I built a set of agents that run every day to find local businesses with no website, build a site for a selected business, and email the mockup to that lead. It's for anyone who sells web services to local businesses and wants to automate the prospecting. 1. Each day, my Gemini and ChatGPT agents check the major cities in the state I select, for the industry I select, and find the best businesses that don't have a website. They add those businesses to a spreadsheet. 2. A second agent reads the spreadsheet, selects the best prospect based on its need for web services and likelihood of buying, and starts building its website. 3. That agent checks the business's social media for images and its Google Business Profile for information. It also looks for a logo. If there isn't one, it creates a logo for them. 4. The agent finishes building the website, which becomes the mockup. 5. A third agent takes the mockup and sends an email with it to the lead. 6. A fourth agent is set up to reply once the lead responds, book them on a call, and put the call on the calendar. The result is a workflow that takes selected businesses from a prospect spreadsheet through a website mockup and follow-up, with replies leading to a booked call.

Late timesheets delayed accurate client billing for our advertising and marketing agency, so I built a Claude project that reconstructs my week from the tools I already use. It drafts time entries that I review and approve before anything reaches our system. It's useful for anyone who logs time after the fact and loses track of smaller tasks. 1. Before each run, Claude checks that my Chrome browsing history and Claude conversation export, which have no connector, are current. If one is out of date, it reminds me and asks whether to wait or continue without it. 2. It pulls the time I've already entered in the system so nothing gets posted twice. 3. It searches the emails I sent or replied to, my calendar meetings (using Gemini notes to confirm they happened and how long they ran), files I edited in Google Drive, Slack threads I joined, work-related browsing, and Claude work sessions. 4. It builds a timeline for each day and groups related items into one block. An email, a doc edit, and a Slack thread about the same issue count once. Personal activity is left out. 5. It estimates time for each block, rates each estimate as high, moderate, or low confidence, and assigns a project and task using my past entries as a guide. 6. It combines related time by day, project, and task before rounding up to the nearest quarter hour, so small items aren’t each rounded up separately, and writes a short description for each entry. 7. It flags unexplained gaps during my usual working hours and asks me about them. It never fills them in on its own. 8. It checks my calendar for PTO and applies our rules for days I worked anyway, so I post only the leave I actually took. 9. It produces a daily reconstruction, a ready-to-enter timesheet, and a weekly reconciliation, then saves a draft to the project. 10. I correct or confirm entries, and Claude updates the totals. Once I approve, it saves a final version. Entering and submitting are separate commands, and it runs each only when I say so. The project instructions require Claude never to invent work or pad hours, never to double-count, and to disclose any source it couldn’t search. Instead of trying to remember my week, I now review entries that already have project codes and descriptions, and it also surfaces work I had forgotten.

I run a photography business and built a three-layer AI workflow to help with daily operations, business improvement, and watching for outside changes that might affect us. It's for small business owners who don't want to monitor every source or handle every issue themselves. The layers are an Operations Core (dashboard, estimates, schedules, reminders, project records, attribution, and other business state), a Work orchestrator with specialist AI agents, and a signal and triage system that watches platform alerts, vendor updates, local-search changes, newsletters, calendars, and marketing sources. Here is how a signal moves through it: 1. The triage layer picks up a signal from one of the sources it watches. 2. Work decides whether the signal actually matters and routes it to the right specialist. 3. The specialist reviews it. I use separate specialists for WordPress, Google Business Profile and local search, marketing and STR signals, AI systems, research, and technical implementation. The WordPress and Google Business Profile specialists are also connected directly to those systems through MCP/API connectors, so they can compare the incoming signal against the current state of the actual site or profile before deciding whether it is relevant or whether any action is needed. 4. If the issue needs deeper evidence, the research specialist investigates before anything moves forward. 5. Work reconciles the findings into a recommendation. 6. I approve or decline the recommendation. The system brings me in only when a real decision is needed. 7. The change is implemented. 8. The Operations Core is updated with the new business state. The persistent Business Desk files hold current priorities, rules, operating guidance, and the source of truth for business state, so each step works from the same context. The main benefit for me is that I don't have to personally track every source or jump into every issue.

I hired an author coach to support me while I wrote my first book, and that relationship was wonderful. But it was an incredibly expensive solution. Coach Libra, at www.coachlibra.com, helps make that experience available to more authors. Modeled on the relationship I had with my author coach, Coach Libra helps authors write the book -- non-fiction, fiction, or memoir -- they've always had inside them. If you are writing your first book and want the guidance and support of a personal author coach, Coach Libra may be a fit. Here is how it works: 1. Coach Libra asks you questions about your book by email. 2. You reply by email to each question with a voice memo or a text. 3. The back and forth continues until your manuscript is complete. The result is a finished manuscript built from your own answers, one exchange at a time.

I'm a psychotherapist in New Zealand. My handouts were static and needed regular updating, so I wanted simple, slightly interactive websites that clients could engage with, without anything massive. I have no coding experience beyond programming a Commodore 64 in BASIC, so I asked Claude to build them. The sites needed to be static, and any data a client enters needed to stay private. 1. I gave Claude a simple design prompt for each tool, starting with the sites I needed. 2. First was an IFS (internal family systems) site, laid out as a house, where a client can map out their internal world and connect the key elements to understand the whole: https://ifs.padma.nz 3. Next was a second house offering a walk through basic tools for emotional regulation. It has no data entry, but includes downloadable information: https://kind.padma.nz 4. Then came a life narrative site using leaves on a tree. Events are logged on a branch with a common theme (for example, school), and photos and music relevant to a life event can be attached: https://leaves.padma.nz 5. Finally, because I use Jung's Active Imagination in my work as outlined by Robert Johnson, I built a site that gives instructions and maps out a pathway for this approach: https://clearing.padma.nz What I end up with is four simple, static sites that replace my out-of-date handouts, with the aim of keeping any client-entered data private.

As an agency, we have to report the work we do for our clients and how efficient it is. Building these presentations by hand is very time consuming and boring, and there is little value in making them. At Le Trefle à 5 Feuilles we decided to automate this recurring task. Before, it took about 3 hours to gather every stats report, make the presentation available to the client, and interpret the stats and recommend further actions. I keep a human in the loop, because someone has to read the report before it is sent. Since we work in Google Workspace, I built the workflow with several Gemini Gems: 1. Google Apps Script watches one folder on Google Drive and detects when a new statistics report is posted. 2. The first Gem decodes the report and inserts the variables into it. 3. The second Gem interprets and sums up all the statistics. 4. The third Gem merges those statistics into the template the workflow calculated. 5. I review the finished presentation before sending it to the client. It now takes only 3 minutes to produce the report, so the consultant can focus on the real value: their analysis of the campaign.

I run N Scheme alone, so I set up one Claude project as my company HQ. A coordinator chat takes my requests and hands each one to its own workstream thread: sales, social content, our free lead magnet, a competition entry, and even my own job and PhD search. Each thread is connected to the real tools. Apollo finds and scores prospects, Attio holds the pipeline and my daily tasks, n8n runs the automations, Webflow hosts the site, and Google Drive keeps the files. This is for solo founders who want the admin handled without giving up control. 1. Every week, the sales thread researches 15 to 20 new prospects and scores them against 12 trigger points. 2. For each prospect, it drafts a LinkedIn message and saves it as a dated task in Attio. 3. Every other Thursday, it drafts my social posts. 4. It keeps a shared memory of my rules and decisions, so nothing has to be re-explained. 5. When I ask, it updates my website. 6. For our free AI Opportunity Snapshot, an n8n workflow researches the company and writes a cited report. 7. I approve the report by email before it goes out. The rule I never break is: AI drafts, a human approves. Nothing is posted or sent without my say-so. The result: I spend my time on judgement and conversations, not admin.

At our localization agency, I run this workflow every month for each client. It looks at the edits our professional translators made to the initial AI translations, sorts those fixes by the kind of mistake they correct, and turns the results into suggested updates for the AI configuration. The goal is that future AI translations for that client make fewer of the same errors. 1. Each month, an AI agent for each client analyzes what the translators edited in the initial AI translations. 2. The agent categorizes the changes according to the mistake each edit fixed. 3. Based on those categories, the agent suggests updates to the AI configuration so future translations are better. What it saves: our language teams no longer spend hours categorizing every single change by hand. It is also much quicker, and it finds patterns that would otherwise be missed when people do this work manually.

A friend who had just started a pet-sitting and dog-walking business asked me to build a booking app. I used Google AI Studio to design the initial prototype. The process was straightforward, and I had a basic working system running within two hours. AI Studio created a Firebase database to store the details and was also effective at designing frontend changes. It published the app for me, and the resulting UI was intuitive. After demonstrating the app, I identified many additional features that needed to be added. This went beyond AI Studio’s capabilities, so I exported the code from AI Studio and started using Claude. I asked Claude to analyze the code and suggest the required changes. Claude identified critical security flaws in the database. I fixed them manually at first, but then realized that Claude could access the Firebase database and fix issues automatically. I continued prompting Claude with additional feature requests, and it built them. There were errors along the way, so I needed to be familiar with Google Chrome’s developer tools to copy the errors and ask Claude how to fix them. I used MailJS for email templates and Resend for email transport. I stored the app in GitHub and allowed Claude to access the repository so it could commit changes automatically. I ran the app locally with npm during development and then hosted it on Vercel. Eventually, I purchased an inexpensive domain name, and the app is currently hosted at Names. One major problem was that when Google AI Studio created the Firebase database, the permissions were locked, preventing me from making administrative changes. I solved this by recreating the database and asking Claude to write the SQL query to set it up. The permissions were still tricky, and I had to continue asking Claude to correct them. Google’s service permissions can be difficult to understand, and finding the correct settings was not always easy. Firebase was also complex to navigate, and getting the permissions configured correctly took time. Once I allowed Claude to connect to the app and its background services through the Google plugin, development became much faster. However, you need to trust the tool carefully and always work on a copy of the live app. Overall, I think app development with Google AI Studio and Claude is impressive. You can create professional apps quickly. I come from an IT support background, though, and I think people who are new to IT may find it difficult to troubleshoot errors without a basic understanding of networking and systems administration. I also built a litter-tracking app using ChatGPT, and it was equally effective. I eventually started using Codex and Claude Code, but I think standard Claude and ChatGPT are more intuitive for nontechnical users. Step-by-step: 1. I used Google AI Studio to create an initial booking-app prototype for a pet-sitting and dog-walking business. 2. I used the Firebase database created by AI Studio to store the app’s details and used AI Studio to design frontend changes. 3. I published the initial app with AI Studio and demonstrated it to identify additional features. 4. I exported the code from AI Studio and asked Claude to analyze it and suggest changes. 5. I addressed the critical database security flaws identified by Claude, first manually and later by allowing Claude to access the Firebase database. 6. I prompted Claude to build additional features and used Google Chrome’s developer tools to copy errors and ask Claude for fixes. 7. I recreated the Firebase database when AI Studio’s locked permissions prevented administrative changes, then asked Claude to write the SQL query to set it up. 8. I used MailJS for email templates and Resend for email transport. 9. I stored the app in GitHub and allowed Claude to commit changes automatically. 10. I ran the app locally with npm during development, hosted it on Vercel, and later purchased an inexpensive domain name that is currently hosted at Names. 11. I connected Claude to the app and background services through the Google plugin, while continuing to work on a copy of the live app.

I’m a freelancer on Fiverr and Upwork. I use ClickUp to manage enquiries, projects, and deadlines, while Slack is my team communication tool. When I receive a high volume of enquiries, I sometimes miss replies to older orders. That can also cause me to overlook important messages and hurt my responsiveness. To address this, I created a workflow in Make.com. Every four hours, I receive alerts about relevant messages. Urgent priorities are marked in Slack, and pending follow-ups that need attention are highlighted. This helps me quickly reply to all relevant messages. Step-by-step: 1. I manage all Fiverr and Upwork enquiries, projects, and deadlines in ClickUp. 2. I use Slack for team communication. 3. I connected the workflow in Make.com to send alerts every four hours. 4. I mark urgent priorities in Slack. 5. I highlight pending follow-ups that require attention. 6. I use the alerts and highlighted messages to reply to relevant enquiries and orders promptly.

I run a one-man home inspection business, and this workflow produces the first draft of my field report while I'm still on site. It's built around a voice-to-report pipeline, and I also use similar automation for the rest of the business. How the field report works: 1. On site, I open a simple web app on my own website from my iPhone and record one voice clip per area of the house (roof, electrical, plumbing, etc.), using AirPods as the mic. 2. The clips upload to my web server, and OpenAI's Whisper turns them into text. 3. Claude reads the transcript with my saved prompt, which tells it to write each finding in my report format, by section, in a professional inspector's voice. It writes the first draft of the report. 4. A Python script on my office PC drops the draft into that job's folder and emails it to me, usually before I'm back in the truck. The rest of the business runs the same way, with Claude-built Python scripts on Windows Task Scheduler. When a job is booked, a script creates the job folder, builds my inspection checklist, and pulls property research (parcel, aerial, sketch, permits). Photos tagged with a single tap on an iPhone Shortcut get sorted into the right job folders each night. Finished jobs feed one master Excel tracker that drives my invoices on a set schedule. A morning summary email and weekly and monthly financial reports round it out. I'm not a programmer. I built all of it by describing what I needed to Claude in plain English. Together we designed a system where Claude is the brain and hands off tasks to ChatGPT, Codex and Claude Code when they're the better fit. Result: I run 3 to 4 inspections a day on my own and get about 3x the work done.

I’m a commercial excellence consultant for industrial B2B businesses, and very few of the clients I speak with have a current view of the competition in their market. Nobody has mapped the competitive landscape recently—sometimes they never have. Clients can often name the companies in the market, but they can’t explain what those competitors do differently or why a customer would choose one over them. That gap is expensive. The mid-market manufacturers I work with, typically in the $75 million to $300 million range, usually don’t have a strategy team to close it. The alternatives are consulting rates for research that goes stale as soon as it’s delivered or a generic template that says the same five things about every market. Neither option is necessarily wrong, but neither is very useful. So I built a Claude Skill: an instruction set that runs live research every time instead of pattern-matching to a generic answer. The input is simple: the company name, its brand voice, the research scope, the geography, and a known competitor list if one is available. Before researching a single competitor, the Skill states the decision the report needs to inform—for example, whether to enter a vertical, how to price against a rival, or where to direct sales next quarter. It checks the client’s own website and capabilities next; an early version once recommended something the client already had. It then maps the competitive field across five tiers: direct, adjacent, disruptor, new entrant, and aspirational for PE-backed clients. It checks Asia-Pacific specifically because that’s the blind spot I’ve seen missed most often. Market size carries a confidence flag instead of false precision. Findings become battlecards phrased the way reps actually talk, rather than in analyst language. The report also includes a threat ranking with a timeline attached, then closes with three opportunities, three risks, and four to six moves for the quarter. Every recommendation is filtered through the original decision instead of being included simply because the research was interesting. The output is two files: an interactive HTML report and a matching PDF. Both are built entirely in HTML and CSS rather than with canvas charts, which can break in exactly the ways that matter—blank on load or missing from the PDF. None of what makes this useful is the AI itself. The important work is naming the decision before researching a competitor, checking what the client already has, tagging confidence instead of faking precision, and writing like a rep rather than an analyst. That’s the difference between a report that gets skimmed once and one that gets acted on. This is one piece of a bigger system I run for industrial manufacturers applying AI to their commercial function. I teach the underlying version of this workflow live. Step-by-step: 1. I provide Claude Skill with the company name, brand voice, research scope, geography, and known competitor list, if available. 2. I define the decision the report needs to inform, such as entering a vertical, pricing against a rival, or directing sales next quarter. 3. I have the Skill check the client’s website and capabilities before researching competitors. 4. I map the competitive field across direct, adjacent, disruptor, new entrant, and, for PE-backed clients, aspirational competitors. 5. I check Asia-Pacific specifically to address a commonly missed blind spot. 6. I assign confidence flags to market-size estimates rather than presenting false precision. 7. I turn the findings into sales-rep-friendly battlecards and add a threat ranking with a timeline. 8. I close the report with three opportunities, three risks, and four to six quarterly moves tied back to the original decision. 9. I deliver the result as an interactive HTML report and a matching PDF, using HTML and CSS instead of canvas charts.

As an AI consultant, I need to stay ahead of a landscape that evolves daily by reading dozens of industry articles each week. Manual tech watch quickly becomes a major time sink and pulls me away from client work. I built a specialized AI agent for challenge-focused summarization. Instead of producing generic, passive summaries, it answers two questions for every article: What specific problem, friction, or limitation does the article highlight? What practical solutions, tools, or actionable steps does it provide? The agent strips away marketing hype, introductory fluff, and generic definitions. It identifies the author’s core pain point and produces a quick-read summary focused on actionable technical or business solutions. This turns 15-minute reads into 30-second, high-value digests. By focusing on problem-solving rather than passive reading, the workflow helps me identify practical tools and frameworks I can apply directly to client projects and improve my consulting work. Step-by-step: 1. I provide the agent with a raw article link or the article text. 2. The agent filters out marketing hype, introductory fluff, and generic definitions. 3. It identifies the core pain point, friction, or limitation discussed by the author. 4. It extracts the concrete answers, including practical solutions, tools, and actionable steps. 5. It synthesizes the findings into a quick-read format focused on actionable technical or business solutions. 6. I use the digest to identify tools and frameworks that may apply directly to client projects.

I’ve automated 90% of my payroll work at an engineering firm. I use Claude Code to write Python scripts that pull timesheets from our ERP, Deltek Ajera. I check the data for oddities and, once everything looks right, push the timesheets into our HRIS, Paycom, through its API. I also have a script that parses and compares the preliminary payroll register with the timesheet data. Once I’m satisfied with the results, I can submit payroll. After payroll, my scripts create all the benefits upload files I used to create by hand. The spreadsheets I relied on for years are no longer necessary. Do I even need a spreadsheet anymore? Step-by-step: 1. I use Claude Code to write Python scripts for the payroll process. 2. I pull timesheets from Deltek Ajera, our ERP. 3. I check the timesheets for oddities. 4. Once the data looks right, I push the timesheets into Paycom, our HRIS, through its API. 5. I parse and compare the preliminary payroll register with the timesheet data. 6. Once I’m satisfied with the comparison, I submit payroll. 7. After payroll, I run scripts that create the benefits upload files I previously created by hand.

I built a workflow to reconstruct auditable, per-client billing for Claude usage after my Anthropic bill tripled in one month and I discovered there was no reliable way to see why. I run a solo consulting practice on a Max plan and pass AI costs through to clients. In September, my extra usage reached $1,805.76 in nineteen days. The usage page attributed 100 percent of one week to "Cowork" without session detail. Auto-reload was enabled by default with no spending limit, and the only usage I could independently verify—my local Claude Code logs—accounted for about $245. The support bot twice described analytics features that did not exist, including a per-day view it later retracted. When I finally reached a human, I got the truth in writing: Max subscribers have no session-level or per-day reporting, exports are Enterprise-only, and Cowork has been cloud-only on consumer plans since August. The Enterprise path to a readable meter starts at twenty seats. I am one person, so I built my own meter. The irony is that I used Claude to design and code it. Claude Code sessions write logs on my own machine. I price them at list rates with the open-source `ccusage` tool and attribute each session to a client by repo folder. That creates the measured pool: real receipts with no allocation. Other CLIs in the same logs, such as OpenAI Codex, are excluded automatically. Chat and Cowork run in Anthropic’s cloud and leave nothing on my disk, so the remaining overage becomes a cloud pool. I split that pool using evidence my workflow already produces: every Cowork document batch leaves a directive file, snapshots, and a report on disk. A Python script counts executed batches per project and measures the number of bytes each one rewrote. The script splits the cloud pool independently by batch count and byte volume, bills the midpoint, and shows both derivations so each client can see the bracket. Every column ties back to the dashboard total to the penny. A second script pulls per-account AWS charges from Cost Explorer because each client has a separate AWS account. Shell aliases make each report a one-command operation. A Claude skill and a monthly scheduled task turn the outputs into a draft invoice that I approve before anything is sent. The result is that a vendor hands me one unexplained number each month, while I hand my clients an evidence file. Month-end billing takes five minutes. It should not have been necessary to build this, but until consumer plans get real usage analytics, this is how I see in the dark. Step-by-step: 1. I collect local Claude Code session logs and use the open-source `ccusage` tool to price them at list rates. 2. I attribute each Claude Code session to a client by repo folder and automatically exclude other CLIs, such as OpenAI Codex, from the measured pool. 3. I treat the remaining overage from cloud-based Chat and Cowork usage as a separate cloud pool because those services leave no usage logs on my disk. 4. I use Cowork’s directive files, snapshots, and reports to identify document batches, then use a Python script to count executed batches per project and measure the bytes each batch rewrote. 5. I split the cloud pool independently by batch count and byte volume, bill the midpoint, and show both derivations as a client-facing bracket. 6. I verify that every column ties back to the dashboard total to the penny. 7. I use a second script to pull each client’s per-account AWS charges from Cost Explorer. 8. I run the reports through shell aliases, then use a Claude skill and a monthly scheduled task to create a draft invoice for my approval before sending it.

I built a band scraper to identify bands on Bandcamp, interact with them, and offer mixing and mastering services. A daily cron job runs an Apify Bandcamp scraper, stores the data, and supports a website hosted on Lightsail. Step-by-step: 1. I configured an Apify Bandcamp scraper to collect band data. 2. I scheduled the scraper to run daily with a cron job. 3. I stored the collected data. 4. I used the data to interact with bands and offer mixing and mastering services through a website hosted on Lightsail.

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.

Earlier in my career, I spent a significant amount of time delivering security risk assessments. These engagements typically took 8–12 weeks from the initial client meeting to the final report, with complex projects often stretching to 3–4 months. Today, I can produce reports of comparable quality in 1–2 days. The automation runs end-to-end in about 15 minutes; I spend the remaining time reviewing and validating the output. The workflow starts with a structured questionnaire and uses ChatGPT, Perplexity, Claude, Make.com, Google Drive, and Gamma.ai to research, draft, format, and deliver the report. This compresses the delivery timeline while preserving quality and lets me focus on expert judgment, validation, and client communication instead of manual report production. Step-by-step: 1. I have the client visit a web page and complete a structured questionnaire built on Lovable.dev. 2. The submitted information triggers an automated Make.com workflow. 3. ChatGPT analyzes the client’s responses. 4. Perplexity conducts targeted research tailored to the client’s industry, context, and risk profile. 5. Claude drafts the report and saves it to Google Drive. 6. The draft is passed to Gamma.ai, which generates the final client-ready report. 7. I receive the report by email, perform a thorough review, and send it to the client once I approve it. 8. I use the time saved to focus on expert judgment, output validation, and client communication.