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LaunchPulse
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AI & Big Data

LaunchPulse

AI-powered startup evaluation engine giving investors and founders data-driven confidence.

ML
Prediction engine
3+
Scoring dimensions
Startups analyzed
100%
Data-driven decisions

What We Built

LaunchPulse is a comprehensive platform combining modern web technologies with machine learning to help founders and investors make data-driven decisions. It analyzes startup metrics, market signals, and document intelligence to predict success probability and surface risk factors before capital is committed.

Challenge, Solution & Outcome

Challenge

Investors rely on gut instinct and incomplete data when evaluating early-stage startups, leading to high failure rates and missed opportunities in both directions.

Solution

A structured ML evaluation platform that ingests startup data, financial documents, and market signals to produce objective, multi-dimensional risk and opportunity scores.

Outcome

Founders receive actionable feedback on their weaknesses before pitching. Investors build conviction faster with quantified risk profiles replacing anecdotal assessment.

Tech Stack Used

Next.js
TypeScript
Python
PostgreSQL
tRPC
TensorFlow
Docker
AWS

Key Features

Success Prediction Engine

ML models analyze startup metrics across team quality, market size, traction, and financials to generate a calibrated success probability score.

Risk Factor Analysis

Automated risk scanning surfaces red flags across regulatory exposure, market saturation, burn rate, and competitive landscape.

Market Fit Scoring

Proprietary algorithm cross-references product-market indicators against verified industry benchmarks to score fit across verticals.

Document Intelligence

Secure vault with intelligent parsing for pitch decks, financial models, and business plans automatically extracting key metrics.

Growth Potential Mapping

Multi-dimensional growth assessment models project revenue trajectories, customer acquisition curves, and expansion scenarios.

Investor Portal

Role-based access control allows deal leads, analysts, and LPs to collaborate on evaluations with granular permission levels.