Research & Technical
Direction
Gem Coral Systems documents the infrastructure patterns, automation systems, applied AI workflows, and business intelligence tools we develop — practical notes from running private AI systems in production, at a scale most teams can actually afford.
What we are building and studying
Four programs spanning local AI deployment, business intelligence, automation, and data sovereignty.
Local AI Infrastructure for Small Business
Most AI tooling assumes cloud deployment. We are building and operating AI workflows on local hardware — GPU servers, small offices, edge deployments — and documenting what actually works at this scale.
Gem Coral runs production LLM inference on its own GPU hardware using Ollama. Patterns we are documenting cover model routing, queue management, failure handling, and cost comparison against cloud APIs. The JahKnow API gateway is the public infrastructure layer for this work.
Entity Intelligence & Market Signals
Authoritative records, company filings, and web-presence signals contain real market intelligence — but only after normalization, entity resolution, enrichment, and graph construction. We build the Gem Coral intelligence engine — the pipeline layer that transforms fragmented business information into searchable operational insight.
Yard Registry is one applied example: a business intelligence system combining authoritative company filings, director relationship graphs, web enrichment, and AI lead scoring. It demonstrates how the same pipeline applies wherever organizations need to extract signal from fragmented business information at scale.
Automation Pipelines for Service Businesses
Service businesses run on disconnected tools. We design and deploy the connective layer that turns websites, forms, AI assistants, CRM entries, and follow-up reminders into one operational system.
Deployed across multiple clients through WebSoQuick and BizBot. The automation layer covers inbound capture, AI-assisted response, CRM update, follow-up scheduling, and per-tenant reporting — each client isolated and independently configurable.
Private AI & Controlled Infrastructure
Exploring AI systems where businesses keep greater control over data, workflows, deployment environment, and operational cost — without depending on public cloud APIs as the only operational path.
Covering model selection, data routing, access control, and audit trail design. Architecture planning is underway. Field notes will follow the first client deployment.
This research ships as working systems.
Everything documented here runs in production somewhere. If one of these problems looks like yours, let's talk about applying it to your organization.