Context engineering for enterprise AI
We build your company's AI intelligence
.
We capture your processes and your knowledge and turn them into AI that reaches production, at a cost you can defend to the board. We start by accelerating your team and, from there, we build the intelligence of your whole company.
Explore Our Services
Focusing on the "why", we combine technical expertise and proven methodology to help you scale your development team.
IT Transformation
We multiply your team's velocity x5-10 without stopping delivery
AI-Accelerated Development
We industrialise software delivery with AI: SDD methodology, our own accelerators and human verification. A 4-8 week quick start, or a full SDLC transformation in 12-16.
AI Officer
We act as your AI Officer for 12-18 months. We redesign processes across functional areas, build your corporate intelligence and hand the function over to you.
onext Enterprise AI
The corporate environment where your AI decides the way your company decides. Processes in production with governed, multi-model AI and cost under control — no vendor lock-in.
Technology
The technical foundation: cloud, diagnosis and turnkey project delivery
Cloud Architectures
Scalable, multi-cloud and serverless infrastructure. From monolith to cloud-native, with cost and security under control.
Tech Audit & Roadmap
A full diagnosis of your stack and team, plus a 90-day plan. The entry door to working with us.
Project Development
We take your software project from idea to production: fixed scope, senior team and turnkey delivery accelerated with AI.
Talent as a Service
Scale your team without months of searching. Supervised by us
Supervised Staff Augmentation
Senior developers embedded in your team. Weekly follow-up and reporting.
We build your MVP
From idea to product in 90 days. An expert team that takes your startup to production.
Dedicated Product Team
Continuous development for existing products. Scale without hiring.
We Want to Be Part of Your Success
We help technical teams dream bigger, move faster, and build extraordinary products. It all starts with a conversation.
They work with us
Success Stories
Real transformations with measurable results. This is how we help technical teams reach their full potential.
From idea to production MVP: Plannet connects communities through events
A complete social platform to create, discover and manage local events and plans
The challenge
Plannet needed to turn its vision into a working mobile app that would let users create, discover and manage social events with ease. As a startup, they required a fast MVP with a dynamic feed, user profiles, event management and a modern cloud architecture to validate their business model and connect local communities.
Tech stack
From constantly missing deadlines to 1 weekly release: transformation with AI and agile methodologies
AI Center of Excellence for a technology product team
The challenge
The team worked without direction: with no clear objectives or defined deadlines, each developer did whatever they considered urgent. The result: constantly broken promises, zero predictability and credibility eroded sprint after sprint. Without agile methodologies or automation, every release was unpredictable. The CTO needed to align the team around objectives, professionalise processes and rebuild the business's trust.
Tech stack
"I had lost the business's trust by promising and not delivering. In 2 months we went from total chaos to a predictable weekly release. Now the team is focused, works with clear objectives and I have rebuilt my credibility."
Hacktua: a wellbeing app with 380+ pieces of content and a personalized profiling algorithm
End-to-end development of an iOS/Android mobile app with CMS, learning paths, recipes and cross-platform subscriptions
The challenge
Hacktua had a clear vision: a wellbeing platform that would help its users build sustainable habits through educational content and personalization. But taking it to production required building from scratch a cross-platform mobile app, a CMS to manage 80+ hacks and 300+ recipes, a secure subscription system on iOS and Android, and an algorithm capable of adapting recommendations to each user's goals. All without compromising performance, security or the experience that justifies a premium subscription.
Tech stack
Latest Insights
Analysis, learnings and perspectives on IT transformation, artificial intelligence and modern architectures.
Prompt injection: your agent's security lives in the permissions, not in the prompt
Inside a language model there is no separate channel for instructions and another for data, so prompt injection cannot be patched — it has to be bounded. This piece moves the defence from where it does not work — the system prompt and filters — to where it does: the tool permission table. Willison's lethal trifecta and Meta's rule of two as design criteria; the nine fields of a tool record; the six patterns with provable resistance and what they cost in utility; and the anatomy of two real incidents, EchoLeak in Microsoft 365 Copilot and the toxic agent flow through GitHub's MCP server. With OWASP LLM01:2025, the UK NCSC warning, "The Attacker Moves Second", CaMeL, AgentDojo, InjecAgent and spotlighting.
The golden set: how to build the cases that decide whether your AI works
"Show me your golden set" organises a conversation about AI in production faster than any other question. This piece builds the object the rubric and the baseline take for granted: where the cases come from (traffic, incidents and risk hypotheses), why you stratify by capability rather than volume, how many cases you actually need, why your labelling error rate is the ceiling for everything else, and why a held-out set is a consumable resource with a looking budget. With CheckList, Northcutt's work on label errors, Aroyo and Welty's crowd truth, the reusable holdout from Dwork and colleagues in Science, GSM1k and the re-annotation of SWE-bench Verified.
Evaluating AI agents: what "works better" actually means
"It works much better" carries no information: missing are what against, measured how, and with how much confidence. This piece builds the two missing objects. The rubric: how binary, verifiable criteria are derived from the golden set, with blocking criteria, trajectory evaluation and the null agent test. The baseline: the five mandatory reference points, including the ablation almost nobody runs. And the minimum statistics needed to know whether a three-point difference is real. With τ-bench and pass^k, "AI Agents That Matter", HealthBench, Agent-as-a-Judge, Anthropic's work on error bars and METR's trials.