Healthcare · Applied AI · Critical systems
We build software for teams tired of fixing the same thing every six months
Argus monitors medical imaging fleets in real time. Minerva is the CRM we use daily to run our own sales operation. And when a team calls us, it's usually because something has been broken longer than they'd like to admit.
Healthcare specialization
Compatible with leading medical imaging manufacturers
Nexure AI products
What we keep in production
Argus and Minerva have been running with our customers — and on us — for months. What we learn keeping them alive is what we apply when we work with your team.
Why Nexure AI
We build and run what we sell
Four things that separate a team that runs product from one that just ships it.
We run what we sell
We keep Argus and Minerva in production. We learned what it takes for a system to hold up 24/7 by being on call on a Saturday.
Healthcare specialization
Biomedical engineer co-founder. Argus was designed next to technical profiles that operate medical imaging equipment every day.
Judgment over hype
If a technical decision doesn't deliver measurable value, we don't take it. We prefer simple when it works.
End-to-end ownership
Design, engineering, deployment, operations. What we ship comes with operational instructions that aren't a PDF forgotten in Notion.
Services · Healthcare-first
Software consulting in healthcare and digital products
Senior product engineering for teams building clinical software, multi-tenant platforms or systems on 24/7 on-call. The same discipline we apply to Argus, applied to your team.
Technologies we master (and choose for impact, not hype)
From uncertainty to execution
We remove the obstacles slowing your software down
Deploys that scare the team. Logs no one looks at. Code no one wants to touch. Cloud costs out of control. LLMs with no traceability. The usual pain points that cost real money when something goes wrong in production.
The Nexure AI standard
Fewer fires. What we ship is what we run ourselves in Argus — every day, holidays included.
Predictable releases
Problem: Every deploy is scary. In clinical, financial or any 24/7 system, a production failure carries real cost — for the business and for the user.
Solution: Pipelines, tests and release engineering designed for environments where you can't break things. The same discipline we run Argus with.
Technical debt with an end date
Problem: Legacy code the team no longer dares to touch. Every new feature takes longer, every migration is dreaded, every incident costs twice.
Solution: Phased refactoring prioritized by risk and value. Migrations that don't block the business. Documentation your team can actually maintain.
Scale that holds
Problem: Works with 100 users, drowns with 1,000. Latency, downtime and infrastructure costs out of control the moment the product moves.
Solution: Architecture for scale: load testing, proactive optimization and real metrics to understand how the system behaves under load.
Technical leadership
Problem: Stack, architecture and hiring decisions made blind or postponed. Non-technical founders with no senior peer to contrast against.
Solution: Fractional CTO with real product-in-production experience. Technical direction with context, not abstract advice.
AI with judgment
Problem: LLMs hallucinating in production, costs running away, zero traceability when an agent makes a decision that matters to the business.
Solution: Validation, fact-checking, observability and cost control from day one. AI treated as a production system, with the same guarantees and metrics.
Operational observability
Problem: Metrics that don't help decide. Dashboards no one looks at. You find out about problems when it's already too late and the cost is already done.
Solution: Observability built for the teams that have to act on the alerts. The same philosophy we apply to Argus for medical imaging.
How we work
Four phases. Each one exists because skipping it cost us money once.
The same discipline we apply to Argus, applied to your team.
Understand the domain
Diagnostic
Sector, team and real constraints before proposing anything. In healthcare, clinical AI or any system in real production, technical decisions are made with domain context — or they don't get made.
Decide with judgment
Architecture and plan
Stack, architecture and milestone roadmap. Realistic estimates and prioritization by impact. If something isn't viable, we say so.
Build with discipline
Execution
Short sprints, code review by default, tests that run in CI. If something merges unreviewed, there's a problem before there's a code problem.
Operate for real
Production
Operational documentation, handover to the client team, post-launch support based on real metrics. We build so it can be operated without dependency on us.
Featured projects
Recent success stories
Ecosystem
Partners & Collaborators
We work with complementary specialists when it adds real value to the project.
Want to collaborate? Let's talk
Tell us what's broken
We work with teams running AI in production, operating medical imaging fleets, or trying to modernise legacy without a full rewrite. If your case looks similar, let's talk.
30 minutes with a senior technical profile — biomedical when applicable.