How to Research 20 High-Quality Prospects in Under 30 Minutes
The Bottleneck of Serial Research
The primary bottleneck in B2B prospecting is not tooling, but the serial nature of human research. We built a new method that uses parallel AI agents to research 20 high-quality prospects at once, reducing weeks of work to under 30 minutes.
Traditional prospecting is a linear, manual process. A researcher finds a company, opens tabs for its website, LinkedIn profile, and recent news, finds a contact, and then repeats the entire sequence. A human can only focus on one prospect at a time, creating a direct trade-off between the speed of prospecting and the quality of the research.
The time cost is significant. Sales development representatives (SDRs) spend between 1.4 and 3.2 hours per day on manual research. Salesforce’s “State of Sales” report found that sales reps can spend up to 72% of their time on non-selling activities, with research being a major component. This manual work is not just slow; it is cognitively expensive.
Switching between dozens of tabs for each prospect incurs a high cognitive cost. Research from the University of California, Irvine shows it can take over 23 minutes to fully return to a task after an interruption. Other studies indicate workers can lose up to 40% of their productive time to this context switching. This constant reorientation leads to errors, inconsistent data, and eventual burnout.
A New Model: Parallel Research Agents
Instead of one person researching 20 companies sequentially, our model allows one person to task 20 autonomous AI agents to research them simultaneously. Each agent is an independent process that can browse websites, parse data, and find specific information based on a single directive.
The process is analogous to parallel computing, where a complex problem is broken into smaller pieces and solved by multiple processors at once. In this case, the complex problem is building a qualified prospect list. The result is that the time required to research 20 prospects is roughly the same as the time required to research one.
A Practical Workflow: 20 Prospects in 27 Minutes
This model shifts the operator's role from low-level researcher to high-level analyst. Here is a typical workflow using Drevon.
Step 1: Define the Prompt (2 minutes)
Create a precise definition of your ideal prospect and the intent signal you want to find. Job postings are one of the most reliable signals, as they are public declarations of budget and need. A strong prompt is specific:
Find me US-based B2B SaaS companies with 50-250 employees that have posted a job for a 'Head of Sales' in the last 60 days. For each, find the VP of Sales and a link to the job post.
Step 2: Deploy the Agents (1 minute)
Input the prompt into the Drevon app. This single command launches 20 independent agents, each tasked with finding one unique company that fits all the defined criteria.
Step 3: Parallel Research Phase (20 minutes)
The agents work simultaneously. Each one autonomously browses sources like LinkedIn, company career pages, and job boards to find a match. Once a company is found, the agent verifies the intent signal—the specific job posting—and extracts the required contact information.
Step 4: Synthesize and Review (4 minutes)
The agents return their findings as a structured list. The output includes the company name, the contact person, a direct link to the job posting as proof of intent, and any other requested data. The operator’s job is no longer to hunt for data but to analyze a complete, verified list and begin outreach.
The Output: Speed Without Sacrificing Depth
This method does not trade quality for speed. Because each agent has a single, focused task, it can perform deeper, more consistent research than a human juggling multiple targets. The final output is not just a list of names; it is a qualified list where every entry is accompanied by verifiable proof of intent.
Acting on verified intent signals dramatically improves results. Standard cold outreach typically sees reply rates of 1-3%. In contrast, outreach based on a strong intent signal can be 3 to 5 times more effective. This changes the nature of the work. The GTM team spends less time on manual data entry and more time on strategy and personalized outreach.
Limitations
This approach has limitations. Current web-browsing agents cannot reliably parse all dynamic, JavaScript-heavy websites. They are also frequently blocked by sophisticated anti-bot systems like CAPTCHAs. This means that while they are effective against common sources of business data like LinkedIn, corporate sites, and job boards, they cannot access 100% of the web. The system is designed to fail gracefully on a single target and move on, ensuring the overall process still delivers results.
The core shift is from serial researcher to parallel analyst. By offloading the repetitive work of finding and verifying prospects to autonomous agents, a single operator can generate the output of a team of researchers in minutes. You can begin using this workflow to build your own high-intent prospect lists.