Healthcare, life sciences and testing services · worldwide
A large-scale AI-secured software lifecycle transformation
Context. A B2B e-commerce programme of hundreds of people, spread across the world, wanted AI in its delivery without opening a security gap.
Mission. Leading the programme's transformation end to end: an AI-secured software development lifecycle, the policies, the tooling, the audit, and the change with every team.
Value. AI inside the pipeline with governance around it: one lifecycle for every team, evidence at every increment delivered.
Banking, insurance and financial services · France
An investment universe application, written by economists, put online safely
Context. A regulated investment advisory firm in France wanted its own view of the investment universe: funds, ETFs, indices, mandates and model strategies, next to the firm's macro view. Its economists wrote the application themselves, with AI coding assistants.
Mission. Taking it from the economists' machines to production on Richelieu: files scanned for malware, vulnerabilities and dependencies reviewed with Tzu, then deployment, monitoring and maintenance with the evidence kept as the work happens. The economists keep writing features.
Value. A working tool online, in the hands of the people who designed it, with the security and the audit trail a regulated adviser has to show. An application they liked was enough. The technical complexity was ours.
Software and SaaS · EU
AI-accelerated delivery, with an engineering manager watching the audit
Context. A fast-growing company wanted custom software built with Claude Code and Codex without losing control of what the assistants produce.
Mission. AI-accelerated delivery by a team of three to five, with an engineering manager from euphile who reads the Tzu maturity mirrors and the continuous audit, and steps in wherever policy and reality part ways.
Value. The speed of an AI-native team with the audit trail of a regulated one: every merge carries its evidence.
Banking, insurance and financial services · EU and Switzerland
Know-your-customer documents that never leave the perimeter
Context. A wealth management client needed to process know-your-customer documents on infrastructure it controls, from the first assessment to production.
Mission. The whole chain: data protection impact assessment, threat analysis, data processing design, solution architecture, DevSecOps, and deterministic, AI-accelerated development on sovereign infrastructure.
Value. Documents processed where the regulator can see them, a delivery chain that leaves evidence at every step, and a system the client runs and audits without us.
Software and SaaS · France
A technical audit of code written with Claude Code and Codex
Context. A startup building AI assistants for consumers across Europe asked for a security audit of its codebase before scaling up.
Mission. A code and architecture audit with Tzu, without penetration testing: policies, dependencies, secrets and data flows, each finding with its evidence attached.
Value. Two security issues that months of assisted development with Claude Code and Codex had not surfaced, fixed before launch, with the evidence ready for investors and customers.
Luxury, retail and education
A concierge service that ships software safely, and faster
Context. A lifestyle concierge company wanted AI in its operations and a development process it could trust with its guests' data.
Mission. Securing the development process, bringing AI into the product and the operations, and raising efficiency on the euphile platform.
Value. Faster delivery on a secured pipeline, and AI that serves guests without exposing who they are.
Software and SaaS · France
Dependencies, intellectual property and drift, watched continuously
Context. A software vendor needed a permanent view of its dependencies, of its exposure on intellectual property, and of the distance between company policy and the system actually running.
Mission. Tzu continuous audit: SCA and SBOM on every dependency, licence and intellectual property checks, and drift detection between written policy and reality, repository by repository.
Value. One place where the board, the customers' auditors and the engineers read the same evidence, updated as the code changes rather than at the annual review.
Industrial and automotive manufacturing, and embedded technologies
One reconciled truth across SAP, Power BI and Excel
Context. A group with several production facilities could not analyse its finances across them: the figures lived in different SAP systems, Power BI models and spreadsheets.
Mission. Financial management reconciliation between facilities: common data models, matching rules, an audit trail for every adjustment, and the dashboards the finance team asked for.
Value. Financial analysis across all facilities on one reconciled dataset, with every discrepancy explained rather than hidden.