AI Engineering for Healthcare and Regulated Industries
We engineer production AI for companies that can't afford to guess: autonomous agents, the infrastructure beneath them, and the compliance around them, built to hold up under real traffic, real data, and real audits.
Proof of concept is not proof of production.
Most AI initiatives don't fail early. They fail three months in, when a promising pilot meets production traffic, live data, and its first compliance review. Our work lives in that gap.
We build the layer that makes intelligent systems dependable: orchestration that survives bad model output, infrastructure that scales without hand-holding, and audit trails that stand up in regulated industries. The model is one probabilistic component inside a deterministic system. Design around that, and you get the leverage of generative AI with the reliability your operations already demand.
That conviction comes from a decade of production engineering in healthcare and biotech, building clinical and genomics systems where a failure is more than an inconvenience. We carry those habits into everything we ship.
The model is one node. The system is everything around it. Select any component to see its job; drag it and watch the rest hold.
Inference workloads fail in ways ordinary services don't: models time out, outputs drift, dependencies stall. We design for this up front, with circuit breakers, fallbacks, autoscaling, and observability deep enough to explain any answer the system gave. Nobody should get paged for something the system could have handled itself.
We've shipped software under HIPAA, SOC 2, and GDPR, and we design for the audit from day one: immutable logs, versioned models with full lineage, access controls that hold up to review. Compliance designed in early is architecture; retrofitted later, it's a rewrite.
GPU bills sink more AI projects than bad models do. We size infrastructure to the workload: quantization and batching where they're safe, spot capacity for training, and dedicated capacity only where latency truly demands it. Costs scale with usage, not ambition.
Independent modules. One production system. Select a capability to trace its place in the whole.
Reasoning, tool use, and memory, engineered so the thousandth run is as reliable as the first.
Compute, deployment, and observability: the plumbing that separates production systems from proofs of concept.
Language models applied to clinical and biological data, deployed the way regulators expect.
The path from commit to production: short, boring, repeatable.
A short technical audit of your stack, your constraints, and your ambition. We tell you plainly what's worth building, and what isn't.
A concrete design before anything ships: components, integration points, failure modes, and running costs. Every artifact is yours to keep.
Senior engineers working in your repositories and your CI. Progress you can watch in commits, not slide decks.
We stay through production hardening: observability, runbooks, and handover, until your team runs the system without us.
A senior engineering team with a decade of clinical pipelines, genomics platforms, and the infrastructure that keeps them alive at 3 AM. When you work with MaximSense, you work with the engineers directly. There is no bench.
The distance between a promising pilot and a dependable product is engineering. That's the part we do. Tell us what you're building.
A reply within one business day, from an engineer, not a sales team.