8 years of experience
On assignmentBackend & AI
- Industry
- LegalTech
Project
Document assistant with sourced answers, team-level permissions and response evaluation.
AI-native Teams · Our selection
Engineers selected to move your product forward.
We select senior engineers in Europe and Latin America for European and US clients. Experienced CTOs and lead developers assess their code, their use of AI and their ability to work as part of a team.
- Code reviewed
- Quality, tests and architecture decisions.
- AI put into practice
- Context, tools and verification of results.
- Collaboration assessed
- Communication, autonomy and accountability.
Experience at the service of your team
Fifteen years in the field. Better judgment for your team.
15+
years of founder experience
Developing products, hiring engineers and leading teams teaches you to recognize the skills that matter on a real project. We put that experience to work for your team: technical judgment, AI proficiency, autonomy and collaboration.
Read our manifestoThe evaluators
Selected by CTOs and lead developers.
France / United States
Veterans of software and AI engineering review code quality, architecture decisions and the ability to deliver as a team. Based in France and the US, some have worked at major Silicon Valley companies.
Jean-David Collard, NODINA’s founder and a PhD in computer science, has worked with these professionals over fifteen years of entrepreneurial projects and client engagements for startups, scale-ups and SMEs in Europe and the United States.
Explore the founder’s backgroundNODINA Select · The method
Rigorous selection, grounded in real work.
Five stages connect technical ability with the demands of your project. Each recommendation draws on observable evidence and identifies areas to explore further.
Verify experience
Discuss past projects, personal contributions and responsibilities actually held.
What matters: explaining what was delivered and the decisions behind it.
Review code and architecture
Examine concrete work: readability, tests, debugging and technical trade-offs.
What matters: explaining decisions and knowing how to evolve a system.
Work with AI
Observe tool use, context preparation and verification of results.
What matters: spotting an incorrect output, testing it and correcting it.
Collaborate with a peer
Review work, discuss a disagreement and share useful context.
What matters: moving the work forward together and owning decisions.
Confirm the fit for the engagement
Match skills with your product, stack, working hours and team composition.
What matters: a clear role, shared expectations and confirmed availability.
What can rule out a candidate
Code the person cannot explain, AI outputs accepted without verification or difficulty acting on a review. Technical strength alone is not enough if the skills and working relationship do not fit the engagement.
NODINA Select · Worked example
Understand why a profile fits your project.
A useful recommendation explains strengths, limitations and conditions for success. Here is how that can translate into an assessment.
NODINA SelectIllustrative example · No real candidate
Backend & AI · Internal document assistant
The engagement involves creating an assistant that cites its sources and respects document access permissions.
Engineering
- Observed evidence
- The code separates retrieval from generation. Tests cover missing documents and denied access.
- Explore further
- Recovery after a processing error still needs to be defined.
AI practice
- Observed evidence
- The person compares responses on representative cases and includes abstention when sources are insufficient.
- Explore further
- Latency budgets and request cost tracking need to be defined.
Collaboration
- Observed evidence
- Decisions are explained to the product team. Review feedback leads to simpler permission handling.
- Explore further
- Plan a review with the person responsible for operations.
Assessment for this engagement
A relevant profile for backend development and AI integration, with architecture support for operations. The final decision depends on your context and the conversation with your team.
Your working environment
Teams that know your tools.
We bring together the skills your stack needs to work directly in your repositories, platforms and delivery processes.
Claude Code
Codex
Claude
ChatGPT
Llama
Cursor
GitHub
LangSmith
Supabase
Vercel
Google Cloud
AWS
Linear
Figma
PostHog
Sentry
Stripe
Team composition depends on your project’s tools. AI use goes hand in hand with reviews, tests and evaluations.
“You have a product to ship. We select engineers who can make decisions, execute and take it through to production.”
AI-native Teams · Getting started
One week to choose. Three to start delivering.
Choose and approve your team in one week. The next two weeks prepare its integration into your project. From three weeks in total, the team is ready to work and produce its first pull requests.
Week 1
Choose and approve your team
From your brief, we define the roles and propose engineers. You meet them and approve the team composition and working arrangement.
Weeks 2 and 3
We prepare onboarding with you
Repository access, development environment, tools and product context: we prepare the work and initial priorities with your team.
At the end of 3 weeks
Your team produces its first pull requests
Engineers work in your codebase and submit their first changes for review, following your priorities and practices.
Three weeks in total from a complete brief: one week for selection and approval, followed by two weeks of preparation. Timeline confirmed based on engineer availability, interview slots and access preparation.
Let’s talk about the team you need.
Share your product, stack and priorities. We help you define the roles and the working arrangement.