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Yao David Logan

Software Engineer fullstack specialized in SaaS, business automation and scalable web/mobile platforms.

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Prospect-Pro AI

AI-powered B2B prospecting: multi-channel sourcing, lead scoring and message personalization.

Software Architect & Fullstack Engineer202660 % more conversions, ~15 h saved per week
Context2026

AI-powered B2B prospecting: multi-channel sourcing, lead scoring and message personalization.

B2B prospecting SaaS that automates sourcing, scoring and outreach. The product ingests company contacts from multiple sources (Pages Jaunes, Pappers, GoAfrica, Google Maps), enriches the data through OpenAI and generates personalized messages that drop directly into client CRMs. Built for agencies and B2B sales teams who want to move from manual sourcing to an automated chain — without losing targeting quality or the legal compliance of the scraping pipeline.

Scope
Software Architect & Fullstack Engineer · Fullstack
Stack
Next.js · Node.js · TypeScript
Signal
60 % more conversions, ~15 h saved per week
ChallengeBefore intervention

Blocked identified.

Manual B2B prospecting in the West African context suffers from scattered contact bases, slow enrichment and generic messages that end up in the trash. Sales teams spent more time building Excel files than talking to qualified leads.

Risk addressedSoftware Architect & Fullstack Engineer · Fullstack
ApproachAfter delivery

Answer built.

Modular ingestion pipeline: each source (Pages Jaunes, Pappers, GoAfrica, Google Maps) is wrapped in a typed connector with rate-limiting and robots.txt compliance. Contacts are normalized into a single schema stored in PostgreSQL. Enrichment layer: OpenAI scores leads against client criteria (size, sector, buying signals) and personalizes outreach messages. Native CRM integration via REST API and idempotent webhooks. UI side: a Next.js dashboard with dynamic filters, a Kanban view of the prospecting pipeline and multi-format export (CSV, Excel, JSON).

Foundation deliveredNext.js · Node.js · TypeScript
Results

Measurable impact

Metrics give a quick read of the case study effects.

Précision IA++84% vs before
92%
Before50%
After92%
Leads qualifiés / mois++325% vs before
850
Before200
After850
ROI commercial++192% vs before
3.5x
Before1.2x
After3.5x
EvolutionPost-launch progression
8504250
S1120
S2340
S3580
S4720
S5850
Architecture

System delivered

A simple read of the functional blocks and their interactions.

clientDashboard Next.js
gatewayAPI Node.js
servicePipeline scraping
serviceService enrichissement
serviceService messages
databasePostgreSQL
databaseRedis (queues)
aiOpenAI
externalSources publiques
externalCRM clients
Dashboard Next.jsAPI Node.jsREST
API Node.jsPipeline scrapingJobs
Pipeline scrapingSources publiquesHTTP rate-limited
Pipeline scrapingPostgreSQLNormalisation
API Node.jsService enrichissementPipeline
Service enrichissementOpenAIScoring
Service enrichissementPostgreSQLUpdate
API Node.jsService messagesCompose
Service messagesCRM clientsWebhooks
API Node.jsRedis (queues)Queues
Précision scoring92%
Vitesse pipeline88%
Couverture sources85%
Personnalisation90%
Adoption client82%
Main signal60 % more conversions, ~15 h saved per week

60 % conversion rate increase observed and ~15 h/week saved on sales teams.

Deliverables
  • Automated sourcing on 4 public channels (Pages Jaunes, Pappers, GoAfrica, Google Maps)
  • AI lead scoring based on client business criteria
  • Automatic contact message personalization (templates + OpenAI)
  • Direct CRM integration via REST API and webhooks
  • Multi-format export and complete pipeline history
Stack
Next.jsNode.jsTypeScriptPythonPostgreSQLOpenAIPuppeteerRedis
Démo publiqueDiscuss a similar need
Collaboration

Let's turn a fuzzy need into a shippable product.

Product diagnosis, SaaS architecture, backend, interface and automations that make a platform usable.

Format
Full-time, freelance, long mission
Focus
SaaS, API, back-office, automation
Discuss a projectDownload CV