AI Enablement for SMB
Cloud AI products send your data to someone else's infrastructure. For regulated industries -- healthcare, legal, finance -- that's not a privacy consideration, it's a compliance one. We deploy private AI into your environment: on your hardware, under your control, with no data leaving the building.
What You Get
Most AI projects fail because the use case was chosen based on what's possible, not what's useful. We start by identifying where AI creates measurable workflow value for your specific practice -- and which use cases aren't worth the effort.
Model inference runs on hardware in your environment -- on-premises server, co-location, or a dedicated private cloud instance you control. No data transits third-party AI APIs. Designed from the lab infrastructure we run ourselves.
The right model for the task -- not the largest one that'll fit on the hardware. We match model capability to use-case requirements, configure inference parameters for your workload, and validate output quality before production.
AI grounded in your actual documents -- clinical protocols, firm templates, policy manuals, case files. Not a general-purpose chatbot. Answers sourced from your data, with citations you can verify.
AI wired into the tools your staff actually uses -- not a standalone interface they have to remember to open. Integration with document management, EHR workflows, or internal portals depending on your environment.
Ongoing model availability monitoring, performance tracking, and update management. AI infrastructure maintained like any other production system -- not deployed and abandoned.
How It Works
We interview staff and review workflows to identify where AI creates real value -- time saved, accuracy improved, or capacity freed. Use cases ranked by impact and implementation complexity before any infrastructure decision.
Hardware requirements sized to the selected models and workload. Existing infrastructure assessed for suitability. Procurement guidance provided if new hardware is required.
Model deployment, inference stack configuration, and integration with selected workflows. Staff training on interaction patterns and output validation before go-live.
Monitoring in place, runbooks documented, and ongoing support defined. The system runs as production infrastructure -- with the same operational discipline applied to everything else we manage.
Who This Is For
HIPAA doesn't prohibit AI -- it prohibits sending PHI to vendors who can't demonstrate adequate controls. Private deployment removes that constraint entirely. The model runs in your environment, on your terms.
Contract review, document summarization, precedent research -- these are high-value AI use cases. They're also use cases where sending client files to a third-party API is a bar compliance problem. Private deployment resolves that.
Cloud AI products often aren't an option. Private AI infrastructure usually is. If you've been told your industry can't use AI, the more accurate statement is that your industry can't use most AI products as deployed.
AI engagements start with use-case discovery -- identifying where AI creates measurable value in your specific workflows before any infrastructure decisions are made.