Build Custom AI Research Agents with Lucid Engine + Perplexity (A New Consulting Service)
Category: Monetization Guide
Excerpt:
Stop your clients from manually running the same research queries every week. This guide shows you how to use Lucid Engine to build automated AI research agents, powered by the principles of Perplexity, into a high-value consulting service for VCs, marketers, and strategists who need consistent, deep insights.
Last Updated: February 4, 2026 ·
AI Agent Building / Research Automation
Your client’s best analyst spends 10 hours a week doing the same manual research.
I once worked with a VC firm where a brilliant junior analyst had a Monday morning ritual. Her job was to answer one question: "What were the 5 most interesting AI startups launched last week?"
Her process was a masterclass in manual diligence. She'd open 20 tabs: Product Hunt, Hacker News, Twitter, TechCrunch, specific subreddits. She'd filter, read, and identify the top contenders. Then, for each one, she'd find the founder on LinkedIn, look up their funding history on Crunchbase, summarize their value proposition, and paste everything into a meticulously formatted Notion table for the partners' meeting.
It was valuable work. It was also a soul-crushing, expensive grind that burned out their smartest junior person. The real cost wasn't her salary; it was the opportunity cost. Instead of analyzing *why* a startup was interesting, she was just finding it.
This guide is about selling the solution to that exact problem. You'll use Lucid Engine to build an automated workflow and the principles of Perplexity to ensure the research is deep and reliable. You're not just saving them time; you're building a custom intelligence agent that becomes their competitive advantage.
The "Manual Research Tax": A Hidden Drain on Every Smart Team
Most companies don't track this, but the cost of repeatable, manual research is staggering. It's a tax paid in hours, morale, and missed opportunities.
It's Slow & Expensive
Your client's team is copy-pasting between browser tabs, manually summarizing articles, and formatting reports. If an analyst earning $80k/year spends 25% of their time on this, that's a $20,000 annual cost for work a machine could do.
It's Inconsistent & Biased
One analyst's "deep dive" is another's quick skim. The quality depends on who does it and how much coffee they've had. The process isn't standardized, leading to inconsistent outputs and decisions based on patchy data.
It Kills Morale & Focus
You hire smart people to think, not to be human web scrapers. Forcing them into repetitive, low-value tasks is a fast track to burnout and turnover. They should be analyzing the data, not just gathering it.
Your Service: Building Custom AI Research Agents
You're not selling access to tools. You're a consultant who designs and builds bespoke, automated research workflows. Your deliverable isn't a report; it's the *machine that generates the report*. This is a high-leverage service that transforms how a team operates.
The "What": A Custom Workflow
A workflow built in Lucid Engine that automatically scours the web, news APIs, and social media, synthesizes the information based on your client's specific criteria, and delivers a structured report to their preferred destination (Email, Slack, Notion) on a set schedule.
The "Why": The ROI
You save dozens of high-value employee hours per month. You ensure research is consistent and comprehensive every single time. You allow your client's team to focus on analysis and decision-making, which is where their real value lies.
The Toolkit: The Scout and The Factory
Perplexity: The Scout
Perplexity is your compass. Before you automate anything, you use it to explore the terrain. Its strength is in providing sourced, conversational answers that help you understand a topic deeply and quickly.
Your Job with Perplexity:
- Manually prototype the research process.
- Discover the most reliable sources (blogs, news sites, forums).
- Refine your research questions into precise prompts.
- Understand the nuances of a topic before you try to teach an agent about it.
Lucid Engine: The Factory
Lucid Engine is where you build the assembly line. It's a visual, node-based platform for creating AI agents. You don't write code; you connect blocks that represent actions.
Your Job with Lucid Engine:
- Translate your manual research process into an automated workflow.
- Chain together nodes like "Search Web," "Get Content," and "LLM Prompt."
- Handle data: loop over lists, store variables, format outputs.
- Set a schedule for the agent to run automatically.
The Blueprint: Building a "Competitor Pulse" Agent, Step-by-Step
Let's get extremely practical. Here's the detailed process for building an agent that monitors three competitors and delivers a weekly summary. This is the core of your service.
Phase 1: The Strategy Session & Manual Brief (with Perplexity)
This is where you prove your value beyond just being a "tool person." You're a strategist first.
1.1. Define the "Jobs to Be Done" for the Research
On a call with the client, ask "why" five times. Don't just ask "What do you want to track?" Ask "What decision will this report help you make?" Get to the core need. Is it for sales enablement? Product strategy? Investor updates?
1.2. Prototype the Research with Perplexity
Open Perplexity and start acting like the analyst. Use its "Focus" feature to narrow down sources (e.g., Academic, YouTube, Reddit).
"Summarize recent news and official blog posts from competitor-x.com over the last 14 days."
"What are the main complaints about Competitor Y on Reddit's /r/saas subreddit?"
"Find recent interviews or podcast appearances by the CEO of Competitor Z."
As you do this, you're not just getting answers. You're identifying the *types* of queries and *sources* that yield the best signal. This is critical for the next phase.
1.3. Create the "Golden Briefing" Document
Assemble one perfect, manually-created report. This document is your North Star. It should have clear sections:
- Executive Summary: 3 bullet points of the most important takeaways this week.
- Competitor A: Key announcements, product updates, marketing campaigns.
- Competitor B: ...and so on.
- Market Chatter: What are people saying on social media and forums?
Get the client to sign off on this format. "This is what the agent will produce every week. Does this help you make better decisions?"
Phase 2: Building the Agent in Lucid Engine
Now, translate that manual process into a Lucid Engine workflow. Here's a more detailed breakdown of the nodes:
2.1. The Input and Loop
Start with an Input Node containing a list of your target competitors. Connect this to a Loop Node to process each one individually.
2.2. The Research Core (Inside the Loop)
For each competitor, you'll have a chain of nodes:
Type: Web Search
Query: "site:{competitor_url}/blog OR site:techcrunch.com '{competitor_name}'"
Parameters: Time Range = "Last 7 Days"
// Node 2: Get Content
Type: Get Page Content
Input: Connect to the URLs from the Web Search node.
// Node 3: Summarize Content
Type: LLM Prompt
Prompt: "You are a business analyst. Based on the following content, provide a concise summary of key announcements, product launches, and marketing campaigns for {competitor_name}. Ignore minor blog posts. Format as markdown bullet points."
Input: Connect to the text from the Get Content node.
2.3. The Final Synthesis
After the loop finishes, you'll have a collection of summaries. Now, you need to assemble them into the final report.
Type: LLM Prompt
Prompt: "You are an executive assistant. Combine the following weekly summaries into a single, clean report titled 'Weekly Competitor Pulse: {current_date}'. Start with a 3-bullet executive summary of the most significant events across all competitors. Then, create a section for each competitor using their name as a heading. The tone should be professional and direct."
Input: Connect to the collected summaries from the loop.
2.4. The Delivery
The final step is sending the report.
Type: Send Email (or Post to Slack, or Create Notion Page)
Recipient: {client_email_address}
Subject: Weekly Competitor Pulse Report
Body: Connect to the formatted report from the previous LLM node.
Phase 3: The QA and Tuning Loop
An agent is a living system. Your job isn't done at launch. You need a quality assurance process.
Agent QA Checklist
- Source Reliability: Is the agent pulling from credible sources, or is it citing forum comments as fact? (Refine the Web Search node's query to include/exclude sites).
- Summary Accuracy: Does the AI summary faithfully represent the source material? (If not, make the LLM prompt more specific).
- Formatting Consistency: Is the final report clean and easy to read every time? (Adjust the final formatting prompt).
- Hallucination Check: Did the agent invent any facts, features, or events? (This is rare with RAG, but always check).
- Completeness: Did the agent miss a major announcement you found manually? (Your Perplexity prototype is the ground truth here).
Pro Tip: Add a "Human-in-the-Loop" node before the final Output. This sends the drafted report to you for a quick review. You can approve it with one click or make minor edits. This offers the perfect blend of AI speed and human oversight, which is a huge selling point for high-stakes research.
Packaging Your Service: From Project to Retainer
Starter Agent Build
$1,200 - $2,500 (one-time)
A single-purpose agent that automates one core research task.
- 1 x Strategy & Prototyping Session
- 1 x Custom Lucid Engine Agent
- Weekly Email or Slack Delivery
- 1 x Training & Handoff Session
Advanced Intelligence Engine
$3,000 - $7,000 (one-time)
A multi-source agent with human review and custom output formatting.
- Deep Dive Strategy Workshop
- Complex Multi-Step Agent Build
- Notion or Google Sheets Integration
- Human-in-the-Loop Approval Step
- 30 Days of Post-Launch Support
Agent Maintenance Retainer
$500+ /month
Ensures your agent stays effective as sources and needs change.
- Weekly Performance Monitoring
- Monthly Prompt & Source Tuning
- Fixing Breakages (e.g., website structure changes)
- Priority Support
Ready to Build?
The best way to learn is by doing. Build a small agent for yourself first. Automate something you do every week.
Once you feel the leverage firsthand, it becomes much easier to sell.
Links include utm_source=aifreetool.site for tracking.
A Final, Honest Note
This isn't a "no-code" solution in the sense that you can just click three buttons and have a perfect agent.
Building a robust workflow in Lucid Engine requires logical thinking, testing, and an understanding of how APIs and data flow.
It's less like programming and more like designing a complex system with visual blocks.
The value you provide isn't just connecting the nodes. It's the strategic work you do in Phase 1:
understanding the client's real needs, defining the right questions, and designing a process that delivers
signal instead of noise. The AI is the engine, but you're the architect. That's what clients are paying for.
