How are people cleaning messy scraped business CSVs before outreach?

I keep running into exports from Google Maps / Outscraper / Apify where half the rows need cleanup before they can go into Instantly, Smartlead, or a CRM.

Stuff like bad URLs, duplicate domains/phones, directory links, missing emails, weird source URLs, and rows that should clearly be skipped.

I made a small cleanup tool for my own workflow and I’m trying it on real messy files now.

If anyone has an old/anonymized export with 50–100 rows, I’ll clean it and send back the output. Mostly looking to see where it breaks.

Marketing & Sales OS
The concept:

Instead of one general AI assistant that does everything, I built 84 specialised agents,each with a single job. Blog Writer only writes blogs. Cold Call Script Writer only writes cold call scripts. The theory is that specialisation produces better outputs because the context, instructions, and examples are all scoped to one task.

What I'm actually unsure about and want feedback on:

1. Is specialisation even the right approach? Or are people better off just writing good prompts into a general model? I have a strong opinion here but want to be challenged on it.
2. 84 agents — is that a feature or a UX problem? I added search and category filters but I genuinely don't know if the number feels powerful or just overwhelming when you first land on it.
3. Brand Voice as a core feature — users upload their guidelines once and every agent writes in their tone automatically. Is this actually solving a real pain point or is it something people think they want but don't use?
4. Human-in-the-Loop by design — I deliberately didn't build auto-publish. Every output needs manual approval before it goes anywhere. Was this the right call or am I just adding friction?

What I've heard so far from early users:

• The specialisation clicks immediately for some people and confuses others
• People love Brand Voice in theory but haven't tested it enough yet
• The pricing model (run-based rather than seat-based) is getting mixed reactions

Would genuinely appreciate anyone who's built something similar or worked in this space poking holes in the approach. What am I missing? What would make you not use this?
And any suggestions and recommendations and improvements.

I sell on both Amazon and my own Shopify store .I run Meta ads that sometimes drive Shopify traffic and sometimes people just end up buying on Amazon after seeing the same ad .Amazon gives some data .Meta gives me another slice .They never add up to anything coherent .My total monthly ad spend is around $5,000 and I have no honest sense of which channel deserves more budget?

TL;DR - These are the lessons I learned:
* We showed people this big, great SaaS - they told us all they wanted was a micro SaaS subset of it
* LinkedIn hates automation, but they're fine with content consumption

A while ago, the startup I was working at tasked me with building a process automation / AI agents framework. They tried n8n, Zapier and the usual candidates but didn't like them.

They liked the framework I built and thought about turning it into a product. So they started cold calling people to validate demand. The problem with cold calling: Most of the people you call aren't looking for a solution at the exact time you're calling them.

But one theme kept coming up: "Can we use this to automate find and contact leads?"

So, we built agents scraping LinkedIn for conversations where people were looking for what they had to offer. And it worked.

As it turns out, LinkedIn hates automation (automated posting, DMs etc.) and they take various measures against scraping (e.g. limiting profile search result count), but they're totally fine with content consumption. You won't get banned for scrolling the feed all day long - and neither will your AI agent.

So I built agents that do just that - and finds "warm" leads in the process.

I demoed it to a few more people and demo call gave me an idea. When I tried to explain to the other person how it worked: "Our agents are like a swarm of puffins scanning the ocean for fish - only the fish are your next customers."

And I thought, "wouldn't puffins make a fun landing page?". So I built prospectpuffin. Not because I set out to build a lead scraper. But because people kept asking for it. And because I like puffins. Let me know what you think.

The more AI gets added to marketing workflows, the more obvious one thing becomes:

i've had my msp for 3 years now and honestly feeling stuck on lead generation for it companies. we've tried cold calling local businesses, got maybe 2 clients from that. linkedin outreach gets some responses but conversion is brutal.

right now we're doing a mix of referrals (which is great but unpredictable), some local networking events, and testing out cold email. for the email campaigns we're using a few data tools - tried Apollo for a bit but the local business data was kinda meh for our area. now testing Prospeo to build lists of local companies that might need IT support. getting decent open rates but still figuring out the messaging.

what's working for you all? i keep hearing about intent data for finding companies shopping for new msp vendors but haven't pulled the trigger on any of those platforms yet. are you focusing more on verticals like healthcare/legal or just going after any SMB? would really love to hear what's moving the needle for other MSPs right now.

AI can draft, summarize, segment, and trigger faster than any human team. But if lifecycle stage, attribution, offer mapping, and customer context are messy, automation just moves bad assumptions faster.

Are teams spending enough time on the data/process layer before adding AI workflows?

I work in brand strategy, and competitor/social reporting was eating my time manually pulling
what brands' competitors were doing across Instagram, Facebook, TikTok and the web, then
writing it up into something presentable.
So I built Signal.

You point it at a brand's competitors, it pulls their recent activity, analyses it,
and exports a presentation-ready report. The half-day job, done in minutes.

Stack if anyone's curious: React front end, Google's Gemini with live search grounding doing the
analysis, Google Sheets as a lightweight data layer.
Coverage: South Africa is deepest; US & UK are live but the brand database is still limited and
growing. If your category's in there you'll get a full report in minutes if not, tell me and I'll add
it.

It's in private beta and I'm letting early users in free before it goes paid. Happy to answer
anything about how I built it and if you do this kind of work and want to try it, say so and I'll
sort you out.

So I went ahead and bought it.

The program includes 28 weeks of weekend live sessions and no-code AI automation consumer use cases and does not provide a certificate at the end. $60.

Now I'm in, I'm starting to wonder if I just paid for something I could've learned entirely on my own. Everything they're covering feels like it exists somewhere on YouTube or in free documentation already.

7 months of weekends is a serious time commitment. And I'm not sure a live session format is actually faster than just picking a tool and building something yourself.

Has anyone here been in a similar situation, paid for a long, structured course, and then realized self-learning would've gotten you there quicker?

If you think these kinds of courses aren't worth it, drop the resources you'd actually recommend in the comments. Free docs, YouTube channels, communities, anything that helps someone build real AI automation projects without needing a 28-week handhold.

I'm trying to figure out if I should stick with it or just cut my losses and go build stuff on my own.

Automation makes everything faster, scheduled posts, auto-replies, drip emails, the works. But sometimes I wonder if we're optimizing for consistency at the cost of actually sounding like a real person.

How others feel about this. Has automation ever made your brand feel a bit robotic or has it not really changed how human you come across?

been trying to figure out the best stack for workflow automation lately. looked at both but honestly still confused which one is worth going deep on. has something else taken over completely or are these two still the main ones people are using?

Here's the thing I keep running into. Half my feed is people arguing about which model won this week. The other half quietly swapped to a free open-weight model months ago and built something around it that the benchmark crowd can't touch.

The benchmark is a snapshot of one model on one task. What actually compounds is the system you wrap around it.

We learned this the boring way at the ai agents company I work for. Our early agents were basically good prompts. Vee for brand voice, Ines for Instagram, a few others. They worked until they didn't, and the moment we scaled, the same prompt produced generic output for every customer because a prompt knows nothing about who it's working for. No amount of prompt polishing fixes that.

The model is the cheap part now

This is where the open-weight shift actually bites. When a free model running 300 agents in parallel beats a paid model 5x its price on real research work, the smart money stops asking "which model" and starts asking "what runs on top of it."

Three questions matter more than the leaderboard:

How many runs can you afford to throw away? Cheap tokens change what you attempt.

Who checks the output? An open swarm is confident and under-cites, so we put one strong model at a single verify gate whose only job is to refute the work, never to praise it.

Does run fifty know anything run one didn't? If every run starts from zero, you have a prompt wearing a costume. A real loop saves what worked as a reusable skill and bakes every caught mistake into a constraints file the next run reads automatically.

That last one is the whole game. The model doesn't retrain between your runs. Your skill library grows. A competitor can clone your prompt in a minute, they can't clone six months of your real runs.

Where I'll admit I'm wrong-ish

Benchmarks aren't useless. If a new open model genuinely jumps a tier on reasoning, that flows straight into the loop and every run gets better for free. So I do watch them. I just stopped treating the top line as the decision.

And the open-weight stuff is rough in real use. Ours over-cites, contradicts itself across sub-agents now and then, and needs that verify gate babysitting it. The gate costs real tokens and it's the part of the setup I'm least happy about.

So genuine question for the people deep in this: is anyone running a fully open-weight loop, verify model included, with no paid model anywhere in the chain? Every setup I trust still has one expensive model guarding the gate, and I want to know if that's actually droppable yet or if we're all quietly paying the same tax.

I am tired of paying Gurus and marketing companies to create ads for my business with basically zero results.

Seeking advice on which tool(s) can help us create automated content and one to push it out to the social media channels

to drive traffic to our website. Feel free to DM me etc. Thanks in advance!

Got into a conversation with a marketing director last week. Her team of 4 was spending 60% of their time on tasks that didn't require creative thought: scheduling posts, pulling reports, writing first-draft email copy, building audience segments, and running competitor research.

None of that work needed a human brain. It just needed a human to approve it before it went live.

So we built something different. Not a black box that spits out marketing output you can't control. Instead, 89+ AI agents that do the repetitive work, but your team stays in the loop on everything that gets published or sent.

The result? One client built what used to take 3 people to do. No new hires. No outsourcing headaches. Just approval workflows that kept human judgment in place.

Here's what we're seeing work:

• Content research and first drafts (before your team writes)
• Email sequences (templated but still reviewed)
• Social media captions (batch approved, not auto-posted)
• Audience segmentation (built the rules, but you decide)
• Competitor reports (raw data pulled, formatted, human sign-off)
• Ad copy variants (AI writes 10, you pick the best 2 to run)

The catch: this isn't "set it and forget it" automation. It's "let AI handle the grunt work, you handle the strategy" automation.

For the builders here: we're using Claude for the reasoning layer, and we built this specifically for marketing and sales workflows. Not a generic AI tool. Not a no-code platform pretending to be AI.

Question for you: what's the one task your team does manually every week that you'd automate if you could do it without losing control?

Customer engagement platforms are web apps that help online stores automate lifecycle marketing across different touchpoints, such as email, SMS, push notifications, WhatsApp, chat, and voice, from one place.

Since I work in this niche and help eCommerce and DTC brands across the globe automate their marketing, I figured many stores still struggle to find the right tool for their business.

For online stores, there are several types of customer engagement platforms, and choosing the right one comes down to matching the platform with your actual business needs:

1. Markopolo AI - best for 1:1 hyper-personalized, omnichannel lifecycle marketing

Markopolo AI unifies six channels (email, SMS, push [web+app], WhatsApp, AI voice calls) into one platform. It triggers automated 1:1 personalized response based on the channels you want them to engage. The platform has native behavioral intelligence, so it always engages with the right customer with the right message, at the right time. Hence, customers find your eCommerce brand to be helpful, and never intrusive or spammy.

Result: better abandoned cart recovery rates and 1:1 personalized lifecycle marketing management

2. Gorgias - best for support-led customer engagement

Gorgias is different from the usual marketing automation tools because it starts from customer support, not campaigns. It centralizes conversations from channels like email, live chat, social, and helpdesk into one place, then uses AI and automation to resolve common customer questions faster. For eCommerce brands, this matters because many buying decisions happen inside support conversations: “Where is my order?”, “Does this size fit?”, “Can I change my address?”, “Is this product right for me?” Gorgias helps brands turn those moments into faster answers, smoother shopping experiences, and sometimes even additional sales.

Result: faster support response times, fewer repetitive tickets, and better conversion from customer conversations.

3. Attentive - best for conversational SMS engagement

Attentive is strong for brands that want SMS to feel less like a one-way promotional blast and more like a personalized conversation. It helps eCommerce brands send behavior-based SMS, email, RCS, and push messages triggered by actions like browse activity, cart abandonment, purchases, preferences, and subscriber behavior. The main strength is mobile-first engagement: instead of only sending generic campaigns, brands can use customer signals to send more relevant messages at the moment the shopper is most likely to act.

Result: stronger SMS engagement, better cart recovery, and more revenue from mobile-first customer journeys

4. Sendlane - best for email and SMS retention automation

Sendlane is built for eCommerce brands that want email, SMS, forms, and reviews connected in one retention-focused system. It is useful when the goal is not just to send campaigns, but to automate flows around abandoned carts, product interest, back-in-stock alerts, repeat purchases, reviews, and customer lifecycle stages. Its strength is in helping brands consolidate the retention stack without making the setup feel too fragmented across different tools.

Result: cleaner email/SMS automation with better workflows.

5. Customer.io - best for event-triggered lifecycle journeys

Customer.io is useful for brands that want more control over event-based messaging. Instead of only relying on basic segments, teams can trigger journeys from specific customer actions, such as viewed product, added to cart, subscribed, purchased, became inactive, clicked a campaign, or entered a specific lifecycle stage. It supports channels like email, SMS, push, in-app, and WhatsApp, which makes it strong for building connected customer journeys across multiple touchpoints.

Result: simpler lifecycle automation and fast messaging that tries to react to customer behavior.

Recently ran into an interesting forecasting problem.

The pipeline looked healthy, plenty of opportunities were sitting in active stages, and the numbers suggested a decent quarter ahead. But when we started digging into the deals behind those numbers, a different picture emerged.

Sales was moving opportunities through the pipeline even when buyers hadn't meaningfully engaged beyond receiving a proposal. Finance was building forecasts based on those pipeline numbers, while marketing was focused on lead volume. Everyone was looking at the same data, but drawing very different conclusions from it.

The biggest issue was what I'd call "ghost deals." Prospects would go silent for weeks and still sit in the active pipeline. On paper everything looked healthy. At quarter end, not so much.

What finally helped was paying less attention to activity metrics and more attention to stage-to-stage conversion rates. We also realized different teams had completely different definitions of what counted as a qualified opportunity, which created a lot of data inconsistencies.

After standardizing those definitions and cleaning up the pipeline, forecast accuracy became much more reliable and the numbers started reflecting what was actually happening in the business.

Curious how other teams handle stale opportunities. Do you automatically close deals after a certain period of inactivity, or is it usually a judgment call by sales managers?

put together every receipt i've spent on outbound coaching and it came to about 20 grand, didn't move my pipeline a single inch.

first was a $5k mastermind run by a former AE who'd sold to 2 F500 logos in his old job and figured that qualified him to coach, with weekly zoom calls and a slack channel that died after a few weeks, plus 12 lessons that boiled down to building a list and following up enough times...

stuff i could have read on any sales blog years ago.

second was the AI prompt newsletter cohort, where every newsletter promised a prompt that 10x'd someone's pipeline or a Clay table that did what FullEnrich does for a fraction of the cost, alongside screenshots of inboxes showing 35% open rates with no proof the meetings happened, all for $400 a year and the privilege of getting an inbox full of cohort-launch emails.

third is the LinkedIn ex-SDR cohort, the ones with 50k followers who quit their bag job and now make their living telling you the SDR playbook they used to hit quota at one company will work at all of them.

it never does, because the playbook is about being good at your specific ICP, which they never mention.

stopped giving them money a while back, and the pipeline didn't get worse, picked up roughly the same number of customers reading old threads on reddit and sending more calls.

what works in outbound is a lean stack of tools and 4 hours a week on the phone. the rest is selling certainty to people who don't have any.

Most people who feel stuck on social are quietly over-producing and under-distributing. They make a genuinely good post, drop it once on one platform, watch it get 400 views, and conclude they need to make more and better stuff. So they grind harder on the part that was never broken.

The math nobody runs: a single piece of content you already made can do five to ten times the work it's currently doing. You're leaving most of its reach on the table because you posted it once and moved on. Fixing distribution is faster, cheaper, and higher-leverage than making anything new. Here's the system I run.

RESURFACE: one asset, many native posts.

A "piece of content" isn't a post, it's a source. One decent video or article contains, at minimum:

• the core idea as a standalone text post
• the single best line as a one-liner
• a "most people get this wrong" framing of the same point
• a 3-bullet how-to pulled from the middle
• a question version that just asks the thing the content answers

That's five native posts from one source, and "native" is the whole game. Reposting a TikTok link to X gets you nothing. Rewriting the idea as a text post that belongs on X gets you reach. Don't move the file around. Move the idea around and dress it for each room.

ADAPT: the identical-everywhere trap.

The most common cross-posting mistake is blasting the exact same caption and format to every platform from one button. It feels efficient and it quietly kills reach, because every platform punishes content that obviously wasn't made for it. The fixes are cheap:

• LinkedIn wants the lesson and the takeaway up top, no slang
• X wants the sharpest single line, no setup
• Instagram/TikTok want the hook in the first 2 seconds and the rest in the caption
• vertical video is a different crop than horizontal, and it's worth the 30 seconds to recut

Same idea, four wrappers. You're not making four pieces of content, you're translating one.

SCHEDULE: cadence can't depend on your mood.

This is the step that separates accounts that grow from accounts that die. Consistency beats brilliance on every platform, and "I'll post when I feel inspired" is the same as "I'll post randomly and then stop." Batch it instead: sit down once, produce a week or two of these adapted posts in one session, and queue them so they go out whether or not you feel like showing up that day. The motivation problem disappears the moment posting is a decision you already made.

(Upfront: I build a scheduler called PostSyncer, so I'm biased toward automating this step. Use whatever fits Buffer, Publer, a Google Sheet and phone alarms. The specific tool matters way less than the fact that your cadence stops being a daily willpower tax.)

RECYCLE: your best post isn't done.

A post that performed well three months ago will perform well again with a new audience, because almost nobody who saw it then will see it now, and the ones who did have forgotten. Keep a short list of your top performers and re-run them on a slow rotation with a fresh hook. Evergreen content has no expiry date; you just keep re-presenting it. The "I'm out of ideas" panic usually means "I forgot I already have winners."

MEASURE: track the outcome, not the applause.

Pick the one number that actually matters to you clicks to your link, email signups, DMs, sales and watch which platform and which format drive it. You'll usually find one channel quietly doing most of the real work while another eats most of your time for likes that go nowhere. Once you know that, you stop spreading effort evenly and pour it into what converts.

That's the whole thing: resurface one source into many, adapt per platform instead of cloning, schedule so cadence survives your mood, recycle your winners, and measure the outcome instead of the vanity number. It costs almost no new creative work, it's pure leverage on stuff you've already got.

Happy to go deeper on any part in the comments, the per-platform adapting rules and the recycle rotation especially. Tell me what platforms you're running and what you're actually trying to get out of them, and I'll suggest a distribution setup for it.