Standard Operating Procedure (SOP)
Founder Discovery, Enrichment & Deal Conversion Funnel

The system automatically ingests corporate entity registrations filed in India over a trailing 20-year window. Vendors must deliver automated software scrapers or integrated commercial data pipelines to process this criteria without manual intervention.

1.1 Automated Registry Integration

Develop automated extraction modules running daily at 01:00 AM IST to query official Ministry of Corporate Affairs (MCA) registries, data partners, or structural web proxies (e.g., Probe42, ZaubaCorp, or equivalent developer portals). Raw payloads must dump into a secure tracking system.

1.2 Strict 20-Year Horizon Filter

The data fetch array must use a dynamic temporal offset to isolate records where the incorporation age falls strictly within a 20-year boundary. Older corporate records must be dropped immediately from processing loops.

1.3 Corporate Class & Legal Structure Filtering

The string checker must filter records based on company suffix designations. The pipeline targets exclusively Private Limited (Pvt. Ltd.) entities. All LLPs, Sole Proprietorships, Public Limited Companies (Ltd.), One Person Companies (OPCs), and Section 8 NGOs must be filtered out.

1.4 Active Operational Status Validation Check

Before confirming entry, the module checks the current status of the target company. The registry value must read exactly "Active" or "Live". Companies flagged as Dormant, Struck Off, Dissolved, or Under Liquidation must be permanently purged.

1.5 Structuring Unified Target Payloads

Cleaned corporate instances are saved directly into the pipeline workflow, tracking the following unified parameters: Corporate Identification Number (CIN), Cleaned Legal Company Name, Date of Incorporation, Registered Office Address components, and a JSON Array containing Signatory Director Names and Director Identification Numbers (DIN).

The vendor must supply a automated scraping layer (utilizing custom Puppeteer instances or platforms like PhantomBuster/Clay) to look up the company names and director profiles extracted in Step 1 on LinkedIn.

2.1 Corporate Page Mapping

The module matches the scraped company title against LinkedIn's organization index to locate official corporate pages, extracting employee headcounts and industry classifications.

2.2 Executive Persona Search

Queries are structurally built combining the company name with the target director's name to isolate individual profile candidates.

2.3 Confidence Scoring Logic

The system computes an identity match score based on headline keywords (e.g., matching strings like "Founder", "CEO", "Managing Director", "Co-Founder"). Profiles that score at or above an 85% match threshold are locked in.

2.4 Experience & Asset Extraction

Once validated, the scraping system parses and stores the individual's profile URL, headline text, historical employment durations, and academic institution records.

Using the verified company and founder profiles, the system queries a vendor-supplied data waterfall (e.g., integrating tools like Apollo, Lusha, or custom web extraction bots) to find valid contact details.

3.1 Mobile Telephone Normalization

Extract active direct mobile numbers and format them cleanly to the international E.164 notation standard (e.g., +91XXXXXXXXXX), removing all brackets, dashes, or local padding zeroes.

3.2 Corporate Email Identification

Locate verified professional business email addresses linked to the specific corporate domain name or the individual executive.

3.3 Real-Time Handshake Verification

Run real-time SMTP handshake checks and MX record verification to confirm each email inbox exists before writing it to the CRM pipeline, keeping the system bounce rate strictly below 1%.

Vendors must build and host an automated conversational voice AI instance (using tools like Retell AI, Bland.ai, or Vapi) linked directly to the data enrichment engine via API webhooks.

4.1 Low-Latency Audio Stream

The voice node must operate with sub-second latency, handling natural conversation pacing, interruptions, and realistic verbal acknowledgments.

4.2 Core Track Record Delivery

The conversational script must clearly state StartupLanes' key ecosystem track records: 136 Portfolio Investments executed, $111 Million USD total capital invested, and 6 corporate IPOs successfully completed.

4.3 Dynamic Academic Affinity Injection

The AI agent reads the extracted academic data fields before making a call. If a target university matches, it dynamically injects a personalized affinity statement into the script:

4.4 Call-to-Action Validation

The flow guides the founder toward a strong call-to-action, motivating them to apply for funding on startuplanes.in and automatically triggering an instant mobile text update once the call finishes successfully.

As soon as a call completes successfully, the system reads the company's regional location coordinates and cross-references them with StartupLanes' localized chapter index.

5.1 Address & Pincode Parsing

The pipeline processes the text address strings to extract state codes and metropolitan zones for regional classification.

5.2 WhatsApp Business API Trigger

An automated API dispatch sends an instant WhatsApp template message welcoming the founder to the ecosystem.

5.3 Geo-Targeted Routing Array

The platform distributes founders to specific community links based on location:

• Delhi/NCR Registrations: Automatically routes to the Delhi NCR Executive Chapter WhatsApp Node.
• Mumbai Regions: Automatically routes to the Mumbai Syndicate Regional Group Asset Link.
• Goa Registrations: Automatically routes directly to the Goa Headquarters Anchor WhatsApp Hub.

Vendors must implement an automated multi-channel messaging schedule across Email, WhatsApp, and interactive systems to secure the founder's attendance at the upcoming local chapter session.

6.1 Sequential Reminder Intervals

The drip engine spaces alerts systematically, pushing confirmations at T-minus 72 hours, 48 hours, and a final morning confirmation text.

6.2 Parameter Enforcement

All communication elements must reinforce these strict corporate scheduling rules: Meetings take place on Saturdays, running strictly from 11:00 AM sharp to 3:00 PM (4-Hour Block).

6.3 Ecosystem Category Exclusivity Rule

The system checks industry registration tags before approval. Only one corporate executive is permitted per distinct industry classification code inside a single chapter to prevent internal competition.

Vendors are required to supply a turnkey cloud call center software application along with operational human agent fulfillment solutions to screen incoming applications.

7.1 Interactive Agent Script Workspace

Human call-center agents are presented with a dynamic form panel within the CRM layout detailing the applicant's company history.

7.2 Corporate Integrity Cross-Examination

Agents interview the founder to verify registration active status, shareholding structures, and clean legal standing.

7.3 Financial Metric Tracking

Staff explicitly capture and verify available cash runway months, current monthly run rate (MRR), and targeted round sizes.

7.4 Star Score Classification

The operator completes the review by assigning an internal qualification score from 1 to 5 stars within the console, mapping founder coachability and deal readiness.

When a human call center agent checks the triage approval box, the platform must automatically trigger background routing rules to log the next review meeting.

8.1 Calendar Integration Lookups

The software connects via API to read the live availability schedules of StartupLanes' corporate Investment Banking Sector Heads.

8.2 Automatic Invite Generation

The module claims an open slot, books a 30-minute meeting session, and automatically writes unique Google Meet or Zoom video links to the invite.

8.3 Payload Association

The booking application appends the user's pitch deck files, LinkedIn summary links, and agent notes directly into the calendar event description field.

During the video pitch session, the corporate Investment Banking division reviews the business's metrics, verifying unit economics, scalability potential, addressable market size, and valuation models.

9.1 Unit Economic Validation

Analysts audit the margins, customer acquisition costs (CAC), lifetime value (LTV), and cashburn pacing patterns.

9.2 Valuation Structure Modeling

The team evaluates the startup's financial projections against industry standard valuation multiples to structure compliant deal parameters.

9.3 Allocation Path Routing

Approved deals are directed toward formal fundraising placement options. Depending on traction, they are packaged into the SL Angel Network platform for immediate syndicate distribution or routed for direct venture capital matching and SME IPO preparation under the management of SL Enterpreneurs Pvt. Ltd.

The CRM pipeline needs automated stage management rules to keep deal workflows moving efficiently, tracking missing pitch deck pages, legal mandates, and due diligence reviews.

10.1 Dynamic Status Alerting

The engine tracks process milestones and fires transactional alerts if item requests (like outstanding financial statements) remain unfulfilled.

10.2 Idle Pipeline Interventions

If a warm lead stalls out or becomes unresponsive for 14 calendar days, the system triggers a high-priority intervention alert.

10.3 Regional Escalation Routing

This automatically assigns a reminder task to the local regional Chapter Director, prompting them to reconnect with the founder in person at the next local Saturday networking session.

Since data is not pre-existent within the organization, developers must implement web hooks and API middleware layers connecting directly with licensed Ministry of Corporate Affairs (MCA) data partners, scrapers, or third-party corporate data providers (e.g., Probe42, ZaubaCorp, Instahyre Corporate Database API).

The connector script must run daily cron jobs at 01:00 AM IST to index freshly updated corporate ledger rows across all Indian states and Union Territories, loading raw payloads into an unverified ingestion landing table (`staging_roc_raw`).

The system must filter out older enterprises and pull fresh data within a 20-year window. The logic engine calculates a dynamic cutoff timestamp based on the execution date:

WHERE date_of_incorporation >= DATE_SUB(CURDATE(), INTERVAL 20 YEAR)

Any record with an incorporation date older than 20 years or a timestamp mismatch error must be permanently pruned from the operational workflow to focus resources purely on contemporary operational setups.

The extraction script must process company name suffix matrices and registration category codes to filter target businesses. The system only keeps entities whose legal structure is classified explicitly as a Private Limited Company.

All Limited Liability Partnerships (LLPs), Sole Proprietorships, One Person Companies (OPCs), Public Limited Companies (Ltd.), NGO foundations, and Section 8 companies must be dynamically stripped from the ingestion logs by the validation string checker.

Before passing a lead to the next stage, the data layer must query the company's operational status from the register. The field values must explicitly read "Active" or "Live".

Any company listed as "Struck Off", "Dormant", "In Process of Liquidation", "Dissolved", or "Amalgamated" must be dropped immediately. This protects the pipeline from dead, inactive, or non-compliant corporate shells.

Once raw registry records pass all filtration rules, the system packages them into a clean, uniform payload. The structured profile row is saved directly to the database layer (`sl_founder_pipeline`) and must include:

• corporate_identification_number (CIN) (Verified 21-character alphanumeric string)
• legal_company_name (Full name cleaned of illegal trailing encoding bytes)
• date_of_incorporation (Standardized YYYY-MM-DD date format)
• registered_office_address (Parsed into street, city, state, pincode structural attributes)
• signatory_director_names (JSON array containing active director identities mapped via DIN values)