Linkworks builds the HL7 interfaces that connect clinical labs to the systems around them, then layers on AI automation that stays on-premise, governed, and grounded in clean data. Orchard Copia and Mirth Connect underneath; self-hosted local LLMs on top.
Before AI can help, the data has to be clean and it has to move. Four disciplines keep clinical data flowing — from the instrument on the bench to the result in the chart.
Custom HL7 v2.x, ASTM, and FHIR interfaces built and maintained on Mirth Connect — routing, transformation, and LOINC mapping that survive real-world edge cases.
Orchard Copia and Harvest administration plus EMR / PMS / HIE interfaces — bringing disparate systems, analyzers, and reference labs into one working ecosystem.
Custom, branded clinical PDF reports with the interpretation logic encoded in data — so labs own their reporting outright and drop recurring third-party reporting fees.
Ongoing interface monitoring with early issue detection and fast resolution, plus day-to-day lab IT help desk — so problems surface before results do.
The hard part of AI in healthcare isn’t the model — it’s keeping it compliant. Linkworks builds automation that runs on-premise, stays grounded in governed data, and holds up to audit. Self-hosted local LLMs, no external API calls, no PHI leaving your environment.
n8n and HubSpot pipelines that provision, validate, monitor, and escalate — turning multi-week integration projects into same-day, hands-off workflows.
Self-hosted GPU clusters running vLLM, Ollama, and LiteLLM with model routing — quantized multi-GPU across NVIDIA and AMD ROCm. Model-agnostic and air-gapped.
Semantic search (pgvector), natural-language-to-SQL (Vanna.ai), and code analysis fused into one grounded answer — with enforced citation and no fabrication.
PHI/PII redaction, role-based access, audit logging, and human-in-the-loop review — so every automated decision is traceable and stands up to compliance.
Automated alert ingestion, PHI-redaction, error grouping, and branded per-lab digest reporting across large interface networks — replacing manual email triage.
MCP server development to give assistants real tools, plus Twilio + Vapi voice and SMS agents that handle live, real-world workflows end to end.
Working systems, not slideware — most running on the same on-premise, governed AI stack.
An AI copilot for Mirth Connect integration engineers — channel analytics plus local-LLM config review, transformer code review, and root-cause analysis. Multi-source RAG, fully air-gapped.
A managed, multi-tenant platform giving small businesses their own governed AI assistant — per-tenant tokens, monthly quotas, budget caps, and audit logging on self-hosted HA infrastructure.
A role-based AI deliberation platform — Strategist, Sentinel, Skeptic, and Judge, each on a swappable model — with audit trails, threshold interrupts, and human-in-the-loop review.
A retrieval-augmented support assistant that makes a legacy LIS’s manuals, SOPs, and diagrams queryable in plain English — strictly grounded, with citation and no fabrication.
An AI booking and discovery platform for salons — voice- and SMS-based agents on Twilio + Vapi, with image-based “book by look” tagging and calendar scheduling.
Research into natural-language agentic AI — autonomous agents commanded entirely through plain-language strategy, with sub-100 ms real-time decisioning.
Clinical data doesn’t get a second chance. Every change is provable, every deploy reversible — whether it’s an interface or an AI agent.
Read the real message flow before touching anything. Map the channels, the systems, and where data is actually breaking.
Surgical, minimal-touch changes with diagnostic breadcrumbs at every decision point — so nothing is a black box.
Import-as-new alongside the old. The previous version stays in place, disabled, as an instant rollback path.
Ongoing monitoring and a fast, evidence-first response when something upstream changes — because it always does.
A bench-credentialed clinical laboratory scientist who builds the interfaces — and now the AI — that regulated labs depend on.
Twenty-five years turning complex, regulated healthcare data into production systems: HL7 pipelines on Mirth Connect, Orchard Copia LIS environments, and in-house interpretive reporting that laboratories own outright — no recurring third-party fees, no vendor lock-in.
That same discipline now drives the AI work: multi-agent governance, agentic automation, RAG for legacy LIS environments, and self-hosted local-LLM clusters for on-premise inference. Deep regulated-domain fluency — CLIA, HL7/LOINC, clinical workflow — is what keeps automation grounded in clean data and defensible under real-world compliance.
A new interface, a pipeline that keeps failing, monitoring you wish you had, or AI you need to run without PHI ever leaving the building — start with a conversation.