Multi-agent architectures. Natural language analytics. Automated pipelines that run while you sleep. Production-grade. Deployed fast. Built by Rory Jenkins — AI Engineer & Full-Stack Developer.
Most companies are sitting on data they can't access fast enough. The tools that were supposed to help — Tableau, Looker, Power BI — became expensive subscriptions that still require a data analyst to pull a simple metric. I build the replacement: agentic AI systems where your team asks questions in plain English and gets answers in under a second, with full role-based access control baked in from day one.
Multi-agent architectures where specialized AI agents handle distinct business functions — each with its own tools, permissions, and context — while sharing a unified data infrastructure.
Natural language interfaces over your existing data. Ask "what were sales in the Northeast last quarter?" and get a structured answer in milliseconds — no SQL, no waiting, no data analyst required.
Identify the manual, repetitive workflows eating your team's time and replace them with reliable, observable automation pipelines that run continuously and alert you when something needs attention.
A growing enterprise was spending over $15,000 per month on Tableau, Looker, and Power BI licensing. Despite the investment, business teams were still waiting days for routine metric pulls. Every "quick question" required a ticket to the data team, a report run, and a meeting to review it. The tools that were supposed to empower the business had become a bottleneck.
I designed and deployed a multi-agent analytics layer on top of their existing data infrastructure. Six specialized AI agents — each scoped to a specific department with tailored tools and permissions — handle all natural language queries. An employee in the EMEA region asking about regional headcount gets the same sub-second response as a US-based finance analyst pulling quarterly margin data. No SQL. No ticket. No waiting.
Most AI projects fail because they start with the technology and work backward. I start with the business problem — the specific workflow, cost, or bottleneck you need to fix — and build the minimum architecture that solves it reliably.
We define the exact problem, identify your data sources, and map the outcome you need. At the end of this call, you'll have a clear project scope and a realistic timeline. No vague "AI strategy" — a concrete build plan.
I design the agent architecture — how many agents, what each one does, how data flows between them, and what the access control model looks like. You see a working prototype before we build the full system.
Production-grade build with real data. I test edge cases, error states, and performance under load. You get regular progress updates and a staging environment to review before anything goes live.
Live deployment with CI/CD patterns, monitoring hooks, and full documentation. Your team knows how to use it, maintain it, and extend it. I don't disappear after launch.
Traditional software is deterministic. It does exactly what you program it to do, in exactly the order you specify. That's useful for stable, well-defined processes. But most real business problems aren't stable or well-defined. Data formats change. Edge cases multiply. Business rules shift every quarter.
Agentic AI systems can reason, plan, and adapt. An agent doesn't need a hard-coded rule for every scenario — it can understand intent, decompose a problem into sub-tasks, call the right tools, and assemble a coherent answer. When the underlying data changes, the agent adapts without a code rewrite.
The difference is felt most clearly in analytics. A traditional dashboard shows you what you programmed it to show. An agentic analytics layer lets your CFO ask "what's driving the margin compression in EMEA this quarter?" in plain English — and get an answer that draws from three different data sources, cross-references against the prior quarter, and flags the two regions responsible.
// Agentic AI doesn't replace your data team. It multiplies what they can deliver — and gives everyone else in the business direct access to answers that previously required a specialist.
The practical case for replacing BI tools is simple math. Tableau Enterprise licensing starts around $70 per user per month. For a company with 500 users, that's $35,000 per month before you pay the analysts to build and maintain the dashboards. Power BI and Looker land in similar ranges at scale.
An agentic analytics system, built once on top of your existing data warehouse, costs a fraction of that to operate. The compute cost of running natural language queries through a well-architected multi-agent system is typically measured in cents per query, not dollars per user per month.
The deeper value is speed. When a business decision requires waiting three days for a report, people stop making data-informed decisions. They go with gut feel, they delay, or they make the wrong call. When the answer is available in under a second, the entire decision-making culture of the organization shifts.
This is what I build. Not demos. Not prototypes. Production systems that enterprise teams use every day to run their businesses.
While every build is custom, certain industries see disproportionate returns from agentic AI because their workflows involve high volumes of data questions, complex permission structures, or expensive off-the-shelf tool licensing.
Replace BI licensing costs. Enable CFOs and analysts to query financial data in natural language. Automate month-end reporting pipelines and exception alerts.
RBAC-compliant analytics over patient and operational data. Automate reporting workflows. Enable clinical managers to access operational metrics without data team bottlenecks.
Multi-region data unification with real-time natural language querying. Automated anomaly detection. Agent-driven exception handling for routing and fulfillment issues.
Product analytics agents for PMs and growth teams. Automated customer health scoring. Natural language access to usage data without requiring SQL skills.
Automate client reporting. Build internal knowledge agents that give consultants instant access to past project data, pricing history, and client context.
Inventory and sales analytics agents. Automated reorder and fulfillment workflows. Multi-channel data unification with natural language query access for buyers and planners.
I'll respond with a scoped plan and a fast path to deployment. No pitch deck. No retainer to "begin the engagement." A real plan, for your real problem.
Rory Jenkins is an AI engineer and full-stack developer specializing in production-grade agentic AI systems, multi-agent analytics platforms, and business automation pipelines. Operating through Jenxz Group LLC, Rory works with mid-market companies and enterprises that need to move fast on AI — building systems that deploy in weeks, not quarters.
Rory holds an advanced Generative AI & Agents Developer certification and has applied that expertise directly to enterprise-grade deployments. His flagship project — a multi-agent analytics platform serving 5,000+ users across 6 global regions — demonstrates what's possible when agentic AI architecture is applied to a real, high-stakes business problem: eliminating $180,000 per year in BI tool licensing while dramatically improving data access speed and quality.
Jenxz Group serves clients in finance, healthcare, logistics, SaaS, and professional services. Services include agentic AI system design and deployment, natural language analytics dashboards, business process automation, IT consulting, and team enablement workshops. Pricing starts at $750 for starter automation projects, with custom scoping for enterprise agent platforms.
To book a strategy call, discuss a project, or see a live demonstration of the multi-agent analytics platform, visit the contact page or use the Book Now link above.