Deploy an AI Prospecting Agent That Finds Your Next Customer
The best AI agent for prospecting is a custom system built for your specific ICP and sales motion. Off-the-shelf AI SDRs often fail because they cannot adapt to your unique value proposition or lead sources.
Syntora specializes in designing and building custom AI prospecting agents tailored to specific business needs. Our approach focuses on engineering systems that adapt to unique value propositions and complex lead generation criteria, rather than offering off-the-shelf solutions.
A truly custom build is more than just a prompt. It involves connecting multiple data sources, orchestrating multi-step research workflows, and managing the evolving state of each prospect. An agent designed to identify manufacturing companies using specific technologies differs significantly from one that finds e-commerce brands based on recent funding rounds. The underlying logic is always tailored to the precise signals that matter for your business goals. Syntora focuses on understanding your unique requirements to design an agent that delivers relevant results without the limitations of generic tools.
What Problem Does This Solve?
Many teams first try point-and-click AI sales tools like Clay. These platforms are excellent for enriching a static list with data points, but their logic is linear. A workflow that needs to visit a website, understand its content, and then decide on a new research direction cannot be built. The process is a waterfall, not a dynamic investigation.
Others attempt to build a process around the ChatGPT web UI. A sales rep spends hours copy-pasting prospect details into a text box with a long, complex prompt. This manual process is impossible to scale, has no error handling, and cannot be integrated with a CRM. If a website is down, the human has to remember to come back later. The system has no memory or persistence.
A common scenario is a 15-person tech consultancy that needs to find companies that recently hired a VP of Engineering and use AWS. Using a tool like Apollo.io, you can find the person but not their company's tech stack in a reliable way. Using Clay, you can enrich the tech stack but cannot confirm it by reading the company's job postings. A human must bridge the gap, defeating the purpose of automation.
How Would Syntora Approach This?
Syntora's approach to building a custom prospecting agent begins with a thorough discovery phase to define your Ideal Customer Profile (ICP) and sales process. This involves codifying your target criteria into a set of precise rules that guide all subsequent automation.
The technical architecture for such a system would typically involve several key components. For initial candidate generation, we would integrate with data APIs like Apollo and People Data Labs, drawing a pool of potential companies. This data would then be enriched by targeted web scraping, potentially using a Python script with the `requests-html` library, to extract specific signals from company websites, such as technology mentions or customer case studies relevant to your ICP.
The core workflow often uses a multi-agent system pattern, such as one implemented with LangGraph. In this design, a supervisor agent would assign tasks to specialized sub-agents. For example, a `Researcher` agent could query a Serper search API for recent news, a `Web-Scraper` agent could extract text from career pages to identify growth signals, and a `Qualifier` agent would compare all gathered data against your defined ICP rules. The system's progress for each prospect would be managed and persisted in a robust database, such as Supabase Postgres, ensuring continuity and data integrity even if workflows are interrupted.
For the final output, a `Writer` agent, utilizing a large language model like the Claude 3 Opus API, would synthesize all collected data points into a personalized communication draft. This draft is composed uniquely for each target, reflecting the specific insights gathered.
Deployment for such a system would typically involve a scalable cloud platform. We would deploy the agent as a FastAPI service on AWS Fargate, triggered by a scheduled job to align with your desired prospecting cadence. Operational visibility is a priority, so we would implement structured logging with `structlog` to send detailed operational data to AWS CloudWatch. Error handling mechanisms would also be designed to manage API failures, flagging leads for manual review after a defined number of retries and allowing the system to continue processing other prospects without interruption. This ensures a reliable and observable automated prospecting pipeline.
What Are the Key Benefits?
Go From Idea to Qualified Leads in 4 Weeks
We deploy the full prospecting system in under one month. You get a pipeline of qualified, researched leads flowing directly to your sales team, not a long setup process.
Pay Once, Own The Machine Forever
A one-time build cost replaces unpredictable monthly SaaS fees that scale with volume. Your only ongoing cost is the direct pass-through for APIs and hosting.
Your Process, Your Code, Your IP
You receive the complete Python source code in a private GitHub repository. This is your asset, not a black-box subscription you rent access to.
Know Exactly When and Why It Fails
We build in detailed logging and Slack alerts. If a lead source API changes, you get a notification with the exact point of failure, not a silent drop in lead quality.
Works With The CRM You Already Use
Qualified leads and research notes are pushed directly to HubSpot, Pipedrive, or Salesforce via their native APIs. No manual data entry for your sales reps.
What Does the Process Look Like?
ICP and Workflow Mapping (Week 1)
You provide your ideal customer profile and current prospecting workflow. We deliver a technical specification document outlining the agent's logic, data sources, and scraping targets.
Agent Build and Initial Run (Weeks 2-3)
We build the core agent system and run it on a sample of 100 prospects. You receive the first batch of generated emails and research notes for review and feedback.
Tuning and Integration (Week 4)
Based on your feedback, we refine the agent's logic and personalization quality. We connect the agent to your CRM, pushing a test batch of 20 leads directly into the system.
Launch and Monitoring (Post-Launch)
The system goes live, running on its schedule. You receive a runbook, access to the source code, and we monitor performance for 30 days to ensure stability and accuracy.
Frequently Asked Questions
- How much does a custom AI prospecting agent cost?
- Pricing depends on the complexity of the research workflow and the number of data sources. A system that just finds contact info is simpler than one that scrapes websites and synthesizes news. Most projects have a one-time build cost, followed by minimal monthly hosting and API fees. We scope the exact price on a discovery call. Book a call at cal.com/syntora/discover.
- What happens when a prospect's website changes its layout and the scraper breaks?
- Our web scraping agents use semantic selectors, not brittle CSS paths, which minimizes breakage. When a site changes fundamentally, the scraper will fail. The system logs the specific URL and error, sends a Slack alert, and continues with the rest of the batch. This allows us to update the scraper for that site without halting all prospecting operations.
- How is this different from hiring a human SDR on Upwork?
- A human SDR costs more per month, is not available 24/7, and their quality can be inconsistent. An AI agent runs the exact same high-quality process on every single lead, every single time. It can process 1,000 leads with the same diligence as 10. You are building an asset that scales, not renting someone's time.
- Can the agent engage in conversations over email?
- We build agents for the initial outreach. Engaging in back-and-forth conversation requires a different architecture focused on state management and intent detection. We recommend starting with the top-of-funnel research and personalization task, as it delivers the highest immediate ROI for sales teams by filling their pipeline with hyper-qualified opportunities.
- What lead sources can the agent use?
- We connect to any source with an API or that can be reliably scraped. This includes databases like Apollo, Crunchbase, and LinkedIn Sales Navigator (via browser automation), as well as niche industry directories or public records. We combine multiple sources to build a richer profile of each prospect than any single tool can provide.
- Who reviews the emails before they are sent?
- The agent generates drafts, it does not send them automatically. The final output is typically a Google Sheet or an entry in your CRM with a 'Draft' status. This gives your sales team final approval, allowing them to add a final human touch or veto a message. This human-in-the-loop step is critical for quality control.
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