B2B Agencies AI Agent for Reddit, X, LinkedIn and Google Maps (And What It Actually Costs)

How It Started

One person generating $300K/month by simply doing cold outreach.

I read a tweet on the timeline from a guy sitting in Bali. He wakes up every day and his Claude Code agent gives him 20-50 leads per day using Apollo's lead-generating and enriching API. He literally spends 45 minutes cold emailing, cold calling, and direct messaging each of them, every single day.

His total revenue was more than $300K ARR, off 250,000 messages sent.

I was thinking deeply about this for a long time, because our own Reddit AI trend scrapers already do the same thing for a few of our clients. We internally provide a dashboard/CRM listing the leads data, refreshing and enriching it on a weekly basis. It's been a genuinely useful way to hand clients proper leads — real OG, actual customers who can be your buyers, not just names on a spreadsheet.

I applied the same analogy to other departments and started building custom agents for every kind of lead, news, data, and content we needed.

AI Agents for B2B Agencies and Solo Founders

Here's the actual list of scrapers/agents we've built or are running:

• Reddit trend scraper from 5-10 subreddits of interest
• Reddit scrapers for SaaS ideas, programming questions, doubts, and queries/content
• Reddit B2B clients asking for an exact business query
• iHateReading programming-related top news, LinkedIn and X trending posts and content
• Angel investors writing checks between $100K-$5M, active on LinkedIn and X
• YC & a16z investment/portfolio companies/startups since 2021 till 2026 — up to 1,000 YC companies scraped for our clients' B2B marketing
• IndiaMart B2B clients for a specific business/query/requirement
• Top mobile apps from the App Store and Play Store, with rating, reviews, descriptions, downloads, and comments
• Individual small influencers, up to 1M subscribers/followers, from YouTube, Instagram, and TikTok
• AI design prompts pulled from several directory websites
• B2B early-stage deeptech and tech startup founders from the last 5 years

Each scraper and agent costs us money, but in return we've ended up saving more than $5K/month compared to paying full price for Apollo, Firecrawl, SERP APIs, Google Maps scraping, LinkedIn APIs, X, YouTube, Reddit, TikTok, and AI/LLM provider APIs — all of which add up fast at scale. We've cut those costs mainly by following two rules:

1. Every scraper runs on an interval, following the rules and obeying robots.txt, so API calls to servers and databases (and therefore cost) stay minimal.
2. Every scraper only injects AI when it's actually needed — reducing cost and cutting our dependency on heavy AI usage where it isn't buying us anything.

We've also built custom in-built scraping APIs for Google, LinkedIn, X, Reddit, Maps, the Apple Store, and general websites, instead of paying full retail for someone else's wrapper.

If you want the actual code behind one of these, I wrote up the Reddit trend scraper that saved us $1,000/month on content creation separately.

Why We're Building These Agents

Every agent can be integrated into B2B agencies, companies, and by solo founders directly.

Every agent saves time and provides clean, enriched data on a dashboard — the kind of enrichment that usually costs more than a 2-person job in compensation to do manually.

How to Run Agents on Cloud

We usually put everything in Docker and deploy on Fly, Render, Railway, AWS, or GCP.

It depends on the job, but my honest preference is: whatever's cheap and easy.

One Dockerfile, a few API keys or secrets, and one CLI command to deploy the entire agent stack on Fly, get the domain, and the API is ready.

For scraping specifically, we usually prefer trigger.dev and CRON schedulers to run the RSS fetcher or Reddit scrapers on the cloud at intervals. I've found this to be the quickest and easiest way to put a real dashboard in front of all our AI agents, instead of SSH-ing into a box every time something breaks.

AI Agent Dashboard

Most of you probably don't know this, but I'm a big fan of dashboards and CRMs. I've even built an entire CRM template using Next.js and Vite, with 60 pages in total.

The saascrm.site template has already helped 10+ B2B businesses roll out their own CRM instead of starting from zero.

I used Cursor prompts to build the CRM and the agent dashboard pages, which broadly contain:

• View all agents
• Run/View/Start/Stop/Remove AI agents
• Data table created by the agents
• Documentation for all agents

Dashboards ease understanding, and that's exactly why I made them — since AI makes it easy, saascrm.site/prompts is our prompts library that makes it a cakewalk to just copy, paste, and get your own CRM running. I go into more of why vertical CRMs specifically keep making sense in this separate breakdown, if you want the fuller argument.

Once an agent is started, the dashboard shows real-time scraping results — it fetches data, loops through intervals, scores it with an LLM, and updates the live table. It also streams the raw CLI output alongside it, for a better debugging experience than staring at a terminal.

In the left sidebar, you can see all agents, click into details, watch the live tables, and read the CLI output, along with a Start/Stop toggle. One dashboard, and our entire 2-4 to 10-person-job equivalent is handled and completed.

Every agent run has an AI/LLM call baked in, either through a provider directly or through a routing API. That means every run costs real money, and to stay on top of that we always calculate total input + output tokens and the model used, to get a solid USD estimate per run.

Every API call to an AI/LLM provider gets recorded and stored — input/output prompts, model type, and name — so we can track and reduce costs across every agent. One good habit that actually moves the needle: minimize the context you inject into the system prompt. Less context in, less cost out.

AI LLM Providers and Costs

My first preference was OpenRouter.

But there are quite a few good options now for teams and solo founders who don't want to lock into a single provider.

Here's the extensive list of OpenRouter alternatives we've actually looked at:

Most of these solve the same underlying problem — you don't want your whole agent stack hardcoded to one model provider's pricing and uptime — but they differ a lot in how much routing intelligence, observability, and caching they bundle on top. If you're just starting out, picking one gateway and actually tracking cost-per-run against it will teach you more than reading every comparison post about which one is "best."

If there's one thing running 10+ agents in production has taught me, it's that the agent itself is rarely the expensive part — it's the scraping, the context you feed it, and how often you let it run unattended. Get those three under control first, and the LLM bill mostly takes care of itself.