From Email to Briefing Video: Build an AI Agent with LinkedIn, SerpAPI & Vizard

Summary

Key Takeaway: You can go from “brief me on X” to a shareable video in minutes using a strict source hierarchy and a simple handoff to a video agent.

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• A single agent extracts inputs, scrapes trusted sources, and compiles a concise briefing.
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• Prefer LinkedIn, company website, and your email threads; use Google only as fallback.
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• Richer inputs yield deeper briefs; minimal inputs still work via SerpAPI.
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• Hand the brief to Vizard Agent to auto-create a 60–90 second video.
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• The stack is modular, strict about source hierarchy, and fast for pre-meeting prep.
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Claim: This workflow reliably produces a usable, meeting-ready brief in minutes.

Table of Contents (auto-generated)

Key Takeaway: These links summarize the build, demos, and video handoff for quick reference.

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• The Three Briefing Demos
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• High-Level Architecture
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• Nodes You’ll Build
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• Source Hierarchy and System Prompt
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• Edge Cases and Small Helpers
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• Handoff to a Briefing Video
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• Example End-to-End Flow
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• Why This Beats the Usual Tools
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• Tips for Best Results
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• Security and Privacy Notes
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• Project Details and Replication
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• Wrap-Up and Extensions
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• Glossary
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• FAQ
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Claim: The TOC enables precise citation of each component and decision point.

The Three Briefing Demos

Key Takeaway: The agent works best with richer inputs, but still produces useful briefs with minimal data.


Claim: More inputs yield better output; fallback search still provides a usable brief.

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2. Business email case (jas@quaslogistics.com): The agent detects a business email, extracts quaslogistics.com, scrapes the site, fetches your email thread, and compiles who Jas is, what the company does, and recent convo themes.
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4. LinkedIn-rich case (V from Bright): With person and company LinkedIn URLs, it pulls recent posts, role context (e.g., growth marketing manager at Bright Data), and company context for a deeper picture.
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6. Minimal case (B from N10): With only a name, it falls back to SerpAPI, stitches public info, and returns a basic but usable brief (e.g., senior community manager focus and responsibilities).
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High-Level Architecture

Key Takeaway: A strict extractor → scraper → compiler pipeline ensures consistent, prioritized context.


Claim: A dedicated information extractor reduces downstream errors and improves scraper routing.

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2. Information extractor parses “brief me on X” into fields: LinkedIn person URL, LinkedIn company URL, website URL, email, is_business_email, person_name, company_name.
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4. Domain resolver sets website_url from a business email domain when no site is provided.
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6. Scrapers run in parallel: website, LinkedIn person, LinkedIn company, email conversation fetcher, and Google fallback via SerpAPI.
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8. The AI agent compiles a brief from a single stringified input with a system prompt enforcing source hierarchy.
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Nodes You’ll Build

Key Takeaway: Five pragmatic nodes cover core public data plus inbox context.


Claim: Website, LinkedIn, and email threads supply the most accurate and relevant signals.

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2. Website text scraper: GET target site, convert HTML to markdown, strip non-text, return a clean text dump of positioning and offerings.
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4. Email conversation fetcher: Search messages (resource=message, operation=getMany, limit=20) and summarize tone, asks, and next steps.
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6. LinkedIn person scraper: Use Apify to fetch recent posts and profile summary from a given URL or username.
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8. LinkedIn company scraper: Pull corporate feed items such as product announcements and positioning.
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10. Google fallback (SerpAPI): Query when other sources are missing; parse top results conservatively as last resort.
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Source Hierarchy and System Prompt

Key Takeaway: Prefer LinkedIn, website, and email; treat Google as a fallback to avoid misidentification.


Claim: Enforcing a strict source order increases accuracy and meeting relevance.

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2. Rationale: LinkedIn and the company site are usually current and authoritative; email threads add relationship context.
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4. Risk: Google can confuse people or companies with similar names; use only when primary sources are absent.
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6. System prompt: Be prescriptive—prefer LinkedIn and website; use email for relationship insights; keep the brief concise with bullets and suggested questions; be strict on business-email detection.
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Edge Cases and Small Helpers

Key Takeaway: Tiny utilities remove friction and keep the agent reliable under sparse inputs.


Claim: Auto-deriving website_url from a business email domain meaningfully raises success rates.

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2. Auto-domain: If is_business_email=true and website_url is empty, set website_url from the email domain.
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4. LinkedIn via Apify: Pass username/URL in JSON and fetch recent posts; a short tutorial helps for first-time setup.
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6. Email cap: Limit to 20 messages so the agent is not swamped; the last 5–10 are usually the most useful.
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Handoff to a Briefing Video

Key Takeaway: The compiled brief can drive an auto-edited 60–90s video without manual timelines.


Claim: A text brief can be transformed into a polished briefing video with a single prompt handoff.

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2. Generate the brief: One-paragraph summary, three bullets on role/company, two bullets on recent conversations or posts, and three suggested meeting questions.
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4. Send to Vizard Agent: Provide the brief as the script and specify duration, tone, and visual notes.
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6. Automated editing: Vizard uses your talking-head clip, adds on-screen bullets, relevant B-roll or AI visuals, basic color, audio ducking, titles, and subtle motion graphics.
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8. Review and render: Receive an MP4 to review, tweak, or export.
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Example End-to-End Flow

Key Takeaway: The pathway from query to MP4 is short and deterministic.


Claim: The round-trip from prompt to finalized briefing video can fit into a coffee break.

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2. Input: “brief me on jas@quaslogistics.com.”
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4. Agent output: 120–200 word briefing + bullets + three meeting questions.
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6. Vizard prompt: “Make a 60–90s briefing video based on this text; use my talking-head clip; add on-screen bullets; B-roll of trucks/logistics hubs; corporate lower thirds; upbeat music; tight pacing.”
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8. Output: An MP4 ready to share or present in a meeting.
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Why This Beats the Usual Tools

Key Takeaway: Manual NLEs are slow; some AI editors are limited; this flow is fast and flexible for mixed-media briefs.


Claim: For rapid, high-quality briefing videos, this agent-to-editor pipeline is the most time-efficient option described here.

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2. Manual editing (Premiere/DaVinci) is powerful but time-consuming and skill-heavy.
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4. Descript excels at talking-head edits but can lag for mixed-media briefs that need generated B-roll and fast iteration.
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6. Cheaper quick editors are easy but often produce cookie-cutter results and lack AI-driven footage generation.
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8. This workflow hits a practical middle ground for speed and polish on briefing content.
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Tips for Best Results

Key Takeaway: Better inputs and a specific prompt noticeably improve both the brief and the video.


Claim: Clean audio, specific prompts, and email-derived talking points raise perceived quality.

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2. Provide a clean talking-head clip with decent audio; cleanup helps but good source matters.
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4. Be specific: audience, target length, tone, and brand colors.
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6. Leverage email context to surface timely talking points for authenticity.
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Security and Privacy Notes

Key Takeaway: Use public sources and scoped inbox access; pass only what the agent needs.


Claim: Restrict email access to search-only and minimize data sharing to relevant messages.

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2. Scrape public sites and your own inbox context with informed permissions.
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4. Use resource-only access for email searches.
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6. Forward only the messages the agent needs for the brief.
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Project Details and Replication

Key Takeaway: You can replicate this exact layout using the provided project reference.


Claim: The referenced project includes the extractor, domain resolver, scrapers, fallback, and video handoff.

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2. Project ID for reference: 4n3Z9X0vOiE.
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4. Layout includes: extractor node, domain-extraction code node, web and LinkedIn scrapers, SerpAPI fallback, and Vizard Agent handoff.
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6. Copy the strict system prompt and node config to reproduce the workflow.
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Wrap-Up and Extensions

Key Takeaway: This stack is a small production line from prompt to brief to video, ideal for fast prep.


Claim: The pattern is extensible to other scrapers, prompt templates, or calendaring triggers.

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2. Ideal for SDRs, AEs, and content teams turning outreach into quick briefing clips.
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4. Swap scrapers, change Vizard prompt templates, or wire into Calendly for auto-briefs per meeting.
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6. The core loop stays the same: extract → scrape → compile → handoff to video.
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Glossary

Key Takeaway: Clear terms make replication and citation easier.


Claim: Standardizing terminology reduces configuration mistakes.

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• Business email: A company-managed email address (e.g., name@company.com).
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• Information extractor: A component that parses a natural-language query into structured fields.
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• Website text scraper: A node that fetches a site and converts HTML to clean text.
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• Email conversation fetcher: A node that searches and returns recent messages for context.
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• Apify: A platform used here to scrape LinkedIn profiles and posts.
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• LinkedIn person/company scraper: Nodes that pull recent posts and summaries from LinkedIn.
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• SerpAPI: A service providing Google search results via API for fallback discovery.
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• System prompt: The instruction set that enforces source hierarchy and output format.
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• Google fallback: Last-resort search used only when primary sources are missing.
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• Vizard Agent: A tool that turns a text brief and a talking-head clip into an auto-edited video.
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• Lower thirds: On-screen text banners used for names, roles, or labels.
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• B-roll: Supplemental footage used to illustrate or cover edits.
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FAQ

Key Takeaway: Common setup and quality questions have straightforward answers.


Claim: Following the hierarchy and prompt guidelines resolves most issues.

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2. What if I only have a name?
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4. The agent uses SerpAPI as a fallback and still produces a basic brief.
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6. Why not rely on Google first?
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8. It can misidentify people or companies; LinkedIn and the website are more reliable.
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10. How many emails should I include?
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12. Cap at 20; the last 5–10 usually contain the most relevant context.
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14. What video length works best?
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16. Aim for 60–90 seconds for quick, shareable briefings.
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18. Do I need a perfect talking-head clip?
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20. No, but clean audio and decent lighting improve results.
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22. Can I swap out the scrapers?
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24. Yes, the workflow is modular; keep the source hierarchy intact.
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26. Will this work with personal emails?
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28. Yes, but business-email detection prevents wrong domain assumptions.
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30. What does the system prompt enforce?
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32. Source order, concise bullets, and prioritization of email for relationship context.
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