AI transformation & delivery

Ship AI-first product. Transform how your team delivers—without gambling on the wrong stack or hiring too soon.

Veridian combines advisory and hands-on leadership: MVP and platform delivery, AI tooling embedded in real workflows, and CTO-level judgment when stakes are high. Built from 18 years in the seat—banking, venture-backed product, and global teams.

What Veridian delivers

A clear menu of outcomes—not a vague “CTO” label.

Pick the problems that match your stage. Engagements are scoped in conversation; investment is agreed case by case—no public rate card.

MVP & product build

From zero to production: architecture, core features, and a release path your team can own—when you need velocity without a permanent executive hire.

AI systems for team productivity

Agent-assisted development, review and CI patterns, and guardrails so engineers ship faster without sacrificing quality or security.

Product & platform strategy

Roadmaps tied to runway and revenue, build-vs-buy, and technical trade-offs explained for non-technical stakeholders.

Go-to-market & launch readiness

Release discipline, observability, and operational readiness so launches survive real users—not just demo day.

AI & data stack design

Tooling choices and pipelines that fit startup constraints: what to adopt now, what to defer, and how to avoid throwaway AI experiments.


How we can work together

Advisory

Ongoing or project-based

Leadership on architecture, prioritization, and vendor decisions—board-ready clarity with optional hands-on review where it moves the needle.

Delivery sprint

Fixed scope, time-boxed

Concentrated execution on a milestone: migration, critical release, or MVP slice—clear outcomes and a clean handoff.

Embedded leadership

Fractional CTO

Part-time executive presence across roadmap, team, and code—until you are ready to hire full-time or graduate to a stable operating model.

Act I

Understanding where you actually are.

Not where your pitch deck says you are. Where you actually are. What does the codebase look like? Will the architecture hold when you 10x users? Is your team structured to ship, or structured to look busy? These are the questions that change trajectories.

Architecture audit

Evaluating your current stack, infrastructure, and code quality against your 18-month growth trajectory. Enough agency-built codebases have crossed this desk to know exactly what technical debt is cosmetic and what's structural.

Team assessment

Team structure, communication patterns, release cadence, incident response. How the engineering org operates, not just what it produces. Teams from 2 to 18 across 4 time zones have been managed this way.

AI readiness

Most companies overestimate their AI capability and underestimate what AI-first engineering actually requires. An honest assessment of where your team is and what the gap looks like.

Act II

Defining where you need to be.

The most expensive technical decision is the one you make without enough context.

Technical roadmap

Not a feature wishlist. A sequence of engineering investments mapped to business milestones — funding rounds, market launches, scaling triggers. Each milestone with success criteria and fallback options.

AI transformation plan

A concrete plan for moving your engineering team to AI-first development. Which workflows change first. Which tools to adopt. How to measure the productivity gains. Built from implementation experience, not speculation.

Hiring and team design

Engineering roles defined with precision. Interview processes that identify the right candidates. Remote-first protocols. Onboarding systems. Teams built across ASEAN, hiring costs reduced 70% by transitioning from outsourced shops to lean in-house teams.

Act III

Building the bridge.

Some engagements stay strategic. Others require opening the IDE and shipping production code. Complete products have been built from empty repositories to paying users. Platform rebuilds led while the existing product kept serving customers. The line between "advisor" and "builder" is artificial.

Fan engagement platform

4 months, zero to production

Full architecture design. AI-powered data analysis pipeline. Ranking algorithms. Crawling infrastructure. Then transitioned to ongoing CTO role as the company grew and hired.

Go, React, PostgreSQL, Azure

Enterprise QC platform

Rebuild under load

12-database consolidation to multi-tenancy. Complete backend rebuild while serving active clients. AI-powered supply chain disruption intelligence system built simultaneously.

NestJS, TypeScript, PostgreSQL, Azure

Epilogue
18+

Years in Software

6

Companies Scaled

18

Largest Team Led

€1M+

Project Budgets


"Understands the whole system. Provides guidance on improving in all aspects. Planning, prioritizing, executing. Strongly recommended."
Raphael Ng, Engineering Director
"Instrumental to the company's success. Multiple multi-million USD funding rounds. Constant dedication. Strongest work ethic."
Adrienne Beaumont, CEO

18 years across investment banking, e-commerce, startup MVPs. Paris to Hong Kong. CTO, VP Engineering, co-founder, lead developer. Master's in Software Engineering, Telecom Nancy. English and French. Focus: early-stage and SME teams shipping product and adopting AI-first engineering.

Go React TypeScript NestJS PostgreSQL AWS Azure Terraform

Every story starts with a conversation.

30 minutes. No slides. Just you describing what's hard, and an honest assessment of whether this practice can help.