Build an AI System to Find Your First 10 Clients
You find clients before starting by building an automated prospecting system. This system identifies ideal prospects online and engages them with personalized outreach.
This is not a simple scraper but a production-grade AI agent. It needs to parse websites, understand job roles, and use the Claude API to write human-like emails that book meetings. The scope depends on the number and complexity of your target data sources, from public forums to industry-specific newsletters.
We built a prospecting agent for a new B2B consultant targeting martech companies. It monitored 5 industry forums and 2 subreddits for relevant conversations. In its first month, it identified 87 qualified leads and booked 11 discovery calls, achieving a 12.6% conversion rate from lead to call.
What Problem Does This Solve?
Most founders start with manual outreach on LinkedIn. This takes 20 hours a week to find 100 relevant profiles and another 10 hours to write personalized connection requests. The process is so time-consuming that most founders abandon it within weeks to focus on other parts of the business, leaving their pipeline empty.
Off-the-shelf tools like Apollo.io provide lead lists but their automation is template-driven. A prospect receiving an email that says 'Hi {firstName}, saw you're {title}' deletes it instantly because it lacks any real context about their needs. These tools are databases with a generic mail-merge function, not an intelligent engagement system.
Scraping tools like Phantombuster are brittle and risky. They run in your browser and are easily detected by platforms like LinkedIn. One minor user interface update can break the scraper entirely. Running it too aggressively can get your account suspended, cutting off your primary channel for finding new clients.
How Does It Work?
First, we define your ideal client profile with over 20 specific attributes, going beyond just title and company size. We identify non-obvious data sources like industry job boards, forum comments, and podcast guest lists. We use the Claude API to read and categorize this unstructured text, surfacing buying signals like 'we're looking to hire an agency' or 'our current reporting is a mess'.
The system's core is an AI agent written in Python using httpx for asynchronous web requests. It runs on a schedule on AWS Lambda, continuously checking your defined sources for new signals. When it finds a potential prospect, it uses the Claude API to draft a personalized outreach message based on their recent online activity. If a prospect posts about a specific problem, the AI drafts an email referencing that exact post.
The agent's drafted messages are written to a Supabase database for your review and approval. Once approved, messages are sent automatically through your own Google Workspace account. Every interaction is tracked, creating a closed-loop system. The average time from prospect identification to a ready-to-send email draft is under 15 seconds. The entire monitoring and execution infrastructure costs under $50 per month to run on AWS.
We use structlog for structured logging so that if a data source changes its API or a message fails to send, we get an immediate alert with full context. This allows us to debug and deploy a fix in minutes, not days. You get a simple dashboard showing leads identified, emails sent, reply rates, and meetings booked, giving you a real-time view of your pipeline.
What Are the Key Benefits?
Book Your First Calls in 14 Days
The complete prospecting agent is deployed and finding leads in a 2-week build cycle. You can start talking to potential clients immediately.
One-Time Build, Zero Per-Lead Fees
A single project cost for the build. You pay only for cloud usage, typically under $50/month, not per-seat or per-lead SaaS fees.
You Get the Full Source Code
The Python agent code is delivered to your private GitHub repository. You own the system and can modify it as your business grows.
Alerts for Broken Sources, Not Silence
The system monitors its own data feeds. If a website changes, you get a Slack alert instead of discovering weeks later that your lead flow died.
Sends from Your Personal Inbox
The system sends email via your Google Workspace or O365 account. Replies land in your inbox, making conversations natural.
What Does the Process Look Like?
ICP Definition (Week 1)
You provide a detailed brief on your ideal client. We translate this into a list of 10-15 online data sources and a set of 20+ query parameters for the agent.
Agent Build & Test (Week 1)
We build the core Python agent and its data parsers. You receive a daily list of prospects the agent finds for your approval, which fine-tunes the logic.
Deployment & Live Run (Week 2)
We deploy the agent to AWS Lambda and connect it to your inbox and Supabase. The agent begins its first live prospecting run, with you reviewing all drafted messages.
Handoff & Monitoring (Week 2)
We finalize the dashboard and deliver the full source code and a runbook. For 30 days post-launch, we provide support and make one round of tuning adjustments.
Frequently Asked Questions
- How much does a system like this cost?
- Pricing is a fixed, one-time fee based on the number and complexity of data sources. An agent that monitors 3-5 public sources is a standard 2-week build. Integrating with private APIs or sources requiring complex authentication adds scope. We provide a firm quote after a 30-minute discovery call at cal.com/syntora/discover.
- What happens if a site blocks the agent?
- We build agents to respect robots.txt and use throttled, asynchronous requests to mimic human behavior. We never scrape platforms requiring a login, avoiding account suspension risks. If a public site blocks an IP address, the AWS Lambda function automatically rotates to a new one. You get an alert, but the system often self-heals.
- How is this better than hiring a virtual assistant (VA)?
- A VA performs manual tasks. They get tired, make mistakes, and can only work so many hours. This AI agent works 24/7, processes information thousands of times faster, and drafts context-aware outreach that a VA cannot match. You are building an asset you own, not renting someone's time.
- Will this system send spam and hurt my reputation?
- No. This is an 'assistant', not a blaster. The agent identifies prospects and drafts messages, but you approve them before anything is sent. We build a simple UI where you can review 50 drafted outreaches in 10 minutes. This human-in-the-loop step ensures quality and protects your brand from sending irrelevant messages.
- Can this system grow with my business?
- Yes. The agent is built with a modular design in Python. Adding a new data source is typically a half-day of work. We can add logic to route different types of leads to different team members or connect it to a CRM like HubSpot once you have one. The Supabase backend can handle millions of records.
- What do I need to provide to get started?
- You need a clear idea of your first ten ideal clients and a business email account (Google Workspace or O365). You do not need any technical skills. During the discovery call, we will work together to define the data sources and prospecting angles. Syntora handles the entire build and deployment.
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