Scaling breaks when execution isn't designed as a system.

I build secure, production-grade growth and execution systems that scale reliably. Designed to give teams leverage — not add complexity.

Post-PMF B2B SaaS Growth systems AI agents in production POC→Production
Sylvain Vandewalle

One job: make execution reliable at scale

Scaling breaks when execution relies on fragile processes. Decisions take weeks. Manual processes don't scale. Critical workflows rely on spreadsheets and goodwill.

I build systems that replace manual tasks with reliable autonomous execution.

01

Growth systems

From manual chaos to complete instrumentation

PROBLEM

You don't know what works. Growth decisions rely on gut feelings. Acquisition is a patchwork of untracked channels. Funnels leak, but nobody knows where.

SYSTEM

Complete event instrumentation, funnels, attribution. Operational dashboards. Data-driven prioritization. Structured experimentation. Measurable acquisition channels (SEO, SEA, outbound).

OUTCOME

You drive growth with reliable metrics, not feelings. Decisions take hours, not weeks.

STACK

HubSpot Mixpanel Rudderstack GTM n8n

EVIDENCE

0→5,000 MAU in 24 months with complete instrumentation

50k+ users tracked with multi-touch attribution

80% onboarding completion after systematic optimization

×5 signups via continuous experimentation

02

Autonomous AI agents

From fragile workflows to augmented execution

PROBLEM

Your critical workflows are fragile: lead qualification, data enrichment, reporting, bid management. It takes time, it breaks, it doesn't scale.

SYSTEM

Autonomous AI agents deployed in production with guardrails, monitoring, logging, secure sandboxing. Multi-agent orchestration for complex workflows. Stack: Dust.tt (primary), n8n, Claude Code for specific needs.

OUTCOME

Revenue-critical tasks run 24/7, reliably, securely, with minimal supervision. You scale execution, not complexity.

STACK

Dust.tt n8n Claude Code Anthropic API OpenAI API

EVIDENCE

~30 agents + orchestrator in production (bid management, 24/7)

'Logistic Anomaly Detection' agent used by end customers (production)

POC→Production in 4-6 weeks with complete guardrails

Critical workflows automated with real-time monitoring

AI agents are not an add-on. They are the secure execution layer of modern growth systems. They give teams leverage, not tech debt.

Systems in production

Not slides. Systems that run, with real users and business-critical decisions.

CONTEXT

Maritime SaaS (Safecube.ai), complete tracking + instrumented funnels

SYSTEM

Event instrumentation, multi-touch attribution, real-time dashboards, continuous experimentation

RESULT

0→5,000 MAU in 24 months. ~800 signups/month → ~300 paying customers. Growth decisions driven by data, not intuition.

CONTEXT

B2B SaaS scale-up, acquisition and activation

SYSTEM

Funnel optimization, tracked channels (SEO/SEA/outbound), systematic A/B experimentation

RESULT

50k+ users in <6 months. 80% onboarding completion (vs 45% before). ×5 signups via measured iterations.

CONTEXT

AI agent in production for end customers (logistics)

SYSTEM

'Logistic Anomaly Detection' agent via GPT API, guardrails, monitoring, decision logging

RESULT

In production, used daily by end customers to detect supply chain anomalies. Not a POC.

CONTEXT

Multi-agent orchestration for bid management

SYSTEM

~30 Dust agents + central orchestrator, 24/7 automated workflows, real-time monitoring

RESULT

Bid decisions automated with minimal supervision. Running for months.

Systems lifecycle

No workshops. No strategy decks. No endless audits. Execution that sticks.

01

Diagnose bottlenecks

I identify where execution breaks and why. Analysis of workflows, missing data, manual processes. Prioritization of critical friction points.

OUTPUT

Clear list of systems to build, by business impact.

02

Design the system

Architecture of instrumentation (events, funnels, dashboards) and/or AI agents (workflows, guardrails, monitoring). No over-engineering. The minimum viable to run reliably.

OUTPUT

Technical spec + implementation plan.

03

Deploy to production

Implementation with guardrails, monitoring, alerts. Testing in real conditions. Adjustments until it runs autonomously. Team training on supervision.

OUTPUT

System in production, documented, monitored.

04

Iterate on what runs

Metrics tracking. Continuous optimization. Scale workflows that prove their value. Abandon what doesn't work. Fast decisions based on data, not opinions.

OUTPUT

Continuous improvement, prioritized backlog, ownership transferred.

This is for you if...

You're a post-PMF B2B SaaS (Seed → Series B)

Your growth or ops break under load

You want reliable, secure systems, not more tools

You have a real problem to solve, not a need for slides

×

You're looking for prompts or a chatbot

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You want strategy without execution

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You don't have an identified scaling problem

×

You want AI hype, not systems that run

Operator, not consultant

I've spent 5+ years building and running growth systems in production. Head of Growth at Sinay.ai (maritime SaaS), managed 2 teams (8 people), scaled from 0 to 5K MAU. Before: Crédit Agricole, e-commerce, freelance. I don't make slides. I build systems that last: complete instrumentation, AI agents in production, automated workflows running for months. Bias for fast execution, data-driven decisions, and pragmatic solutions. I deploy AI agents securely, with guardrails, monitoring, and supervision. If it doesn't run reliably in production, it doesn't exist.

Contact

Caen, France

Availability depends on scope — write to me