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Services
Managed IT, integration, analytics, custom build, and AI — one company in Grand Rapids.
Support, integration, analytics, custom build and AI are usually sold by five different firms who blame each other when something breaks. We do all five, which means the AI feature is designed against the data you really have, fed by integrations we wrote, running on systems we are accountable for — and supported by the people who built them.
Security is the constraint we design around, not a phase at the end.
One contract, one escalation path, no vendor triangle.
United States based, engineering through support.
Practice 01
Managed IT & support
We run the systems your organization runs on — and the ones we sold you.
Monitoring, patching, backup and restore testing, identity and endpoint hardening, network and server administration, and a help desk staffed by people who can read the code underneath. Healthcare taught us to treat downtime and audit trails as the same problem.
Patch, backup and restore verification — restores actually tested, not assumed
Work we’re asked for
- Infrastructure assessment and remediation plan
- Monitoring, alerting and patch management
- Backup, restore rehearsal and disaster-recovery planning
- Identity, access and endpoint hardening (SSO, MFA, least privilege)
- Network, firewall, server and virtualisation administration
- Help desk and end-user support under an agreement
- Mail-flow security, including our own Secure Message gateway
Practice 02
Systems integration
Make the systems you already paid for behave like one system.
EHR, practice management, ERP, billing, directory, imaging, custom line-of-business apps — connected properly, with the interfaces documented and the failure modes understood. Most of what looks like a software problem is two systems disagreeing about the same record.
Interfaces between clinical, financial and operational systems
Work we’re asked for
- Interfaces between clinical, practice-management, ERP and billing systems
- Directory and identity integration, so accounts are provisioned once
- Migrations and platform cutovers planned around a tested rollback
- Replacing brittle exports and manual re-keying with real integrations
- Vendor coordination — we talk to their engineers so you don’t have to
- Interface documentation your own team can support afterwards
Practice 03
Data analytics
Get the numbers out of six systems and into one answer.
Pipelines between line-of-business systems, a modelled warehouse instead of a spreadsheet lineage nobody can audit, and reporting a non-technical owner can read without a translator. This is also the groundwork that decides whether an AI project is feasible at all.
Pipelines and integrations across clinical, ERP, CRM and billing systems
Work we’re asked for
- System integrations between line-of-business applications
- ETL / ELT pipelines with tests and documented lineage
- Warehouse and reporting models on SQL Server or your platform of choice
- Operational dashboards for people who don’t write SQL
- Data-quality auditing, de-duplication and master-data cleanup
- Search and vector indexes that make a corpus usable by AI
Practice 04
Custom software & hardware
When the product you need doesn’t exist, we build it — including the box it runs on.
Applications, appliances and integrations built to fit a workflow rather than the other way round. We ship our own hardware-based products, so a purpose-built appliance is a normal engagement here rather than an exotic one.
Line-of-business applications and workflow tooling
Work we’re asked for
- Line-of-business applications built around an actual workflow
- Purpose-built appliances — hardware, image and management interface
- Embedded and edge deployments for sites with no room for a server rack
- Taking a prototype to something a customer can install, license and update
- Modernising an application that works but can no longer be maintained
- Maintained under agreement afterwards, or handed over with documentation
Practice 05
AI development & integration
Applied AI that ships into production systems.
We build LLM features into software that already has users: retrieval over your own documents, agentic tooling with real permission boundaries, classification and extraction that replaces manual review, and private deployments where the data cannot leave your perimeter. We use the same techniques on our own work, which is how our product roadmap moves as fast as it does.
Retrieval-augmented assistants grounded in your own content, with citations
Work we’re asked for
- Feasibility review: what AI would and would not improve here
- Retrieval-augmented assistants over your documents and records
- Document extraction and classification replacing manual review queues
- Agentic automation with tool-level risk gates and audit trails
- Evaluation harnesses so accuracy is measured, not asserted
- Private / on-premise model deployment and provider-agnostic integration
- AI features built into existing .NET, Blazor and React applications
How we engage
Assess, build, operate
Every engagement opens with a paid assessment, so the recommendation you get is independent of what we would like to sell you — and yours to act on with us or without.
Assess
A short paid discovery: we inventory the systems, read the code and the contracts, and come back with what is worth doing, what it costs, and what we would not touch.
Build
Senior engineers, two-week increments, working software at the end of each one. You see progress running in your own environment rather than described in a status report.
Operate
We keep running it — monitoring, updates and support under an agreement — or hand it over with documentation and training if you would rather own it.
Get in touch
Not sure which practice you need?
Most people arrive with a symptom rather than a category — reports nobody trusts, a server nobody wants to touch, a pile of documents someone reads by hand. Describe the symptom and we’ll tell you what it actually is.
Prefer to talk? The number is in the header, on every page.
