Jordan Goodson — AI Integration & Automation Engineer
Turning complex challenges into scalable solutions. I utilize AI to build strategies and systems that drive business success. From automating workflows to creating intelligent and knowledgeable assistants, I specialize in translating complexity into powerful scalable solutions.
With over 20 years of hands-on experience, I bring deep knowledge to every project. My goal is to develop solutions so robust they eliminate the need for future interventions. I'm driven by the power of technology to transform businesses, and I thrive on bringing innovative ideas to impactful execution.
projects.log
Self-hosted autonomous AI platform: planner→helpers→synthesizer LLM loop, autonomous multi-task crew board, bench-driven model router, tiered memory, and an hourly self-auditing agent that files its own improvement tasks.
1,973 automated tests · 6-scanner self-audit loop · Prompt-injection screening at every LLM boundary
End-to-end editorial automation: scraper agent surfaces trending topics, analysis agent synthesizes angles, generation agent drafts, a simulated audience panel scores drafts, and the top 30% publish with AI-selected imagery — minimal human intervention.
Replaced a fully manual editorial workflow · Audience-panel scoring selects top 30% of drafts · Runs end-to-end: research → draft → review → publish
AI-assisted support triage using a local LLM: reviews incoming tickets against historical data, surfaces relevant solution tickets and documentation in-ticket, and reclassifies type, board, and priority before assignment.
Solution candidates surfaced in-ticket, automatically · Auto-reclassification before an engineer ever opens it · Extending to skills-based auto-routing
Claude + Microsoft Fabric pipeline that turns raw ticket metrics from Power BI into client-ready narrative QBR reports with charts — distributed automatically by email and Teams, with on-demand generation via a Copilot agent.
Quarterly reports generated and distributed hands-free · On-demand ad hoc QBRs from a Copilot agent · Raw metrics → client-ready narrative, automatically
certs.verified
architecture.trace
How I architect multi-agent pipelines — from raw data to actionable output. This reflects the real architecture behind Obassi/Hiveforge.
Tickets, docs, APIs, web feeds
Scrapes, cleans, chunks incoming data
Embeds chunks, retrieves by semantic similarity
Decomposes task, routes to specialist agents
Aggregates results, generates final response
Open to consulting engagements, AI integration projects, and conversations about what automation can do for your business.
Get in touch →