Each of the 9 BMC boxes is mapped to its dominant Trinity Graph layer and grounded in Buckley Associates' real business context. Annotations show where Enginuity creates or captures value.
Enginuity note: The CFO is the high-centrality seed node. Win the CFO → earn access to every downstream data owner. The manufacturer relationships are the source of the pricing complexity Enginuity resolves.
The moat: Every day Enginuity runs behind Buckley's firewall, it accumulates ground truth no competitor can replicate. The knowledge graph compounds. This is not a SaaS dashboard — it's an institutional memory.
GTM insight: Buckley's CFO is the seed node. If the deployment proves ROI, the pattern replicates to every PE-backed manufacturer with the same "pretty dashboards, ugly data" problem.
Revenue logic: If Enginuity finds even one 2% margin leak on $100M revenue, that's $2M/year recovered. A $10K/mo license is a 16x ROI. The CFO will sign that check.
Role-based persona document for the CFO of a $100-150M hybrid HVAC distributor/manufacturer. Grounded in Buckley Associates' real business structure. No individual names — this is the archetype.
Owns the P&L across three fundamentally different business models operating under one roof. Responsible for 5-year strategic planning, board/owner reporting, and capital allocation. Inherited a tech stack that was never designed to give them the visibility they need. Has Tableau on their desktop and a growing suspicion that what it shows them isn't the full truth.
Distribution, manufacturing, and services each have completely different cost structures, margin profiles, and revenue timing. But financial reporting treats them as one blended entity. When overall margin drops, the CFO can't tell which pillar is bleeding — or which customer within that pillar.
CRITICALBuckley promises free next-day delivery across 5 warehouses in the Northeast. The logistics cost is real but buried — not allocated to the products or customers consuming it. High-volume, low-margin customers may be getting subsidized delivery that erases their profitability entirely.
HIGHThe 50,000 sq ft union sheet metal shop runs on timecard systems. Rounded timecards, overtime allocation, and job-level labor tracking are noisy and unreliable. The CFO can see total labor cost but can't trace variance to specific manufactured products or customer orders.
CRITICALTableau dashboards visualize what happened. They don't challenge whether the underlying data is accurate, don't model what will happen next quarter, and don't flag customers whose margin is trending negative. The CFO is navigating with a rearview mirror that may itself be distorted.
HIGH30+ manufacturer lines means 30+ pricing structures, discount tiers, rebate programs, and co-op marketing agreements. Tracking realized margin per manufacturer line across all 7 locations is a manual, error-prone process. Money is likely being left on the table — or misattributed.
HIGHThe 5-year plan depends on assumptions about customer profitability, margin trends, and growth capacity. But if the data feeding those assumptions hasn't been audited or cleansed, the plan is built on sand. The CFO knows this but doesn't have a systematic way to fix it.
CRITICALKnow the real margin on every customer, after allocating delivery cost, labor variance, manufacturer rebates, and service overhead. Not an average — a precise, auditable number for every account.
Get flagged when a customer's profitability is trending downward before it shows up in the quarterly review. Early enough to renegotiate pricing, adjust service levels, or make strategic decisions.
See distribution, manufacturing, and services as three distinct businesses with independent P&Ls, contribution margins, and growth rates — while also seeing the unified picture for strategic planning.
Know which numbers to trust and which are suspect. A systematic data quality layer that flags anomalies, quarantines questionable entries, and gives the CFO a "confidence score" on every report.
...so I can answer tough questions about where we're making money and where we're losing it, without hedging behind "we need to look into that."
...so I can negotiate from a position of knowledge, not gut feel. Including delivery cost, service calls, returns, and manufacturing labor on their custom orders.
...so I can model growth scenarios that are grounded in reality, not projections built on averages that mask structural problems.
...so I can make capital allocation decisions knowing whether our shop is actually profitable on a per-job basis, or just looks profitable in aggregate.
...so I can evaluate new vendor partnerships (like the recent Fujitsu VRF line) against real cost-to-serve data, not sales projections alone.
...so I can make decisions with confidence instead of wondering if the numbers are just artifacts of how data was entered, rounded, or aggregated.
Where Enginuity sits in Buckley's existing technology stack. The critical insight: Tableau is the visualization layer, not the data source. Enginuity is the intelligence and cleansing layer that sits between the raw source systems and any visualization tool.
⚠️ Tableau is the mirror, not the source. Without Layer 2, it visualizes whatever it's fed — including garbage.
Tableau is a visualization tool. Enginuity is a data intelligence layer. They're not competitors — Enginuity makes Tableau trustworthy.
| Capability | Tableau (Current) | Enginuity (New Layer) |
|---|---|---|
| Visualize historical data | ✓ Yes | — Passes to Tableau |
| Audit source data quality | ✗ No | ✓ Core function |
| Quarantine anomalous entries | ✗ No | ✓ Automated detection |
| Translate cross-system identifiers | ✗ No | ✓ Entity resolution |
| True customer-level profitability | ✗ No | ✓ All-in cost allocation |
| Multi-pillar margin reconciliation | ◐ Manual only | ✓ Automated across 3 pillars |
| Predictive margin risk alerts | ✗ No | ✓ Customer-level trajectory |
| Data confidence scoring | ✗ No | ✓ Every output scored |
| Behind-the-firewall knowledge graph | ✗ No | ✓ Compounds daily |
| Labor variance tracing (union shop) | ✗ No | ✓ Per-job, per-product |
| Delivery cost allocation | ✗ No | ✓ Per-customer, per-route |
| Interactive dashboards | ✓ Best-in-class | — Feeds Tableau or replaces it |
How Max walks into the CFO's office on Day 1 of the internship.
Distribution across 30+ manufacturer lines. A 50,000 sq ft union manufacturing shop. A field services team doing startup and commissioning. Each one has a different cost structure, different margin profile, and different customer economics. But your financial systems weren't built to tell you which one is making you money and which one is quietly eroding your margin.
You've got dashboards. They're well-built. But they visualize whatever data they're fed — and nobody is auditing that data. Rounded timecards in the shop. Delivery costs that aren't allocated to customers. SKUs that mean different things in different locations. The dashboards look clean. The data underneath might not be.
It ingests the messy source data from your existing systems — whatever they are. It cleanses it, quarantines anomalies, translates identifiers across departments, and reconciles margins across all three business pillars. Then it feeds verified, trustworthy data up to Tableau — or directly to you.
I'm here for the summer. Give me access to your source data, and I'll build the intelligence layer that tells you what's actually true. By August, you'll know the real margin on every customer, every product, every project — across all three pillars. And you'll never look at a dashboard the same way again.