Elevrics is a boutique AI & BI strategy consultancy. We design and build governed analytics and production AI for small and mid-market organizations — measured in ROI, and built with the security discipline of the compliance-sensitive environments where we learned the craft. We've been doing this work, in different uniforms, for 25 years.
The name Elevrics comes from elevated analytics. The practice rests on one discipline — turning fragmented, sensitive information into decision-grade systems, with security and governance built into the architecture rather than appended as policy — applied across three pillars: agentic AI integration, governed analytics, and data modernization.
In practice that means production MCP architecture connecting AI to your real systems, OAuth-governed access, row-level security, and HIPAA-aware data handling; consolidated single-source-of-truth reporting with metric definitions leadership can run the business on; and platform modernization — including insourcing strategies that turn external vendor spend into infrastructure you own — for mid-market law firms, owner-led SMBs and professional services, and organizations with serious BI investments (Domo, Snowflake, Power BI/Fabric).
Engagements follow a simple ladder — an Enterprise AI Readiness Audit, then a scoped build (a Secure Integration Sprint, BI Modernization & Platform Consolidation, or an Executive Dashboard & KPI Program), and ongoing Fractional Data & AI Leadership.
Elevrics founder Jared Katz has done one thing in very different uniforms: build systems that turn fragmented, sensitive information into decisions people can trust — in environments where being wrong is expensive.
Early in his career, that meant quantitative open-source intelligence work in national security — synthesizing foreign media at scale into 300+ finished intelligence reports for policymakers, under real security constraints. That experience left two convictions that shape everything Elevrics builds: sensitive data demands architecture-level discipline, not policy documents; and analysis is only valuable when a decision-maker can trust it completely.
For the past decade-plus, he has applied that discipline to enterprise data: six years as a Principal Consultant at Domo architecting data consolidation for Fortune 500 clients — single sources of truth for executives betting real money on the numbers — followed by senior BI leadership in-house, and today, Director of Business Intelligence at a ~200-person national law firm. That current work is the clearest picture of what Elevrics does: one of the legal sector's first production agentic AI layers — MCP servers, OAuth 2.0, row-level security, HIPAA-aware pipelines — that lets attorneys use AI on sensitive case files without leaking a byte. The same governed-data discipline surfaced $465K+ in annual cost savings, realized and projected, in the past year alone.
Recent graduate work in weather and climate risk analytics (University of Illinois) extends the same skill to physical and operational exposure — quantified inside the dashboards executives already use, turning "resilience" from an annual PDF into a daily metric. Education: MBA, University of Utah; BA in Russian & Communications, Brigham Young University; Graduate Certificate in Climate Risk & Data Analytics, University of Illinois Urbana-Champaign (2026); Certified Major Domo.
These aren't aspirational statements — they're how the work is scoped, built, and priced.
Access control, data lineage, and compliance handling are designed into the system from the first diagram. A policy document can't stop a leak; an OAuth flow with row-level security can.
You're not buying MCP servers — you're buying "our attorneys can use AI on case files without a breach" and "our executives trust the numbers." The tools are how; they're never the headline.
Every pipeline ships ready for scrutiny: documented lineage, auditable access, containerized CI/CD deployment. If your carrier, client, or regulator asks how AI touches the data, the answer is a diagram.
The ladder — audit, scoped build, retainer — has published scopes and prices across all five offers. "Can you also just…" requests get scoped honestly, not absorbed. That protects your budget as much as our quality.
Demos and pilots that never survive contact with compliance aren't results. We build for daily production on real, sensitive data — because that's where we've done it before.
Everything is documented, deployed in your accounts, and transferable. The fractional retainer exists because senior oversight is valuable — not because anything we build is a black box.
Structured, collaborative, and honest about constraints — yours and ours.
Before anything is built, we map your data landscape, your security requirements, and your obligations. Nothing touches sensitive data on assumptions — and you get a fixed-fee deliverable you can defend to your board, partners, or carrier.
The people who know your workflows — attorneys, analysts, operators — are essential to getting the design right and owning the outcome. We share work in progress and build in validation at every stage.
AI models have failure modes. Data quality matters. Projections carry uncertainty. We're upfront about all of it and design the review loops and controls that make those realities safe — rather than pretending they don't exist.
A pipeline nobody maintains is a liability, not an asset. Everything ships documented and transferable — and for organizations that want senior ownership to continue, that's exactly what the fractional retainer is for.
That's exactly what we build. Start with a 30-minute conversation about your data landscape, your goals, and any obligations the data carries.
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