Industries
Where the work actually happens.
Common challenges we see most, and where our AI, integration, and security capabilities apply — not a claim of delivered work in any of them. That proof comes from the case studies below, as they're published.
Healthcare
Patient records living across systems that were never designed to share data, strict consent requirements, and AI use cases — like clinical note extraction or triage — where a wrong answer reaches a patient record.
Where we can help
Systems integration that bridges EMR and practice-management platforms without a rip-and-replace; AI applied narrowly to extraction and triage with a human in the loop; data handling designed around consent and access from the start.
Diagnostics
Lab and imaging results that need to move accurately between ordering systems, instruments, and reporting platforms, often under tight turnaround pressure.
Where we can help
Device and protocol integration for lab instruments, data pipeline engineering for results flow, and document intelligence for structured report generation.
Pharmacovigilance
Adverse event data arriving from multiple channels in inconsistent formats, with regulatory timelines that don't tolerate a manual triage backlog.
Where we can help
Document and image intelligence for case intake, natural language understanding for signal triage, and secure handling of regulated safety data by design.
Logistics
Shipment, inventory, and routing data split across carrier systems, warehouse software, and spreadsheets — delays compound the longer they go unnoticed.
Where we can help
Legacy and enterprise bridging between carrier and vendor systems, data pipeline engineering for real-time visibility, and anomaly detection for exceptions that need attention before they become a missed delivery.
Industrial & manufacturing operations
Equipment and sensor data trapped in machines and legacy protocols never built to report anywhere, making predictive maintenance and quality tracking a manual, after-the-fact exercise.
Where we can help
Device and protocol integration to get sensor data out of hardware, machine learning for failure and anomaly detection, and a systems readiness review to map what's actually connected before proposing changes.
Case studies
Proof, by industry.
Healthcare
Two systems in production: a clinical documentation assistant that generates structured notes from clinician conversations, and a remote patient monitoring platform streaming vitals from 25,000+ connected devices.
→ See the case studyIndustrial & Manufacturing
A predictive maintenance platform monitoring 10,000+ devices, analyzing IoT telemetry in real time and alerting operators before equipment fails.
→ See the case studyQuestions
Frequently asked.
Have you worked in our industry before?
Healthcare and industrial/manufacturing are proven below with real case studies. For other industries, a systems readiness review is a low-commitment way to find out whether our approach fits your specific environment, regardless of industry label.
Do you only work in the industries listed here?
No — these are the industries our team has the deepest direct exposure to. If your systems involve regulated or high-consequence data outside this list, the same approach still applies; say so on the scope call.