Every engagement below is real. Names and identifying details are generalized, but the architectures, the numbers, and the outcomes are exactly as they happened — $465K+ in annual savings surfaced in the past year alone, alongside AI infrastructure running daily on some of the most sensitive data there is.
How to read these: Each case follows the same structure — situation, challenge, approach, outcome. Together they show one skill applied in different settings: turning fragmented, sensitive information into decision-grade systems, with governance built in rather than bolted on.
A ~200-person national law firm wanted AI working on medical chronologies and case documents — data covered by HIPAA, BAA obligations, and attorney-client privilege. Attorneys could see the leverage; compliance could see the exposure. Consumer AI tools were a nonstarter, and "just be careful" is not an architecture.
The firm needed AI that could read and act on real case data without creating a single ungoverned path between sensitive documents and a language model. Every connection had to be attributable to a user, constrained by role, and defensible to a malpractice carrier, a client, or a regulator asking exactly how the AI touches the data.
We designed and built the firm's production AI infrastructure — one of the legal sector's first production agentic AI layers: custom Model Context Protocol (MCP) servers, containerized and deployed through CI/CD, connecting Claude to the firm's case management (Filevine), BI, CRM, and telephony systems.
Every connection is governed end-to-end: OAuth 2.0 authentication, per-user tier routing across 90+ users and six access tiers, and row-level security so each person's AI sees only what that person is entitled to see. HIPAA-sensitive workflows — including AI-assisted medical chronologies — run with BAA-compliant handling and structured, reviewable output.
The same governed layer powers AI-augmented operations beyond documents: transcript search across 65,000+ archived calls, governed outbound AI calling, and scheduled executive AI briefings — all inside the same auditable controls.
An organization investing more than $20M in television advertising received a performance analysis from its external marketing vendor showing roughly 3% year-over-year growth. Leadership sensed a disconnect — the business felt like it was growing faster than the numbers said — but had no independent way to check.
Major strategy decisions, including where to place the next media dollar, were riding on the vendor's numbers. If the reported near-flat growth was real, it argued for cutting or restructuring spend. If it was wrong, acting on it could damage a campaign that was actually working. Nobody was auditing the auditor.
We independently rebuilt the vendor's analysis from the raw data — more than 100,000 tracked calls across the full media program — and found timing-normalization errors: the analysis compared unequal periods in a way that systematically understated recent performance by roughly 17 percentage points.
With the analysis corrected, real growth was approximately 23%, not the reported 3%. The same audit surfaced an actionable optimization: a $500K reallocation away from underperforming months, projected to yield 2,000–3,000 additional TV-driven calls annually at zero added cost.
An organization was spending roughly $500K per year on external data vendors — pipelines, warehousing, and reporting infrastructure it depended on daily but didn't own. Costs compounded annually, every change request went through someone else's queue, and the institutional logic of the business lived in systems the business couldn't see into.
A rip-and-replace migration would put daily reporting — and the decisions built on it — at risk. The organization needed a path that captured the economics of ownership without a single day of broken dashboards, and without betting the transition on a big-bang cutover.
We architected a phased "blend to build" insourcing strategy: an internally owned AWS data warehouse designed to run at roughly $80K per year steady state, stood up alongside the vendor systems rather than in place of them.
Pipelines migrate in sequenced phases — each one validated against the vendor's output before cutover — so reporting never breaks and trust in the numbers carries through the transition. Governance, access controls, and metric definitions transfer with the data, and every pipeline lands in infrastructure and accounts the organization owns.
A professional services organization was producing a monthly performance report for board review by manually pulling and reconciling sales, accounting, and operational data across multiple systems. The process consumed one to two days of staff time every month — time spent on assembly, not analysis.
The report existed to drive strategic conversation, but the effort required to produce it left little room for the deeper thinking that made it valuable. Metrics were accurate but static. Cross-system patterns and non-obvious drivers went largely unexplored simply because there wasn't time. The human expertise in the room was being spent on preparation rather than insight.
We designed and built an automated reporting pipeline anchored around a defined set of metrics drawn from the organization's sales, accounting, and operational data sources. The system pulls from those sources on a recurring basis, updates all visualizations automatically, and generates narrative commentary that tracks and contextualizes metric trends — producing a structured draft aligned to the flow of the board discussion.
Beyond the standard metrics layer, we built a cross-analysis process that goes deeper into the source systems to surface non-obvious drivers, emerging concerns, and areas worth investigating — connections that wouldn't appear in any single system viewed in isolation.
Critically, the process keeps a human in the loop. The preparer receives a complete draft, reviews and validates the results, probes further where the data warrants it, and approves the final report before it goes to the board. The pipeline augments judgment — it doesn't replace it.
A large organization with approximately 25 inbound customer service and sales representatives was struggling to keep pace with inquiry volume despite significant staffing. Live answering was a core brand value — customers expected and were promised a real person. Missed calls were running at 8%, and the team was falling short of that standard in ways that were costing both revenue and customer trust.
The issue wasn't headcount — it was alignment. Capacity existed within the team, but it wasn't deployed where and when demand was actually arriving. Downtime was clustered in the wrong places while peak inquiry windows went understaffed. Response consistency was also uneven, with no documented criteria guiding how representatives triaged and prioritized different inquiry types.
We conducted a deep analysis of historical call data and inquiry patterns — examining volume by time of day, day of week, and inquiry type — to map precisely where demand peaked and where staff capacity sat idle. That analysis revealed specific, actionable windows where missed calls were concentrated, and corresponding periods of excess capacity that could be redistributed.
From that data we developed targeted scheduling recommendations: adjusted shift start times, restructured lunch break rotations, and realigned coverage on high-volume days. No additional headcount was needed.
Alongside the scheduling work, we designed an AI-assisted triage and routing tool built around the organization's actual decision rules — criteria that had previously existed informally across staff. Workflow mapping sessions with key personnel surfaced and formalized those rules into consistent, documented process. The tool was piloted with a small group before broader rollout, with a written governance policy covering appropriate use.
Standard flood risk assessment relies heavily on FEMA flood zone designations and historical insurance claim data. But those inputs share a structural blind spot: properties outside mapped flood zones are systematically underinsured, which means when they flood, claims are rarely filed. The absence of claims gets misread as the absence of risk.
Fifty years of claim data showed certain areas as low-risk. No existing maps flagged them. But that record reflected insurance behavior — not what actually happened on the ground. The real signal was hiding in a different dataset entirely.
Rather than relying on insurance claims — which are only filed where insurance exists — we turned to FEMA individual assistance applications filed after storm events. Emergency aid requests don't require a flood insurance policy. They get filed by anyone who experienced damage, regardless of whether they were in a mapped flood zone or carried coverage.
That data told a fundamentally different story. Areas that had never generated meaningful insurance claims had in fact experienced flood damage — documented through aid applications that had simply never been connected to risk modeling. Cross-referencing aid application patterns against flood zone boundaries revealed a meaningful gap: communities with real, recurring flood exposure that neither the maps nor the claim data had ever surfaced.
It's the same discipline behind our intelligence-grade approach to data: the most important signal is often in the dataset nobody thought to connect. The methodology extends forward — flagging similar at-risk areas before damage occurs, and feeding exposure into the operational dashboards leadership already uses, rather than an annual PDF.
A mid-size business produced complex, custom products with production timelines that varied significantly depending on project complexity. Layered on top of that variability was meaningful seasonality in sales — meaning demand signals were neither consistent nor evenly distributed across the year. Different departments experienced peaks and valleys at different times, but the connection between those patterns was not well understood or anticipated.
Because production timelines were long and variable, by the time a department felt the pressure of an incoming workload surge, it was often too late to staff for it effectively. Hiring and reallocation decisions were being made reactively rather than planned in advance. The data to anticipate these patterns existed — it simply hadn't been connected and analyzed in a way that made the future workload visible early enough to act on it.
We analyzed historical project data to establish average production timelines across similar project types, creating a baseline for how long work typically moved through each stage of the process. By examining activity and workflow metrics earlier in the production pipeline — before downstream departments were engaged — we built leading indicators that predicted when specific groups would experience demand spikes weeks or months in advance.
Seasonal sales patterns were layered into the model to account for the predictable rhythms that influenced when new projects entered the pipeline in the first place. Together, these inputs produced a forward-looking view of departmental capacity demand across the full production cycle.
From that analysis we developed staffing recommendations that gave leadership two distinct tools: advance notice of when specific departments would need augmented capacity, and a structured approach to reallocating existing staff from lower-demand areas to support higher-demand ones.
A mid-size company serving automobile consumers noticed a meaningful decline in sales inquiries in the second quarter compared to the first. Leadership knew something had shifted but didn't have a clear explanation for why — or a confident sense of how to respond.
Without understanding the drivers behind the decline, any response carried significant risk. Cutting marketing spend might accelerate the damage. Investing heavily into a structural problem rather than a temporary one could be equally costly. What the business needed before acting was an honest, data-grounded account of what was actually happening and why.
We began by examining whether a seasonal pattern existed in the business's historical inquiry data. It did — interest in their product category softened predictably in spring and summer, meaning some portion of the decline was a normal, recurring rhythm rather than a signal of something newly wrong.
But seasonality alone didn't fully account for the magnitude of the drop. We looked deeper at the demographic profile of their typical customer and found that a significant share were lower-income consumers — a segment with meaningful sensitivity to gas prices. Gas prices had spiked recently, and the effect on this customer base was compounding: they were driving less, reducing their immediate need for the company's services, and had less discretionary income available at the same time.
Using statistical weighting, we quantified the relative contribution of each factor — seasonality and gas price sensitivity — to the overall decline in inquiries. Research consistently shows that companies which maintain brand investment while competitors pull back capture a disproportionate share of attention during downturns. We recommended the company hold its advertising commitment rather than scale back.
A ~5,000-resident Florida residential community faced a forced modernization: the legacy vehicle-transponder access system was going obsolete, security cameras were aging, and security data lived in systems nobody could report from. Decisions fell to a volunteer board fielding competing vendor claims, with every choice directly affecting thousands of residents' daily access and safety.
The community needed several interlocking systems evaluated and replaced at once — access control, cameras, security data — without disrupting daily entry for thousands of vehicles, and without the in-house technical depth to independently pressure-test what vendors were promising.
Elevrics led the community's infrastructure and security data advisory: managing the migration of roughly 2,000 vehicles to a modern transponder access system, modernizing ~20 security cameras, and onboarding a new security data platform so incidents and access events became reportable rather than anecdotal.
The same advisory seat evaluated access-control and age-verification platforms against the community's actual requirements — not vendor feature lists — and directed research into lightning detection and equipment protection for outdoor facilities.
Not every result needs its own case study. The same governed-data habits routinely surface savings and fix problems in passing:
Every engagement starts with the fixed-fee Enterprise AI Readiness Audit — so the first thing you buy is an honest map, not a leap of faith.
Schedule an Architecture Audit