow that continuously checks machine telemetry and production signals in Snowflake alongside asset, meter, and maintenance history from MaintainX. When it detects abnormal performance or increasing failure risk, it identifies the affected equipment, estimates the likely operational impact, and prepares a recommended maintenance action. Nothing is scheduled automatically at that point. The recommendation first goes to the maintenance manager in Microsoft Teams for approval. Once approved, Awish creates and assigns the work order in MaintainX, then keeps tracking and updating its status until the maintenance is completed. The useful part is that the system does not wait for a machine to fail before maintenance starts, but it also does not let AI make the maintenance decision on its own. The analysis is automated, while the actual intervention still requires human approval. Step-by-step: 1. I described the maintenance process I wanted in the Awish chat. 2. Awish planned the workflow and selected Snowflake, MaintainX, and Microsoft Teams for the required steps. 3. I connected the client’s accounts and approved the automation plan. 4. Awish continuously analyzed production and telemetry data in Snowflake together with MaintainX asset, meter, and maintenance history. 5. When it detected abnormal behavior or increasing failure risk, it identified the affected equipment and estimated the likely operational impact. 6. It prepared a recommended maintenance action and sent it to the maintenance manager in Microsoft Teams. 7. Once the manager approved the recommendation, Awish created and assigned the work order in MaintainX. 8. The workflow continued tracking the work order and updating its status until the maintenance was completed. Trigger → Analyze → Approval → Action Machine signals → Failure-risk analysis → Teams approval → MaintainX work order

I created a website lead workflow that researches each company, scores the opportunity, updates my CRM, drafts a personalized reply, and notifies me on WhatsApp. I had previously built similar workflows with Claude Code, but connecting external applications was often the hardest part. I had to manage OAuth setup, credentials, APIs, and different integration requirements for every app. I also found it difficult to monitor and control completed automations from WhatsApp without building additional infrastructure. To solve this, I created Awish, a tool focused on AI-powered automation. Instead of manually designing every workflow, I describe the result I want in chat. Awish’s agents understand the request, plan the automation in the background, determine which applications and steps are required, and prepare the complete workflow for me. When a new lead submits my website form, the workflow researches the company, evaluates the lead against my criteria, assigns a score, and adds the contact and research results to my CRM. It then drafts a personalized response based on the lead’s company and needs before sending me a WhatsApp notification for review. The application connections are handled through a simple sign-in flow. I connect my personal accounts with a click instead of manually working with OAuth code, API keys, or custom authentication logic. Once the workflow is active, I can monitor it, receive updates, and manage its actions directly from WhatsApp without opening the Awish application. Step-by-step: 1. I open the Awish chat and describe the workflow I want: when someone submits my website form, research the company, score the lead, add it to my CRM, draft a personalized response, and notify me on WhatsApp. 2. Awish’s agents analyze the request and create the application and automation plan on my behalf, including the trigger, research steps, lead-scoring logic, CRM fields, response draft, and notification. 3. I review the proposed plan and sign in to the required applications with my personal accounts to connect them to the workflow. 4. Awish builds the automation so each new website submission triggers company research and collects the information needed to evaluate the opportunity. 5. The workflow scores the lead, creates or updates the CRM record, and stores the contact details, company research, and lead score in the appropriate fields. 6. It drafts a personalized response using the information submitted by the lead and the additional company research. 7. I connect WhatsApp from the Awish chat, and the workflow sends me the lead details, score, CRM update, and response draft there. 8. I review and manage the automation directly from WhatsApp without needing to open Awish or build a separate messaging integration. The result is a lead-management workflow I can create through a conversation, connect to my existing applications without manually implementing OAuth, and control from WhatsApp while the agents handle the planning and execution in the background.

After a sales call, the conversation is only part of the work. Someone still needs to write the notes, update the CRM, remember the next step, prepare the follow-up email, and make sure the deal does not disappear between meetings. I wanted that entire post-call process to happen automatically. I created an Awish workflow that starts when a sales call ends. It analyzes the conversation, extracts the important sales information, updates the CRM, creates the next actions, drafts a personalized follow-up email, and sends the team a short summary. The workflow looks for the information I would normally write down myself: what the prospect needs, the objections they raised, their timeline, agreed next steps, and anything that could affect the deal. Customer-facing actions stay under my control. The workflow prepares the follow-up, but I can review important messages before they are sent. Step-by-step: 1. I tell Awish what I want to happen after every sales call: analyze the conversation, update the CRM, prepare the follow-up, and notify the team. 2. Awish understands the request, plans the workflow, and determines which applications are needed. 3. I connect my meeting platform, HubSpot, Gmail, and Slack to the automation. 4. When a call ends, the workflow analyzes the transcript and extracts the prospect’s needs, objections, timeline, decisions, and agreed next steps. 5. It creates or updates the contact and deal in HubSpot and adds any follow-up tasks that came out of the conversation. 6. It drafts a personalized Gmail follow-up based on what was actually discussed instead of using a generic sales template. 7. It sends a short Slack summary to the team with the deal status, important points, and next action. Instead of a sales call ending as a transcript or a page of notes, it immediately becomes structured CRM data, clear next actions, and a follow-up that is ready to review. The useful shift for me is that the workflow is not replacing the sales conversation. It is handling everything that normally gets forgotten or delayed after the call.

I wanted to see what Awish would automate first if I gave it a real client business instead of a predefined workflow. I used Martolia Marble, one of my previous clients, and gave Awish the website. I asked it to analyze the business, suggest automations that could support growth, and build them only after I approved them. Awish analyzed the website and proposed several opportunities. I chose two: a lead generation and email outreach workflow, and an Instagram workflow that tracks relevant trends, plans content, creates posts, and publishes them. Step-by-step: 1. I gave Awish the Martolia Marble website and asked it to analyze the business. 2. Awish identified areas where automation could support growth and suggested workflows. 3. I approved a lead generation workflow that finds relevant prospects and prepares personalized email outreach. 4. Awish selected the required apps, I connected the accounts, and the agents built the workflow. 5. I also approved an Instagram workflow that analyzes relevant trends, plans upcoming content, creates the posts, and publishes them. 6. Once the accounts were connected, both automations were ready to run without me manually building each step. What I liked most was starting with the business rather than with an automation idea. I gave Awish the website, reviewed its suggestions, and approved only the workflows that made sense for the client.