Elevrics turns fragmented data into governed analytics and production AI — agentic AI integration, BI modernization, and decision-grade reporting, all built with the security discipline of compliance-sensitive environments. Every engagement follows the same ladder: a fixed-fee Enterprise AI Readiness Audit, a scoped build (a Secure Integration Sprint, BI Modernization & Platform Consolidation, or an Executive Dashboard & KPI Program), and ongoing Fractional Data & AI Leadership. Each rung is the natural exit of the previous one.
Schedule an Architecture AuditThe question it answers: "Can we do this safely, and what would it take?"
Before anyone builds anything, you need an honest map. The Audit is a structured, fixed-fee diagnostic of your data infrastructure, your security and compliance requirements, and the realistic path to putting AI to work on your sensitive data. It's designed for the buyer who has to defend the decision — to a managing partner, a compliance officer, a board, or an operating partner.
Every system that holds decision-relevant data — case management, CRM, warehouse, telephony, documents — mapped with its integration surface, data quality, and governance state.
Your actual obligations — HIPAA/BAA, attorney-client privilege, OAuth and access-tier design, row-level security — translated into concrete architecture requirements, not policy language.
A defensible, sequenced plan for AI integration: what to build first, what controls each phase requires, and what it will cost. The audit deliverable ends with a scoped sprint proposal.
The question it answers: "Build it."
A scoped build, deployed to production — not a proof of concept that dies in a slide deck. The sprint delivers the secure middleware between your data and AI: custom MCP servers, consolidated and governed data, and automated workflows that operate inside auditable controls from day one.
Production Model Context Protocol servers connecting LLMs to your proprietary systems — containerized, CI/CD-deployed, and governed by OAuth 2.0 flows and per-user tier routing.
Fragmented sources unified into a governed single source of truth — the Fortune 500 consolidation pattern, applied at mid-market scale, so AI acts on numbers everyone trusts.
AI acting on enterprise data within auditable controls — document parsing, medical chronologies, intake triage, reporting automation — with HIPAA/BAA-aware handling where the data demands it.
Architecture diagrams, data lineage, and access-control documentation. When your carrier, client, or regulator asks how the AI touches the data, you have a diagram, not a shrug.
Asked for "just a quick integration"? That's the sprint — and the audit is how we scope it. Fixed scopes are how we keep quality high and timelines honest.
The question it answers: "Why do we have five versions of every number — and why does it cost so much?"
Most organizations don't have a data problem — they have a fragmentation problem. This build unifies scattered and legacy source systems into a governed single source of truth, with enforced metric definitions and validation standards. Where the economics support it, it goes further: a phased insourcing strategy that replaces external data-vendor spend with infrastructure you own.
Disparate systems — case management, CRM, ad platforms, telephony, accounting, HR — unified into one governed platform that becomes the organization's source of record. The Fortune 500 consolidation pattern, applied at mid-market scale.
A phased strategy that transitions external data-vendor spend to an internally owned warehouse — sequenced so reporting never breaks, with governance carried over from day one. Our flagship version of this replaced ~$500K/year in vendor costs with an ~$80K/year owned AWS platform.
Legacy reporting moved to a modern platform without losing the institutional logic buried in it. Native Domo architecture depth (Certified Major Domo), plus Power BI/Fabric modernization advisory for Microsoft-stack organizations.
The economics matter: consolidation isn't just governance — it's a line item. Recent modernization work surfaced $465K+ in annual savings (realized and projected) in a single year, without added headcount.
The question it answers: "Can leadership actually run the business on these numbers?"
Decision-grade reporting for owners and executives: metric definitions everyone agrees on, dashboards that answer the questions leadership actually asks, forecasts built on leading indicators, and independent auditing of the numbers your vendors hand you. Smaller than a full modernization — and often the fastest way to find out what your data has been trying to tell you.
A defined, documented metric layer — what each number means, where it comes from, and who owns it — so every dashboard reads from the same source of truth instead of five competing spreadsheets.
Forward-looking views built from your own operational history — demand surges, capacity gaps, seasonal rhythms — visible early enough to plan for rather than react to.
Independent verification of the analyses your agencies and vendors deliver. Our flagship audit of a $20M+ TV program found timing errors that understated growth by ~17 points — the reported 3% was actually 23%.
Recurring board and leadership reports generated as complete, validated drafts — visualizations updated automatically, narrative commentary included, and a human approving every word before it ships.
The question it answers: "Who owns this going forward?"
Data strategy isn't a one-time build. Models change, prompts drift, vendors make claims, access needs evolve, and governance only works if someone enforces it. The fractional retainer gives owners and executives a senior data and AI leader — strategy, vendor accountability, and measurable ROI — without a full-time hire.
Ongoing operation and hardening of the deployed infrastructure — deployments, monitoring, environment and secret management, and incident response.
Access tiers, row-level security, and data-handling standards enforced as the organization changes — new hires, new matters, new systems.
The AI layer kept current as models and capabilities evolve — evaluated, tested, and rolled out without breaking the controls around them.
A standing senior voice for leadership — what to adopt, what to decline, which vendor claims to audit, and where the next dollar of data spend actually returns. Measurable ROI is the standing agenda item.
Small and mid-market organizations where reporting drives real decisions, and where the data often carries real obligations.
Personal injury and plaintiff firms with medical-records workflows, and any firm whose attorneys want AI on case files. The controls your compliance obligations require — HIPAA/BAA handling, privilege-aware access, row-level security — are the exact pattern we run in production today.
Organizations with a serious BI or warehouse investment and no secure bridge between that data and AI. We speak the platforms natively — a decade of Domo architecture at Fortune 500 scale, plus Power BI/Fabric modernization advisory — and build the governed agent layer on top.
Growing businesses running on fragmented systems and gut feel, without — or not yet needing — a full-time data hire. Fractional data and AI leadership brings governed reporting, vendor accountability, and cost savings that typically pay for the engagement.
Because when the data carries real obligations, what you build second matters less than what you get wrong first. The audit surfaces the security requirements, data-quality gaps, and access-control design before any AI touches sensitive data — and it ends with a scoped build proposal, so nothing is built on assumptions.
As architecture, not policy documents. BAA-aware data handling, OAuth 2.0 authentication, per-user tier routing, row-level security, and audit logging are designed in from the first diagram. We've run this pattern in production for HIPAA-sensitive workflows at a national law firm.
Only what the governance layer allows, per user, per role. That's the point of the middleware: the AI never bypasses the access controls your organization already depends on — it inherits them.
Yes. Everything is documented, deployed in your infrastructure and accounts, and transferable. The fractional retainer exists because oversight is valuable — not because we build black boxes.
Yes — that's the Executive Dashboard & KPI Program, a first-class offer with its own fixed scope ($8k–$20k). Smaller requests still get scoped honestly, usually in the first call, so you're never buying an open-ended engagement. Fixed scopes protect your budget as much as our calendar.
Yes. The security discipline comes standard because it's how we build, but most of what clients buy is simpler: numbers leadership can trust, reporting that replaces gut feel, vendor claims independently verified, and data costs that go down instead of up. Compliance-sensitive teams need us; well-run businesses just benefit.
A 30-minute conversation, then a fixed-fee Enterprise AI Readiness Audit. You'll know exactly what it takes to put AI safely to work on your data — and exactly what it costs to find out.
Schedule an Architecture Audit