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Pierre DeramelPD

Pierre Deramel

Expert AI Automation | n8n Make Agents LLM

€300/day
Paris, FR
8-15 years

Average response time: 1 hour

Freelancer profile translated to English.
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About Pierre

I deploy AI agents and automation workflows that make artificial intelligence work for your teams on repetitive tasks.


FOR WHOM:scale-ups, tech SMEs, ops teams from 5 to 200 people losing 10+ hours per week on manual processes (lead qualification, reporting, customer support, monitoring, data entry).

WHAT I DELIVER:
> Autonomous business AI Agent (lead gen, support, monitoring) with memory and decision logic
> End-to-end n8n / Make workflows with error handling, monitoring, and documentation
> RAG pipelines to query your internal documents with source citations
> Scraping and structured extraction with LLM parsing

MY CONCRETE RESULTS:
> Automation of lead qualification: -8 hours/week for a sales team of 4
> Internal RAG Chatbot: 70% of support questions resolved without human intervention
> Scraping + enrichment pipeline: 3000 prospects processed in 2 hours instead of 3 days

**MY PROCESS**: ROI Audit (D1) > Schema + validation (D2-3) > Iterative Build (W1-3) > Documented delivery + team training + 30-day support


Engineer by training. 4 languages (FR/EN/ES/AR). Response within 2 hours on weekdays.
  • French

    Native or bilingual

  • English

    Native or bilingual

  • Italian

    Native or bilingual

Can work on-site
Paris (up to 50km)

Experience

  • deramel pierre
    Expert AI Automation | n8n Make LangChain Agents | +40% team productivity
    August 2025 - Today (11 months)
    I deploy AI agents and automation workflows that make artificial intelligence work for your teams on repetitive tasks.

    FOR WHOM: scale-ups, tech SMEs, ops teams from 5 to 200 people losing 10+ hours per week on manual processes (lead qualification, reporting, customer support, monitoring, data entry).

    WHAT I DELIVER:
    > Autonomous business AI Agent (lead gen, support, monitoring) with memory and decision logic
    > End-to-end n8n / Make workflows with error handling, monitoring, and documentation
    > RAG pipelines to query your internal documents with source citations
    > Scraping and structured extraction with LLM parsing

    MY CONCRETE RESULTS:
    > Automation of lead qualification: -8 hours/week for a sales team of 4
    > Internal RAG Chatbot: 70% of support questions resolved without human intervention
    > Scraping + enrichment pipeline: 3000 prospects processed in 2 hours instead of 3 days

    MY PROCESS : ROI Audit (D1) > Schema + validation (D2-3) > Iterative Build (W1-3) > Documented delivery + team training + 30-day support

    Engineer by training. 4 languages (FR/EN/ES/AR). Response within 2 hours on weekdays.
  • Calfrontai
    AI Architect
    January 2025 - June 2025 (5 months)
    Context: CallFrontAI was facing rapid growth with teams spending most of their time on manual and repetitive tasks across all departments. My mission: design and deploy an AI automation architecture covering the entire organization.

    Scope of the mission (6 departments):

    Sales / CRM
    Implementation of an AI agent for qualifying and scoring incoming leads, automatic synchronization with the CRM, personalized follow-up sequences by LLM based on prospect profile.

    Customer Support
    Deployment of a RAG system on the internal knowledge base: autonomous resolution of level 1 and 2 tickets, intelligent escalation to human agents for complex cases.

    Marketing
    Automation of competitive intelligence, generation of content briefs, and multichannel publication via n8n workflows connected to existing tools.

    Finance / Reporting
    Multi-source data consolidation pipeline with automatic generation of weekly reports and real-time alerts on critical KPIs.

    HR / Recruitment
    Automated CV sorting by AI agent, personalized responses to candidates, interview scheduling, and documentary onboarding without manual intervention.

    Operations / Logistics
    Orchestration of inter-departmental flows, elimination of duplicate data entry between tools, workflow monitoring with anomaly alerts.

    Deployed Stack:
    n8n . Make . LangChain / LangGraph . OpenAI API . Claude API . Python . Zapier

    Result:
    50 to 100 hours of manual work eliminated per week across the organization, allowing teams to focus on high-value-added tasks.
    AI Automation Langchain Make n8n Claude Code

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Education

  • AI Engineer
    ibm
    2025
  • Big Data Engineer
    supinfo
    2021

Certifications

  • Automation to AI Agents: Advanced
    Make
    2026

Skill set

Categories