AI agents built for your business

We audit what AI can really do for you, build systems for your exact workflows, and make your engineering team agent-native.

Trusted by

Startups, scale-ups, VC and PE funds, governmental institutions, and Big 4 consulting firms

What we do

Three ways to work with us

Custom AI systems in production, AI developer experience for your team, and AI audit & due diligence, each with a fixed-price entry offer.

01Build

Custom AI Systems

We build AI systems for your exact workflows: workflow automation, multi-agent systems, and assistants that answer from your own data.

Use case

Multi-agent system where one agent retrieves data, another runs the calculations, and a third checks the result against your rules.

Start with the AI Roadmap

02Enable

AI Developer Experience

We make your engineering team agent-native: the harness coding agents need to perform, shared skills, and workshops on your own codebase.

Use case

Agent orchestration workshops on a team’s own codebase, with skills and workflows the team keeps and extends.

Start with the DevEx Baseline

03Audit

AI Audit & Due Diligence

We assess your systems, data, and team to establish what AI can and cannot improve in your operations. Useful before you build, and for investors before they invest.

Use case

Technical due diligence for a private equity firm: code, architecture, scalability, and the real risks, summarized for an investment decision.

Start with the DD offer

Success stories

Blackfin

We run Blackfin’s technical due diligence on fintech targets, each deal inside a short window. We review the data rooms, the architecture, and the code, test each AI thesis, and run deep-dive sessions with the CTOs. The committee gets a clear technical read in time for every decision.

Pigment

Enterprise teams were running complex, multi-step data workflows by hand. We built a multi-agent system where specialized agents coordinate retrieval, calculation, and validation across each step, turning that work into one reliable, automated workflow at scale.

Airsaas

Turning free-form input into structured project data was slow and manual. We built a multi-agent architecture where planning and execution agents work together to convert natural language into structured, validated outputs, so complex generation workflows run end to end with little manual work.

Weglot

Automated translation had to stay accurate on technical terms without hurting search. We built a RAG system with custom embeddings and several verification layers, so translations hold their domain accuracy while meeting SEO requirements at scale.

MerciApp

Generic grammar checking couldn’t cover the product’s needs. We built custom NLP models and integrated them into the live product, delivering real-time, context-aware writing suggestions that go well beyond basic grammar.

VC Firm

Sales data entry into the CRM was manual and easy to skip. We built a natural-language interface connected to the CRM through Telegram, so the team updates records from where they already work and structured data lands automatically, with no new tool to learn.

The team

Builders who ship AI in production

We met at a research lab at EPITA, where we built our foundations in AI and software engineering. We then spent years shipping AI in production: Robin as Founder and CTO at Spimed-AI, then Lead AI Engineer at Pigment, and Augustin as a startup founder and AI Software Engineer at Yoobic. We started Moqa Studio in Paris at the end of 2023.

We think the divide is no longer between companies that use AI and those that do not. It is between the ones who build it into how they actually work and the ones who bolt on generic tools. Off-the-shelf assistants give quick wins, then stall. The lasting value comes from systems shaped around your data, your workflows, and your rules. That is what we build.