Natively Profit Scout is a prospect-intelligence and sales-prep cockpit built with Native.Builder for ethical public business research, workflow discovery, stakeholder mapping, pain-point analysis, opportunity scoring, and CRM-ready outreach planning.
The app is designed to help analyze a target business before outreach, identify where that business may have operational friction, and prepare a more relevant consultative sales path using the Natively Profit Builder model:
RIDE → SELECT → PRICE
This project is being built as a reusable sales enablement tool and Native.Builder showcase.
Natively Profit Scout helps a user prepare for business outreach by turning public company information, user-provided notes, pasted research, and customer-disclosed information into a structured sales intelligence brief.
The goal is not to create a generic scraper or surveillance tool.
The goal is to create a safe, repeatable prospect-intelligence workflow that helps answer:
- What does this company do?
- Who likely works there by department or role?
- What tools, workflows, and business functions are visible?
- Where might they be losing time, money, or operational efficiency?
- Who is likely the right person to speak with?
- What pain points should be validated in discovery?
- What Native.Builder/Natively solution could create fast business value?
- What demo, proof, or sales path should be prepared before outreach?
- What should be transferred into a CRM?
Natively Profit Scout is built around the RIDE → SELECT → PRICE flow.
The prospect should experience value before the pricing conversation.
The app helps identify:
- The best live proof or demo to show
- The business pain the demo should address
- The source signal that supports the demo angle
- The first 90-second demo script
- Discovery questions to ask during the demo
- What to listen for during the conversation
The prospect should be guided toward the right solution path.
The app helps identify:
- Best-fit department
- Best-fit use case
- Likely stakeholder alignment
- Required features
- Nice-to-have features
- Expansion path
- Decision criteria
The value story should become a clear next step.
The app helps prepare:
- Cost-of-inaction framing
- Package recommendation
- Budget-owner assumptions
- Close questions
- Objection-handling prompts
- CRM-ready follow-up notes
Natively Profit Scout is structured around these major feature areas:
The dashboard provides a high-level view of analyzed companies and prospect status.
Planned and current dashboard concepts include:
- Total companies analyzed
- Hot, warm, and cold prospect groupings
- Fit score
- Pain score
- Urgency score
- Recent analyses
- Recommended next action
- Top target cards
The company analysis flow collects structured information about a prospect.
Inputs may include:
- Company name
- Website
- Industry
- Location
- Estimated company size
- Products and services
- Target customers
- Sales model
- Delivery model
- Support model
- Operations model
- Known people or departments
- Known tools and systems
- Workload friction
- Sales context
- Notes from calls, websites, LinkedIn, Discord, email, CRM, or manual research
The company profile page organizes the business context into a usable sales-prep brief.
Profile sections may include:
- Company summary
- Business model
- Customer segments
- Operational maturity
- Digital maturity
- AI readiness
- Budget likelihood
- Sales difficulty
- Public intel summary
- Best first conversation angle
- Missing information to validate
The Pain Point Map helps identify business friction and score opportunity strength.
Pain-point scoring dimensions:
Pain Severity
Frequency
Revenue Impact
Ease of Solution
Decision-Maker Visibility
Each dimension uses a simple 1–5 scale:
1 = Low
2 = Mild
3 = Moderate
4 = High
5 = Critical
The app uses these scores to calculate opportunity strength and help prioritize which problems are worth discussing first.
Pain points may come from:
- User-entered intake data
- Public-intel signals
- Auto-fill recon findings
- Manual edits
- Workflow inference
- Tool detection
- Discovery notes
The Stakeholder Map helps identify who matters in a sales conversation.
Stakeholder categories include:
- Economic buyer
- Technical buyer
- Daily user
- Champion
- Blocker
- Influencer
- Executive sponsor
- Procurement/admin
- Unknown but needed
The goal is to identify the role or person the user most likely needs to speak with in order to earn the business.
The app should prioritize professional role analysis, not personal profiling.
The Tool & Workflow Map helps organize visible and suspected business systems.
It may include:
- Known tools
- Suspected tools
- Detected public tools
- Inferred workflows
- Manual tasks
- Bottlenecks
- Duplicate data entry
- Missing integrations
- Natively ideas
- Native.Builder demo opportunities
Tool and workflow analysis may include public signals from:
- Website scripts
- Visible links
- Forms
- Booking pages
- Careers pages
- Support pages
- Help centers
- Pricing pages
- Public product/service pages
- User-pasted notes
The Public Intel layer helps analyze public business context.
Supported source types include:
- Public website text
- Public About page text
- Public Services page text
- Public Careers page text
- Public Contact page text
- Public Blog or News page text
- Public Pricing page text
- Public case study text
- Manually pasted LinkedIn company notes
- User-provided research notes
- Customer-disclosed information
Public Intel is designed to extract:
- Growth signals
- Hiring signals
- Tool signals
- Workflow signals
- Sales motion signals
- Support burden signals
- Operational friction signals
- Compliance/security signals
- Market-positioning signals
- Outreach openings
- Discovery questions
- Native.Builder demo ideas
The Auto-Fill Public Recon layer is intended to reduce manual entry by analyzing safe public signals and suggesting fields to apply to a company profile.
This feature is designed to help auto-fill or suggest:
- Company description
- Industry
- Products/services
- Target customers
- Departments
- Suspected tools
- Detected tools
- Inferred workflows
- Pain points
- Opportunities
- Best first conversation angle
- Suggested stakeholder roles
- CRM notes
- Outreach openings
Auto-fill suggestions should never silently overwrite user-entered data.
The app should show:
- Suggested value
- Source
- Evidence
- Confidence level
- Apply button
- Reject button
- Edit-before-applying option
The Opportunity Engine converts pain points, recon findings, workflow signals, and public-intel data into possible business opportunities.
Opportunity cards may include:
- Opportunity title
- Business problem
- Who feels the pain
- Who pays for the fix
- Proposed solution
- Native.Builder build idea
- Required features
- Estimated complexity
- Estimated business value
- Suggested demo angle
- Suggested first build prompt
- Discovery questions
- Proof needed
- Close strategy
Opportunity types may include:
- Internal dashboard
- CRM cleanup
- Lead routing
- Sales follow-up automation
- Customer support assistant
- Intake form and routing workflow
- Proposal generator
- Booking/scheduling flow
- Knowledge base assistant
- Compliance checklist assistant
- Security triage dashboard
- Inventory/work order tracker
- Content engine
- Community onboarding system
- Employee training tool
- Client portal
- Executive reporting dashboard
The Profit Builder Plan turns the analysis into a sales path.
It includes:
- RIDE plan
- SELECT plan
- PRICE plan
- Recon-based RIDE demo
- First 90-second demo script
- Discovery questions
- Objection-handling notes
- Native.Builder demo prompt
- Suggested next step
The purpose of this page is to turn research into action.
The CRM Export page prepares sales-ready output.
Export fields may include:
- Company name
- Website
- Industry
- Location
- Company size
- Primary contact
- Decision maker
- Champion
- Pain points
- Public signals
- Detected tools
- Inferred workflows
- Opportunity score
- Recommended product
- Suggested package
- Best outreach opening
- RIDE plan
- SELECT path
- PRICE close plan
- Follow-up date
- CRM tags
- Notes
- Confidence level
Supported export concepts include:
- Copy to clipboard
- JSON export
- CSV export
- Markdown brief
- CRM-ready plain text
Public recon findings should be tagged clearly.
Each finding should be labeled as one of:
Detected
Inferred
User Provided
Assumed
Definitions:
- Detected: directly observed from a public source.
- Inferred: reasoned from visible public signals.
- User Provided: entered or pasted by the user.
- Assumed: a low-confidence working assumption that needs validation.
Each finding should include a confidence level:
High
Medium
Low
Findings should include source context wherever practical.
Natively Profit Scout is intended for ethical public business intelligence and sales preparation only.
Allowed source types include:
- Public company websites
- Public pages supplied by the user
- Public business text pasted by the user
- User-provided notes
- Customer-disclosed information
- Lawful API integrations configured by the user
This project should not include functionality for:
- Unauthorized access
- Credential harvesting
- Private scraping
- Stealth tracking
- Employee stalking
- Login-wall bypassing
- Paywall bypassing
- CAPTCHA bypassing
- Collection of sensitive personal attributes
- Hidden surveillance
- Non-consensual employee monitoring
- Scraping private portals
- Circumventing platform access controls
If a public source cannot be fetched safely, the app should provide a manual paste fallback rather than attempting to bypass access controls.
Natively Profit Scout should not scrape logged-in LinkedIn pages.
Acceptable LinkedIn-related use:
- User manually pastes public LinkedIn company About text.
- User manually pastes public job descriptions.
- User manually pastes public company notes.
- User manually provides names or roles already found through lawful research.
The app may analyze pasted LinkedIn notes for:
- Hiring signals
- Growth signals
- Public role structure
- Leadership priorities
- Likely buyer roles
- Warm outreach themes
The app should clearly label these as:
Manual public LinkedIn notes — no automated LinkedIn scraping.
The first version is frontend-friendly and should work without paid APIs.
Expected stack:
- React
- Vite
- TypeScript
- Browser localStorage for v1 persistence
- Deterministic rule-based analysis where practical
- Modular service layer for future backend or API integrations
Core architecture areas may include:
src/
components/
context/
data/
pages/
services/
types/
utils/
Important service concepts:
analysisEngine
publicIntelEngine
reconScanner
toolFingerprintEngine
workflowInferenceEngine
exportUtilities
The v1 app is expected to use browser localStorage.
That means:
- Data persists in the current browser.
- Data does not automatically sync across devices.
- Clearing browser storage may delete saved analyses.
- Future versions may add Supabase, database persistence, or CRM sync.
Current and expected limitations:
- Browser fetch may fail because of CORS.
- Some websites may block browser-based public fetches.
- Manual paste fallback is required for many pages.
- LinkedIn is manual paste only.
- No private scraping.
- No login scraping.
- No backend enrichment by default.
- No paid enrichment APIs by default.
- No cloud sync unless added later.
- Recon findings should be treated as discovery prompts, not verified truth, until confirmed.
This repository is synced from Native.Builder.
Recommended workflow:
- Build and iterate inside Native.Builder.
- Keep the generated source code synced to GitHub.
- Avoid committing secrets or build artifacts.
- Review source changes before publishing major versions.
- Use small stabilization passes before adding new features.
Recommended local commands after source sync:
npm install
npm run dev
npm run buildIf scripts differ in package.json, use the scripts defined there.
Do not commit:
.envfiles- API keys
- Tokens
- Sponsor codes
- QR codes
- Redemption links
- Private URLs
- Private credentials
node_modules/dist/- Build output
- Private customer data
- Sensitive personal information
Recommended .gitignore entries:
node_modules/
dist/
build/
.env
.env.*
!.env.example
.DS_Store
.vscode/
.idea/
*.logNatively Profit Scout is an active Native.Builder project.
Current build focus:
- Stabilize recon data persistence
- Ensure recon-generated pain points and opportunities persist
- Ensure Tool & Workflow Map, Opportunity Engine, Profit Builder Plan, and CRM Export all read from the same persisted recon data model
- Maintain ethical public-intel boundaries
- Keep the app usable as a repeatable prospect-prep cockpit
Possible future improvements:
- Supabase persistence
- CRM integrations
- Google Sheets export
- Airtable export
- HubSpot export
- Salesforce export
- Backend public-page fetch proxy
- Official enrichment API support
- Workspace/team support
- Report templates
- One-page sales brief PDF
- Demo script generator
- Outreach sequence generator
- Native.Builder demo prompt library
- Role-specific sales playbooks
- Admin settings for scoring weights and offer packages
Natively Profit Scout is a safe public-intelligence and sales-prep tool for finding better business conversations.
It helps the user walk into outreach with more context, better discovery questions, clearer value hypotheses, and a stronger demo plan.
It is designed to support ethical consultative selling, not manipulation, surveillance, or unauthorized data collection.
No open-source license has been selected yet.
Until a license is added, all rights are reserved by the repository owner.